(H: Hypothesis, P: Proposition, S: Testable Statement, RQ: Research Question)
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Analysis of Axiomatic Theory Elements (ATE) |
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Paper Hypotheses |
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ATE Type |
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Agarwal, R., and Karahanna, E. 2000. “Time Flies When You’re Having Fun: Cognitive Absorption and Beliefs About Information Technology Usage,” MIS Quarterly (24:4), pp. 665-694. |
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H1: Perceived usefulness of an information technology has a positive effect on behavioral intention to use the information technology. |
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H2: Perceived ease of use of an information technology has a positive effect on behavioral intention to use the information technology. |
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H3: Perceived ease of use of an information technology has a positive effect on the perceived usefulness of the information technology. |
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H4: After controlling for self-efficacy perceptions, cognitive absorption with an information technology has a positive effect on the perceived ease of use of the information technology. |
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H5: After controlling for self-efficacy perceptions, cognitive absorption with an information technology has a positive effect on the perceived usefulness of the information technology. |
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H6: Computer playfulness has a positive effect on cognitive absorption with an information technology. |
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H7: Personal innovativeness has a positive effect on cognitive absorption with an information technology. |
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Ahuja, M. K., and Thatcher, J. B. 2005. “Moving Beyond Intentions and Toward the Theory of Trying: Effects of Work Environment and Gender on Post-Adoption Information Technology Use,” MIS Quarterly (29:3), pp. 427-459. |
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H1a: Quantitative overload negatively influences trying to innovate with IT. |
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H1b: Qualitative overload negatively influences trying to innovate with IT. |
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H2: Perceived autonomy positively influences trying to innovate with IT. |
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H3: The relationship between autonomy and the trying to innovate with IT will be stronger in men than in women. |
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H4: The relationship between overload and trying to innovate with IT will be stronger in women than in men. |
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H5a: Autonomy interacts with quantitative overload to positively influence individual's trying to innovate with IT. |
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H5b: The above interaction effect will be stronger in women than in men. |
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H6a: Autonomy interacts with qualitative over load to positively influence individual's trying to innovate with IT. |
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H6b: The above interaction effect will be stronger in women than in men. |
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Ang, S., and Straub, D. 1998. “Production and Transaction Economies and IS Outsourcing: A Study of the U.S. Banking Industry,” MIS Quarterly (22:4), pp. 535-552. |
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H1: The higher the comparative production cost advantage offered through IT outsourcing, the greater is the degree of IT outsourcing. |
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H2: The less the transaction costs involved in hiring outsourcers, the greater is the degree of IT outsourcing. |
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H3: The less the financial slack, the greater the degree of IT outsourcing. |
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NOTE: Firm (bank) size is also tested, but not hypothesized. |
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Arnold, V., Clark, N., Collier, P. A., Leech, S. A., & Sutton, S. G. 2006. “The Differential Use and Effect of Knowledge-Based System Explanations in Novice and Expert Judgment Systems,” MIS Quarterly, (30:1), pp. 79-97. |
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H1: KBS users will be more likely to adhere to the recommendation of a KBS when explanations are provided. |
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H2: When using a KBS with explanation facilities, novices will choose more feedforward explanations than experts. |
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H3: When using a KBS with explanation facilities, experts will choose more feedback explanations than novices. |
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H4: When using a KBS with explanation facilities, novices will choose more declarative knowledge explanations than experts. |
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H5: When using a KBS with explanation facilities, novices will choose more initial problem-solving strategy-based explanations than experts. |
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H6: When using a KBS with explanation facilities, experts will choose more procedural knowledge explanations than novices. |
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H7: Experts that use feedback explanations when using a KBS with explanation facilities will be more likely to adhere to the recommendation. |
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H8: Experts that choose more feedback explanations when using a KBS with explanation facilities will be more likely to adhere to the recommendation. |
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Bassellier, G., Benbasat, I., and Reich, B. H. 2003. “The Influence of Business Managers’ IT Competence on Championing IT,” Information Systems Research (14:4), September 2003, pp. 317-336. |
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H1: IT knowledge in business people positively influences their intentions to champion IT in their organizations. |
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H2: IT experience in business people positively influences their intentions to champion IT in their organizations. |
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Bharadwaj, A. S. 2000. “A Resource-based Perspective on Information Technology Capability and Firm Performance: an Empirical Investigation,” MIS Quarterly (24:1), pp. 169-196. |
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H1: Superior IT capability will be associated with significantly higher profit ratios. |
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H2: Superior IT capability will be associated with significantly lower cost ratios. |
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Bhattacherjee, A. 2001. “Understanding Information Systems Continuance: An Expectation-Confirmation Model,” MIS Quarterly (25:3), pp. 351-370. |
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H1: Users’ level of satisfaction with initial IS use is positively associated with their IS continuance intention. |
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H2: Users’ extent of confirmation is positively associated with their satisfaction with IS use. |
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H3: Users’ perceived usefulness of IS use is positively associated with their satisfaction with IS use. |
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H4: Users’ IS continuance intention is positively associated with their perceived usefulness of IS use. |
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H5: Users’ extent of confirmation is positively associated with their perceived usefulness of IS use. |
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Bhattacherjee, A., and Premkumar, G. 2004. “Understanding Changes in Belief and Attitude Toward Information Technology Usage: A Theoretical Model and Longitudinal Test,” MIS Quarterly (28:2), pp. 229-254. |
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RQ1: Do IT users’ beliefs and attitude toward IT usage change over time as they experience IT usage first-hand? |
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NOTE (1): We adopted the first research question (RQ) out of three RQs in the paper since it represents the research model (Figure 1) of the paper sufficiently. NOTE (2): The authors did not present their research hypotheses explicitly. For your reference, the following hypotheses are derived from Figure 1 of their paper. H1: Beliefs (Forwards-looking) -----(+)----> Beliefs (Modified) H2: Beliefs (Forwards-looking) -----(-)-----> Disconfirmation H3: Beliefs (Forwards-looking) -----(+)-----> Satisfaction H4: Beliefs (Forwards-looking) -----(+)-----> Attitude (Initial) H5: Disconfirmation -----(+)-----> Beliefs (Modified) H6: Satisfaction -----(+)-----> Attitude (Modified) H7: Attitude (Initial) -----(+)-----> Attitude (Modified) H8: Beliefs (Modified) -----(+)-----> Attitude (Modified) H9: Beliefs (Modified) -----(+)-----> Intention (Modified) H10: Attitude (Modified) -----(+)-----> Intention (Modified) |
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Bock, G. W., Zmud, R. W., Kim, Y. G., and Lee, J. N. 2005. “Behavioral Intention Formation in Knowledge Sharing: Examining the Roles of Extrinsic Motivators, Social-Psychological Forces, and Organizational Climate,” MIS Quarterly (29:1), pp. 87-111. |
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H1: The more favorable the attitude toward knowledge sharing is, the greater the intention to share knowledge will be. |
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H2: The greater the anticipated extrinsic rewards are, the more favorable the attitude toward knowledge sharing will be. |
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H3: The greater the anticipated reciprocal relationships are, the more favorable the attitude toward knowledge sharing will be. |
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H4: The greater the sense of self-worth through knowledge sharing behavior is, the more favorable the attitude toward knowledge sharing will be. |
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H5: The greater the sense of self-worth through knowledge sharing behavior is, the greater the subjective norm to share knowledge will be. |
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H6: The greater the subjective norm to share knowledge is, the greater the intention to share knowledge will be. |
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H7: The greater the subjective norm to share knowledge is, the more favorable the attitude toward knowledge sharing will be. |
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H8: The greater the extent to which the organizational climate is perceived to be characterized by fairness, innovativeness and affiliation, the greater the subjective norm to share knowledge will be. |
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H9: The greater the extent to which the organizational climate is perceived to be characterized by fairness, innovativeness and affiliation, the greater the intention to share knowledge will be. |
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Brown, S. A., and Venkatesh, V. 2005. “Model of Adoption of Technology in Households: A Baseline Model Test and Extension Incorporating Household Life Cycle,” MIS Quarterly (29:3), pp. 399-425. |
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H1: Marital status and age will moderate the relationship between applications for personal use and intention to adopt a PC for household use, such that applications for personal use increases in importance as age increases, particularly for those households in which there are married couples. |
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H2: Child’s age will moderate the relationship between utility for children and intention to adopt a PC for household use, such that utility for children increases in importance as the child’s age increases. |
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H3: Age will moderate the relationship between utility for work-related use and intention to adopt a PC for household use, such that utility for work-related use increases in importance until retirement age, at which point utility for work-related use is no longer relevant. |
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H4: Age will moderate the relationship between applications for fun and intention to adopt a PC for household use, such that applications for fun decreases in importance as age increases. |
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H5: Age will moderate the relationship between status gains and intention to adopt a PC for household use, such that status gains decreases in importance as age increases. |
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H6: Age, marital status, and income will moderate the relationship between the normative beliefs (i.e., friends and family, secondary sources, and workplace referents) and intention to adopt a PC for household use, such that normative beliefs increases in importance with increasing age and decreasing income, particularly for those who are married. |
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H7: Age and income will moderate the relationship between the external control beliefs (i.e., fear of technological advances, declining cost, and cost) and intention to adopt a PC for household use, such that external control beliefs increases in importance as age increases and income decreases. |
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H8: Age will moderate the relationship between the internal control beliefs (i.e., perceived ease of use and requisite knowledge) and intention to adopt a PC for household use, such that internal control beliefs increase in importance as age increases. |
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Chau, P. Y. K., and Tam, K. Y. 1997. “Factors Affecting the Adoption of Open Systems: An Exploratory Study,” MIS Quarterly (21:1), pp. 1-24. |
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H1: Higher levels of market uncertainty will positively affect the likelihood of open systems adoption. |
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H2: Higher levels of perceived benefits of adopting an open system will positively affect the likelihood of open systems adoption. |
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H3: Higher levels of perceived barriers to adopting an open system will negatively affect the likelihood of open systems adoption. |
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H4: Higher levels of perceived importance of standard compliance, interoperability and interconnectivity to an organization will positively affect the likelihood of open systems adoption. |
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H5: Higher degrees of complexity of IT infrastructure will positively affect the likelihood of open systems adoption. |
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H6: Higher levels of satisfaction with existing systems will negatively affect the likelihood of open systems adoption. |
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H7: Higher degrees of formalization on systems development and management will positively affect the likelihood of open systems adoption. |
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Chidambaram, L. 1996. “Relational Development in Computer-supported Groups,” MIS Quarterly (20:2), pp. 143-165 |
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S1: Computer-supported teams need longer time to develop close relations compared to face-to-face teams. (p. 144) |
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S2: The model this study proposes to test is predicated on SIPs arguments that, over a period of time, groups using computers will gradually develop close relational ties, despite some initial difficulties. (p. 146) |
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NOTE: The author did not specify theoretical statements. Testable statements (and tested in the lab experiment) are extracted from the paper. |
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Choudhury, V., and Sampler, J. 1997. “Information Specificity and Environmental Scanning: an Economic Perspective,” MIS Quarterly (21:1), pp. 25-53. |
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P1: An organization will increase the frequency with which it monitors a potential source of information in the environment until the point at which either (1) the marginal increase in surveillance costs is equal to the marginal reduction in the expected opportunity costs or (2) the probability of capturing the information is 1, whichever comes first. Further: Expected opportunity costs of failing to capture information from a source = (Expected value of the information) (Probability of failing to capture the information in a timely manner), and Probability of failing to capture the information in a timely manner = f (Frequency with which the source is monitored, Time specificity in acquisition of the information). |
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P2: Organizations will outsource the acquisition of environmental information that is low in organizational knowledge specificity but will retain internally the responsibility for acquiring information that is high in organizational knowledge specificity. |
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P3a: Decision makers will personally acquire information that is high in knowledge specificity in acquisition. |
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P3b: Decision makers will delegate the task of acquiring information with medium specificity in acquisition to subordinates. |
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P3c: For information that is low in knowledge specificity in acquisition, managers will delegate the task of acquiring information to (a) a central environmental scanning unit if the knowledge specificity in use of the information is low and (b) to a subordinate if the knowledge specificity in use of the information is high. |
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Compeau, D. R., and Higgins, C. A. 1995. “Computer Self-Efficacy: Development of a Measure and Initial Test,” MIS Quarterly (19:2), pp. 189-211. |
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H1: The higher the encouragement of use by members of the individual’s reference group, the higher the individual’s computer self-efficacy. |
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H2: The higher the encouragement of use by members of the individual’s reference group, the higher the individual's outcome expectations. |
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H3: The higher the use of the technology by others in the individual’s reference group, the higher the individual's computer self-efficacy. |
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H4: The higher the use of the technology by others in the individual’s reference group, the higher the individual's outcome expectations. |
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H5: The higher the support for computer users in the organization, the higher the individual’s computer self-efficacy. |
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H6: The higher the support for computer users in the organization, the higher the individual’s outcome expectations. |
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H7: The higher the individual’s computer self-efficacy, the higher his/her outcome expectations. |
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H8: The higher the individual’s computer self-efficacy, the higher his/her affect (or liking) of computer use. |
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H9: The higher the individual’s computer self-efficacy, the lower his/her computer anxiety. |
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H10: The higher the individual’s computer self-efficacy, the higher his/her use of computers. |
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H11: The higher the individual’s outcome expectations, the higher his/her affect (or liking) for the behavior. |
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H12: The higher the individual’s outcome expectations, the higher his/her use of computers. |
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H13: The higher the individual’s affect for computer use, the higher his/her use of computers. |
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H14: The higher the individual’s computer anxiety, the lower his/her use of computers. |
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Compeau, D. R., Higgins, C. A., and Huff, S. 1999. “Social Cognitive Theory and Individual Reactions to Computing Technology: A Longitudinal Study,” MIS Quarterly (23:2), pp. 145-158. |
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H1: The higher the individual’s computer self-efficacy, the higher his/her performance related outcome expectations. |
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H2: The higher the individual’s computer self-efficacy, the higher his/her personal outcome expectations. |
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H3: The higher the individual’s computer self-efficacy, the higher his/her affect (or liking) of computer use. |
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H4: The higher the individual’s computer self-efficacy, the lower his/her computer anxiety. |
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H5: The higher the individual’s computer self-efficacy, the higher his/her use of computers |
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H6: The higher the individual’s performance related outcome expectations, the higher his/her affect (or liking) for the behavior. |
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H7: The higher the individual’s personal outcome expectations, the higher his/her affect (or liking) for the behavior. |
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H8: The higher the individual’s performance related outcome expectations, the higher his/her use of computers. |
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H9: The higher the individual’s personal outcome expectations, the higher his/her use of computers. |
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H10: The higher the individual’s affect for computer use, the higher his/her use of computers. |
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H11: The higher the individual’s computer anxiety, the lower his/her use of computers. |
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Daft, R. L., Lengel, R. H., and Trevino, L. K. 1987. “Message Equivocality, Media Selection, and Manager Performance: Implications for Information Systems,” MIS Quarterly (11:3), pp. 355-366. |
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H1: Managerial information processing will be characterized by a positive relationship between message equivocality and media richness. |
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H2: Managers will select oral media for communication episodes high in equivocality and written media for communication episodes low in equivocality. |
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H3: Managers who are sensitive to the relationship between equivocality and media richness are more likely to be rated as high performers. |
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Davis, F. D. 1989. “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Quarterly (13:3), pp. 318-340. |
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H1: Perceived usefulness -----> Attitude toward using the system |
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H2: Perceived ease of use -----> Attitude toward using the system |
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DeLone, W. H., and McLean, E. R. 1992. “Information Systems Success: The Quest for the Dependent Variable,” Information Systems Research (3:1), pp. 60-96. |
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S1: SYSTEM QUALITY and INFORMATION QUALITY singularly and jointly affect both USE ad USER SATISFACTION (p. 83) |
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S2: The amount of USE can affect the degree of USER SATISFACTION positively or negatively (p. 83) |
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S3: The degree of USER SATISFACTION can affect the amount of USE positively or negatively (p. 83) |
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S4: USE and USER SATISFACTION are direct antecedents of INDIVIDUAL IMPACT (p. 83, 87) |
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S5: [INDIVIDUAL] IMPACT on individual performance should eventually have some ORGANIZATIONAL IMPACT (p. 87) |
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Dennis, A. R., Fuller, R. M., and Valacich, J. S. 2008. "Media, Tasks, And Communication Processes: A Theory Of Media Synchronicity." MIS Quarterly (32:3), pp. 575-600. |
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P1: Communication performance will depend on the fit between a medium’s synchronicity and the fundamental communication processes being performed. |
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P1a: For communication processes in which convergence on meaning is the goal, use of higher synchronicity media will lead to better communication performance. |
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P1b: For communication processes in which the conveyance of information is the goal, use of lower synchronicity media will lead to better communication performance. |
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P2: Transmission velocity improves shared focus which will have a positive impact on a medium’s capability to support synchronicity. |
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P3: Parallelism lowers shared focus which will have a negative impact on a medium’s capability to support synchronicity. |
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P4a: Media with more natural symbol sets (physical, visual, and verbal) have a greater capability to support synchronicity as compared to media with less natural symbol sets (written or typed). |
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P4b: Using a medium with a symbol set better suited to the content of the message will improve information transmission and information processing, and therefore will have a greater capacity to support synchronicity. |
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P5: Rehearsability lowers shared focus, which will have a negative impact on a medium’s capability to support synchronicity. |
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P6: Reprocessability lowers shared focus, which will have a negative impact on a medium’s capability to support synchronicity. |
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P7: Although individuals working together on tasks will benefit from the use of both high and low synchronicity media, their need for media synchronicity will depend on their level of familiarity with each other, with the task, and with the media. |
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P7a: Individuals working together with well established norms working on familiar tasks using familiar media will have the least need to use media supporting high synchronicity. |
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P7b: Individuals working together without well established norms working on unfamiliar tasks using unfamiliar media will have the greatest need to use media supporting high synchronicity. |
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Dimoka, A., Hong, Y., and Pavlou, P. A. 2012. “On Product Uncertainty in Online Markets: Theory and Evidence,” MIS Quarterly (36:2), pp. 395-426. |
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H1: Product uncertainty is distinct from, yet influenced by, seller uncertainty. |
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H2a: Product uncertainty (description and performance) is negatively associated with price premiums. |
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H2b: Seller uncertainty (adverse selection and moral hazard) is negatively associated with price premiums. |
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H3: The diagnosticity of online product descriptions (textual, visual, and multimedia) is negatively associated with product uncertainty. |
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H4: The negative effect of the diagnosticity of online product descriptions on product uncertainty is negatively moderated (attenuated) by seller uncertainty. |
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H5: The existence of third-party product assurances (inspection, history report, warranty) is negatively associated with product uncertainty. |
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Enns, H. G., Huff, S. L., and Higgins, C. A. 2003. “CIO Lateral Influence Behaviors: Gaining Peers’ Commitment to Strategic Information Systems,” MIS Quarterly (27:1), pp. 155-176. |
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H1: Rational persuasion will be positively related to the influence outcome. |
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H2: Consultation will be positively related to the influence outcome. |
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H3: Personal appeal will be positively related to the influence outcome. |
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H4: Ingratiation will be negatively related to the influence outcome. |
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H5: Exchange will be negatively related to the influence outcome. |
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H6: Coalition will be negatively related to the influence outcome. |
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H7: Pressure will be negatively related to the influence outcome. |
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Gattiker, T. F., and Goodhue, D. L. 2005. “What Happens After ERP Implementation: Understanding the Impact of Interdependence and Differentiation on Plant-Level Outcomes,” MIS Quarterly (29:3), pp. 559-585. |
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H1a: In a plant within an ERP implementation, the greater the interdependence of one plant with other plants in the organization, the greater the ERP-related coordination improvements accrued by that plant. |
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H1b: In a plant within an ERP implementation, the greater the interdependence of one plant with other plants in the organization, the greater the ERP-related task efficiency improvements accrued by that plant. |
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H2a: In a plant within an ERP implementation, the greater the differentiation of a plant from the other plants in an organization, the lower the ERP-related coordination improvements accrued by that plant. |
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H2b: In a plant within an ERP implementation, the greater the differentiation of a plant from the other plant in an organization, the lower the ERP-related task efficiency improvements accrued by that plant. |
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H3a: In a plant within an ERP implementation, customization moderates the negative impact of an individual plant's differentiation on coordination improvements from ERP accrued to that plant. |
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H3b: In a plant within an ERP implementation, customization moderates the negative impact of an individual plant's differentiation on task efficiency improvements of ERP accrued to that plant. |
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H4a: In a plant within an ERP implementation, greater time elapsed since ERP implementation is associated with greater coordination improvements of ERP accrued to that plant. |
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✓ |
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H4b: In a plant within an ERP implementation, grater time elapsed since ERP implementation is associated with greater task efficiency improvements of ERP accrued to that plant. |
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H5a: In a plant within an ERP implementation, greater data quality is associated with greater coordination improvements for the plant. |
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H5b: In a plant within an ERP implementation, greater data quality is associated with greater task efficiency for that plant. |
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H6a: In a plant within an ERP implementation, greater task efficiency is associated with greater local (plant) level overall benefits from ERP. |
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H6b: In a plant within an ERP implementation, greater improvements in coordination with other subunits are associated with greater local (plant) level overall benefits from ERP. |
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H6c: In a plant within and ERP implementation, greater data quality is associated with greater local level overall benefits from ERP. |
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Gefen, D., Karahanna, E, and Straub, D. W. 2003. “Trust and TAM in Online Shopping: An Integrated Model,” MIS Quarterly (27:1), pp. 51-90. |
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H1: Perceived Usefulness (PU) will positively affect intended use of a business-to-consumer Web site. |
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H2: Perceived Ease of Use (PEOU) will positively affect intended use of a business-to-consumer Web site. |
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H3: PEOU will positively affect PU of a business-to-consumer Web site. |
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H4: Trust in the e-vendor will positively affect intended use of a business-to-consumer Web site. |
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✓ |
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H5: Trust will positively affect PU. |
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H6: Familiarity with a trustworthy e-vendor will positively affect trust in that e-vendor. |
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H7: Calculative-based beliefs will positively affect trust in an e-vendor. |
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H8: Perceptions of situational normality will positively affect trust in an e-vendor. |
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H9: Perceptions of structural assurance built into a Web site will positively affect trust in an e-vendor. |
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H10: PEOU will positively affect trust in an e-vendor. |
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H11: Situational normality will positively affect PEOU. |
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H12: Familiarity with the e-vendor will positively affect PEOU. |
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Goodhue, D. L., and Thompson, R. L. 1995. “Task-technology Fit and Individual Performance,” MIS Quarterly (19:2), pp. 213-236. |
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P1: User evaluations of task-technology fit will be affected by both task characteristics and characteristics of the technology. |
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P2: User evaluations of task-technology fit will influence the utilization of information systems by individuals. |
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P3: User evaluations of task-technology fit will have additional explanatory power in predicting perceived performance impacts beyond that from utilization alone. |
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Griffith, T. L., Fuller, M. A., and Northcraft, G. B. 1998. “Facilitator Influence in Group Support Systems Intended and Unintended Effects,” Information Systems Research (9:1), pp. 20-36. |
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P1: The strength of a facilitator’s ability to influence to control the group's process will be a function of the facilitator's power. |
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P2: The strength of a facilitator’s influence will be greater if the facilitator has expert power or status than if the facilitator has only legitimate power. |
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P3: The effects of information processing on facilitator influence will be moderated by facilitator power. |
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P4: Facilitator influence is more likely to be intentional and not impartial when facilitators have agendas that conflict with the agenda of the group. |
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P5: Facilitator influence will be moderated by task characteristics. |
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P6: Facilitator awareness of power and information processing will moderate facilitator influence. |
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Griffith, T. L., Sawyer, J. E., and Neale, M. A. 2003. “Virtualness and Knowledge in Teams: Managing the Love Triangle of Organizations, Individuals, and Information Technology,” MIS Quarterly (27:2), pp. 265-287. |
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P1a: More virtual teams are more likely to transform implicit knowledge into explicit knowledge than are less virtual teams. |
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P1b: More virtual teams are more likely to have access to their extant explicit knowledge than are less virtual teams. |
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P2: Implicit knowledge can be transferred to explicit knowledge to the extent that a proactive effort is made to verbalize rules, terminology, and descriptions |
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P3a: Having acquired tacit knowledge from collocated sources, members of more virtual teams will be less able to transfer this knowledge to their teams than would members of less virtual teams. |
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P3b: Individual members of more virtual teams are less likely to acquire tacit knowledge from their teammates than are members of less virtual teams. |
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P4a: More virtual teams will have greater difficulty forming collective knowledge than will less virtual teams. This effect will be moderated by their experienced richness of communication. |
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P4b: To the extent that collective knowledge is formed in more virtual teams, their collective knowledge is expected to be more accessible via technological tools, than would the collective knowledge of less virtual teams. |
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P5a: More virtual teams have a greater likelihood of enacting an independent approach to their tasks and, therefore, are expected to have less shared understanding of these tasks than less virtual teams. |
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P5b: Access to and appropriation of tools and structures that support highly interdependent work will moderate this result on shared knowledge. |
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P6: The transition of potential team knowledge to usable knowledge is positively moderated by individual absorptive capacity. |
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P7: To the extent that more virtual work limits the social interaction of the team members, virtual team work will reduce the absorptive capacity of team members. |
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P8: The transition of potential team knowledge to usable knowledge is positively moderated by individuals' connections to relevant communities of practice. |
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P9a: More virtual teams will have greater access to communities of practice than will less virtual teams. |
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P9b: Tacit knowledge from members' links to communities of practice are less likely to be disseminated within more virtual teams than they are within less virtual teams. |
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P10: The transfer of potential team knowledge to usable team knowledge will be positively moderated by team transactive memory. |
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P11a: More virtual teams will have lower transactive memory development than less virtual teams. |
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P11b: The expected overall negative relationship between teams working more virtually and the development of transactive memory will be mitigated to the extent that technologies or organizational systems are used to support transactive memory development. |
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P12: The transition of potential team knowledge to usable knowledge is positively moderated by the level of synergy in the team. |
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P13: The relationship between team virtualness and synergy will be moderated by the degree of match between team task and technology use. |
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Hong, W., Thong, J Y. L., and Tam, K. Y. 2004. “Does Animation Attract Online Users’ Attention? The Effects of Flash on Information Search Performance and Perceptions,” Information Systems Research (15:1), pp. 60-86. |
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H1a: Response time will be shorter when the target item is flashed compared to when it is not flashed. |
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H1b: Response time will be longer when a non-target item is flashed compared to when it is not flashed. |
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H1c: Response time reduces to a greater extent in high than in low local density environments when the target item is flashed compared to when it is not flashed. |
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H1d: Response time increases to a greater extent in high than in low local density environments when a non-target item is flashed compared to when it is not flashed. |
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H2a: Recall of the target item will be different when it is flashed compared to when it is not flashed. |
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H2b: Recall of the target item will be different when a non-target item is flashed compared to when it is not flashed. |
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H2c: Recall of the non-target item will be higher when it is flashed compared to when it is not flashed. |
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H2d: Recall of the non-target items will be lower when the target item is flashed compared to when it is not flashed. |
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H2e: Recall of the non-target items that are not flashed will be lower when a non-target item is flashed compared to when it is not flashed. |
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H2f: Overall recall will be lower when the target item is flashed compared to when it is not flashed. |
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H2g: Overall recall will be different when a non-target item is flashed compared to when it is not flashed. |
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H3a: Focused attention will be lower when the target item is flashed compared to when it is not flashed. |
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H3b: Focused attention will be lower when a non-target item is flashed compared to when it is not flashed. |
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H4a: Attitudes toward using the website will be different when the target item is flashed compared to when it is not flashed. |
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H4b: Attitudes toward using the website will be less favorable when a non-target item is flashed compared to when it is not flashed. |
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Jarvenpaa, S. L., Shaw, T. R., and Staples, D. S. 2004. “Toward Contextualized Theories of Trust: The Role of Trust in Global Virtual Teams,” Information Systems Research (15:3), pp. 250-267. |
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H1a: Initial trustworthiness has a direct, positive relationship with early trust. |
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H1b: There is a direct, positive relationship between the level of early communication by other members on the team and a member’s early trust. |
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H1c: A member’s initial trustworthiness in the team has a direct, positive relationship with the member’s early perceived cohesiveness of the team. |
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H2a: Early trust moderates the relationship between the level of communication by other members on the team and perceived satisfaction with the team. |
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H2b: Early trust moderates the relationship between the level of later communication by other members on the team and perceived cohesiveness of the team. |
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H2c: Early trust moderates the relationship between the level of communication by other members on the team and perceived quality of a team’s outcome. |
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H2d: Trust has a negligible effect on the quality of task performance. |
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H3: The effect (either direct or moderation) of trust on attitudes of team members is weak in a situation with strong structure. |
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Johnston, A. C., and Warkentin, M. 2010. "Fear Appeals and Information Security Behaviors: An Empirical Study," MIS Quarterly (34:1), pp. 549-566. |
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H1: Response efficacy will have a positive effect on end user intentions to adopt recommended individual computer security actions with respect to spyware. |
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H2: Self-efficacy will have a positive effect on end user intentions to adopt recommended individual computer security actions with respect to spyware. |
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H3: Social influence will have a positive effect on end user intentions to adopt recommended individual computer security actions with respect to spyware. |
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✓ |
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H4a: Perceptions of threat severity will negatively influence perceptions of response efficacy. |
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H4b: Perceptions of threat severity will negatively influence perceptions of self-efficacy. |
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H5a: Perceptions of threat susceptibility will negatively influence perceptions of response efficacy. |
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H5b: Perceptions of threat susceptibility will negatively influence perceptions of self-efficacy. |
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Kankanhalli, A., Tan, B. C. Y., and Wei, K. K. 2005. “Contributing Knowledge to Electronic Knowledge Repositories: An Empirical Investigation,” MIS Quarterly (29:1), pp. 1-42. |
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H1: Loss of knowledge power is negatively related to EKR usage by knowledge contributors under conditions of weak pro-sharing norms. |
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H2a: Codification effort is negatively related to EKR usage by knowledge contributors under conditions of weak generalized trust. |
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H2b: Codification effort is negatively related to EKR usage by knowledge contributors under conditions of weak pro-sharing norms. |
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H2c: Codification effort is negatively related to EKR usage by knowledge contributors under conditions of weak identification. |
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H3a: Organizational reward is positively related to EKR usage by knowledge contributors under conditions of weak pro-sharing norms. |
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H3b: Organizational reward is positively related to EKR usage by knowledge contributors under conditions of weak identification. |
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H4: Image is positively related to EKR usage by knowledge contributors under conditions of weak pro-sharing norms. |
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H5: Reciprocity is positively related to EKR usage by knowledge contributors under conditions of weak pro-sharing norms. |
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H6: Knowledge self-efficacy is positively related to EKR usage by knowledge contributors. |
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H7: Enjoyment in helping others is positively related to EKR usage by knowledge contributors. |
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Karahanna, E., Straub, D., and Chervany, N. L. 1999. “Information Technology Adoption Across Time: A Cross-Sectional Comparison of Pre-adoption and Post-adoption Beliefs,” MIS Quarterly (23:2), pp. 183-213. |
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H1: The relationship between attitude and behavioral intention will be stronger for users than for potential adopters of an IT. |
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H2: There will be more behavioral beliefs underlying attitude for users than for potential adopters of an IT. |
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H3: The relationship between subjective norm and behavioral intention will be stronger for potential adopters than for users of an IT. |
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Karimi, J., Somers, T. M., and Gupta, Y. P. 2004. “Impact of Environmental Uncertainty and Task Characteristics on User Satisfaction with Data,” Information Systems Research (15:2), pp. 175-193. |
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H1a: Higher levels of dynamism increase the non-routineness of tasks. |
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H1b: Higher levels of dynamism increase the interdependence of tasks. |
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H2a: Higher levels of heterogeneity increase the non-routineness of tasks. |
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H2b: Higher levels of heterogeneity increase the interdependence of tasks. |
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H3a: Higher levels of hostility increase the non-routineness of tasks. |
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H3b: Higher levels of hostility increase the interdependence of tasks. |
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H4: Task non-routineness has negative effects on user satisfaction with data. |
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H5: Task interdependence has negative effects on user satisfaction with data. |
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Keil, M., Tan, B. C. Y., Wei, K. K., Saarinen, T., Tuunainen, V., and Wassenaar, A. 2000. “A Cross-Cultural Study on Escalation of Commitment Behavior in Software Projects,” MIS Quarterly (24:2), pp. 299-325. |
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H1: In all cultures, risk propensity will have a significant inverse effect on risk perception. |
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H1a: The inverse relationship between risk propensity and risk perception will be stronger in cultures lower on uncertainty avoidance. |
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H2: In all cultures, risk perception will have a significant inverse effect on willingness to continue a project. |
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H3: In all cultures, risk propensity will have a significant direct effect on willingness to continue a project. |
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H4: In all cultures, level of sunk cost will have a significant inverse effect on risk perception. |
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H4a: The inverse relationship between level of sunk cost and risk perception will be stronger in cultures lower on uncertainty avoidance. |
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H5: In all cultures, level of sunk cost will have a significant direct effect on willingness to continue a project. |
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Kettinger, W. J., and Lee, C. C. 2005. “Zones of Tolerance Alternative Scales for Measuring Information Systems Service Quality,” MIS Quarterly (29:4), pp. 607-623. |
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S1: Is an IS-adapted ZOT SERVQUAL psychometrically sound? (p. 610) |
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S2: More specifically, do the two expectation levels of IS service quality (desired and adequate) and the perceived IS service level possess the same dimensions of SERVQUAL (including common items)? (p. 610) |
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Kim, K. K., and Michelman, J. E. 1990. “An Examination of Factors for the Strategic Use of Information Systems in the Health Care Industry,” MIS Quarterly (14:2), pp. 201-215. |
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P1: For the integration of HIS [Hospital Information Systems] to be successful, the conflict between physicians and administrators is an important factor that a hospital must effectively deal with. |
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P2: Integration of isolated TPS/IRS [Transaction Processing Systems, Information Reporting Systems] throughout the organization is an important factor in the strategic use of healthcare information systems. |
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P3: Healthcare organizations can achieve competitive advantages through the successful application of TPS/IRS to strategic areas. |
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Kim, S. S., Malhotra, N. K., and Narasimhan, S. 2005. “Two Competing Perspectives on Automatic Use: A Theoretical and Empirical Comparison,” Information Systems Research (16:4), pp. 418-432. |
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H1-0: As past use increases, the influence of users’ evaluations on usage intention will increase or at least stay the same. |
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H1-A: As past use increases, the influence of users’ evaluations on usage intention will decrease. |
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H2-0: As past use increases, the influence of usage intention on IT use will increase or at least stay the same. |
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H2-A: As past use increases, the influence of usage intention on IT use will decrease. |
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NOTE: H1-0, H2-0 (null hypotheses), H1-A, H2-A (alternative hypotheses) |
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Ko, D. G., Kirsch, L. J., and King, W. R. 2005. “Antecedents of Knowledge Transfer from Consultants to Clients in Enterprise System Implementations,” MIS Quarterly (29:1), pp. 59-85. |
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H1: The more arduous the relationship between a consultant and a client, the less the knowledge transfer. |
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H2: The greater the shared understanding between a consultant and a client, the greater the knowledge transfer. |
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H3: The greater the absorptive capacity of a client, the greater the knowledge transfer. |
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H4: The more intrinsically motivated the client, the greater the knowledge transfer. |
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H5: The more intrinsically motivated the consultant, the greater the knowledge transfer. |
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H6: The more extrinsically motivated the client, the greater the knowledge transfer. |
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H7: The more extrinsically motivated the consultant, the greater the knowledge transfer. |
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H8: The more credible the consultant, the greater the knowledge transfer. |
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H9: The more credible the consultant, the less arduous the relationship between consultant and client. |
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H10: The greater a client's communication decoding competence, the less arduous the relationship between a consultant and a client. |
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H11: The greater a consultant's communication encoding competence, the less arduous the relationship between a consultant and a client |
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H12: The greater a client's communication decoding competence, the greater the shared understanding between a consultant and a client. |
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H13: The greater a consultant’s communication encoding competence, the greater the shared understanding between a consultant and a client. |
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✓ |
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Koh, C., Ang, S., and Straub, D. W. 2004. “IT Outsourcing Success: A Psychological Contract Perspective,” Information Systems Research (15:4), pp. 356-373. |
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H1: Perceived outsourcing success by the customer is positively associated with the extent to which supplier obligations have been fulfilled. |
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H2: Perceived outsourcing success by the supplier is positively associated with the extent to which customer obligations have been fulfilled. |
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Leidner, D. E., and Kayworth, T. 2006. “Review: A Review of Culture in Information Systems Research: Toward a Theory of Information Technology Conflict,” MIS Quarterly (30:2), pp. 357-399. |
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P1a: The greater the cultural distance between the group responsible for championing the IT and the group adopting the IT, the greater the system conflict experienced by the group adopting the IT. |
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P1b: The greater the breadth of IT implementation across groups, the greater the system conflict experienced by the organization. |
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P1c: The greater the system conflict experienced by a group, the less likely the group is to be a forerunner in the adoption of the system. |
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P1d: The greater the system conflict experienced by a group, the greater the modification of use to support the group’s values. |
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P2a: The lower the contribution conflict experienced by the most powerful actors within a group, the lower the contribution conflict experienced by the group. |
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P2b: The lower the contribution conflict in a group to which another group aspires, the lower the contribution conflict in the aspiring group. |
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P2c: The greater the contribution conflict experienced by a group, the less strategic a role that IT will play in that group. |
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P2d: The greater the contribution conflict experienced by a group, the less likely the innovative uses of IT by the group. |
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P3a: The greater the difference in the IT values between the champion group and the user group, the greater the vision conflict experienced by the user group. |
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P3b: The lesser the involvement of the most powerful within a user group in development, the greater the vision conflict experienced by the user group. |
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P3c: The higher the vision conflict a group has with respect to a system, the lower the adoption rate of the system by the group. |
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✓ |
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P3d: The greater the vision conflict experienced by a group, the greater the potential change to the group's IT values. |
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✓ |
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P4: Managers can reduce all forms of conflict by promoting shared IT values. |
✓ |
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P5: The emergence and resolution of the three types of conflict will, over time, result in cultural changes. [three types of conflict: system conflict, contribution conflict, vision conflict] |
✓ |
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Lewis, W., Agarwal, R., and Sambamurthy, V. 2003. “Sources of Influence on Beliefs about Information Technology Use: An Empirical Study of Knowledge Workers,” MIS Quarterly (27:4), pp. 657-678. |
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H1: Beliefs about the ease of use of a technology have a significant positive influence on beliefs about the usefulness of the technology. |
✓ |
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H2a: Perceived top management support for the use of a technology has a significant positive influence on individual beliefs about the usefulness of the technology. |
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✓ |
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H2b: Perceived local management support for the use of a technology has a significant positive influence on individual beliefs about the usefulness of the technology. |
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✓ |
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H3: Perceived social influence from referent others have a significant positive influence on individual beliefs about the usefulness of the technology. |
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✓ |
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H4a: Computer self-efficacy has a significant positive influence on individual beliefs about the usefulness of a technology. |
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✓ |
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H4b: Personal innovativeness in the domain of information technology has a significant positive influence on individual beliefs about the usefulness of a technology. |
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✓ |
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H4c: Computer self-efficacy has a significant positive influence on individual beliefs about the ease of use of a technology. |
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✓ |
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H4d: Personal innovativeness in the domain of information technology has a significant positive influence on individual beliefs about the ease of use of a technology. |
✓ |
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Liang, H., Saraf, N., and Hu, Q. 2007. "Assimilation of Enterprise Systems: The Effect of Institutional Pressures and the Mediating Role of Top Management," MIS Quarterly (31:1), pp. 59-87. |
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H1: Stronger top management beliefs about the benefits of ERP lead to higher levels of top management participation in the ERP assimilation process. |
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H2: Higher levels of top management participation in the ERP assimilation process lead to a higher extent of ERP assimilation within the organization. |
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H3a: Higher levels of mimetic pressures lead to stronger top management beliefs about the benefits of ERP assimilation. |
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H3b: Higher levels of mimetic pressures lead to higher levels of top management participation in the ERP assimilation process. |
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H4: Higher levels of coercive pressures lead to higher levels of top management participation in the ERP assimilation process. |
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H5: Higher levels of normative pressures lead to higher levels of top management participation in the ERP assimilation process. |
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Liang, H., and Xue, Y. 2009. “Avoidance of Information Technology Threats: A Theoretical Perspective,” MIS Quarterly (33:1), pp. 71-90. |
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P1: Users’ avoidance behavior under threat of malicious IT is a dynamic process that intends to enlarge the discrepancy between users’ current state and their undesired end state. |
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P2: Users appraise how to cope with malicious IT only after they appraise the threat of malicious IT. |
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P3: Users may employ either problem-focused or emotion-focused coping to reduce the threat of malicious IT. |
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P3a: Users perform problem-focused coping to objectively mitigate the danger of malicious IT, thus reducing the threat. |
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P3b: Users perform emotion-focused coping to subjectively reduce the threat of malicious IT. |
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P4: Users’ IT threat perception is determined by their perceived susceptibility and severity of malicious IT. |
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P4a: The more users believe that they are susceptible to malicious IT, the stronger their threat perception will be. |
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P4b: The more users believe that the negative consequences caused by malicious IT are severe, the stronger their threat perception will be. |
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P4c: Perceived severity and susceptibility have a positive interaction effect on perceived threat. The boundary condition of this interaction is that if either variable takes a value of zero, the other will have no relationship with perceived threat. |
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P5: A safeguarding measure’s effectiveness, costs, and users’ self-efficacy toward it influence users’ perceptions of avoidability of the IT threat. |
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P5a: Users are more likely to perceive an IT threat as avoidable by taking a safeguarding measure when this measure can effectively reduce the IT threat. |
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P5b: Users are more likely to perceive an IT threat as avoidable by taking a safeguarding measure when the costs associated with this measure decrease. |
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P5c: Users are more likely to perceive an IT threat as avoidable by taking a safeguarding measure when they feel confident in taking this measure. |
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P6: Users who perceive the threat of malicious IT will be motivated to avoid the threat by employing safeguarding measures and/or perform emotion-focused coping. |
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P7: Users who perceive an IT threat to be avoidable will be motivated to take safeguarding measures and less likely perform emotion-focused coping. In contrast, users who perceive an IT threat to be unavoidable will likely perform emotion-focused coping. |
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P8: The relationship between perceived threat and avoidance motivation is a convex curve so that it becomes weaker as perceived threat increases. |
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P9: The relationship between perceived avoidability and avoidance motivation is negatively moderated by perceived threat so that it is weaker when perceived threat increases. The boundary condition is that if perceived threat is zero, there would be no relationship between perceived avoidability and avoidance motivation. |
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P10: Users’ avoidance motivation leads to their avoidance behavior, which is taking safeguarding measures to reduce the IT threat. |
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P11: Users’ risk tolerance negatively influences their perception of IT threat. |
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P12: Social influence affects users’ evaluation of IT threats and safeguarding measures as well as their motivation to avoid IT threats. |
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Lilien, G. L. Rangaswamy, A., Van Bruggen, G. H., and Starke, K. 2004. “DSS Effectiveness in Marketing Resource Allocation Decisions: Reality vs. Perception,” Information Systems Research (15:3), pp. 216-235. |
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H1: Model-based DSSs will improve objective decision outcomes relative to the Excel-only tool. |
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H1a: DSSs will generate more incremental returns/profits relative to the Excel-only tool. |
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H1b: DSSs will result in more favorable experts’ evaluation of the decisions relative to the Excel-only tool. |
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H2: DSSs will have no effect on the subjective evaluation of the decision outcomes relative to the Excel-only tool. |
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H2a: DSSs will have no differential effects on decision satisfaction relative to the Excel-only tool. |
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H3: DSSs will have mixed effects on the decision process. |
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H3a: DSSs will lead to less effort devoted to problem solving than the Excel-only tool. |
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H3b: DSSs will enhance the quality of the discussions as compared to the Excel-only tool. |
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H3c: DSSs will lead to fewer decision alternative generated than the Excel-only tool. |
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H4: DSSs will have mixed effects on the subjective evaluation of the decision process. |
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H4a: DSSs will have no effect on perceived problem complexity relative to the Excel-only tool. |
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H4b: DSSs will lead to less perceived learning relative to the Excel-only tool. |
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H4c: DSSs will be perceived as useful relative to the Excel-only tool. |
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Lim, K. H., Benbast, I., and Ward, L. M. 2000. “The Role of Multimedia in Changing First Impression Bias,” Information Systems Research (11:2), pp. 115-136. |
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H1: A multimedia system will reduce the influence of first impression more than a text-based system. |
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H2: A multimedia system will lead to a higher level of confidence with the decision made than will a text-based system. |
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Lowry, P. B., Zhang, J., Wang, C., and Siponen, M. 2016. “Why Do Adults Engage in Cyberbullying on Social Media? An integration of Online Dis-inhibition and Deindividuation Effects with the Social Structure and Social Learning Model,” Information Systems Research (27:4), pp. 962-986. |
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H1: An increase in anonymity is associated with increased cyberbullying NSI. |
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H2: An increase in anonymity is associated with decreased cyberbullying costs. |
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H3: An increase in anonymity is associated with increased cyberbullying benefits. |
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H4: An increase in anonymity is associated with increased cyberbullying neutralization. |
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H5: An increase in anonymity is associated with decreased cyberbullying situational morality. |
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H6: An increase in cyberbullying NSI is associated with increased cyberbullying. |
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H7a: An increase in cyberbullying benefits is associated with increased cyberbullying. |
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H7b: An increase in cyberbullying costs is associated with decreased cyberbullying. |
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H8a: An increase in cyberbullying neutralization is associated with increased cyberbullying. |
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H8b: An increase in situational morality is associated with decreased cyberbullying. |
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Majchrzak, A., Rice, R. E., Malhotra, A., King, N., and Ba, S. 2000. “Technology Adaptation: The Case of a Computer-Supported Inter-Organizational Virtual Team,” MIS Quarterly (24:4), pp. 569-600. |
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RQ1: Can the workgroup adapt any or all structures, or does it primarily try to adapt to the technology's initial spirit? |
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RQ2: Do pre-existing structures constrain the workgroup's adaptation process, even when these structures are malleable? |
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RQ3: After the initial adaptation to achieve alignment, does the workgroup experience the need for further adaptations? |
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RQ4: What is the nature of these adaptations: are they discontinuous, responding to windows of opportunities, or are they continuous, gradually closing misalignments? |
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Majchrzak, A., Beath, C. M., Lim, R. A., and Chin, W. W. 2005a. “Managing Client Dialogues During Information Systems Design to Facilitate Client Learning,” MIS Quarterly (29:4), pp. 653-672. |
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H1: A team’s use of collaboration elaboration (CE) would be related to increased client learning, after controlling for cooperative interdependence. (p. 658) |
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H2: Client learning would be related to superior IS design outcomes, after controlling for developers’ communication quality. (p. 658) |
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NOTE: The authors did not present their research hypotheses explicitly. Testable statements are extracted from their paper. The basic proposition of the research is as follows: “The more collaborative elaboration (CE) experienced during the IS design phase of an IS development project, the greater the client learning and the greater the client learning, the more positive the IS design-phase outcomes.” |
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Majchrzak, A., Malhotra, A., and John, R. 2005b. “Perceived Individual Collaboration Know-How Development Through Information Technology–Enabled Contextualization: Evidence from Distributed Teams,” Information Systems Research (16:1), pp. 9–27. |
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H: For individuals that are performing non-routine tasks, there is a linear positive relationship between their perceived collaboration know-how development and their perceptions of the degree of support provided by IT for their contextualization needs. There is a curvilinear relationship between the development of collaboration know-how and the perceived degree of contextualization support provided by the IT system for individuals engaged in routine tasks in distributed teams. |
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Malhotra, N. K., Kim, S. S., and Agarwal, J. 2004. “Internet Users’ Information Privacy Concerns (IUIPC): The Construct, the Scale, and a Causal Model,” Information Systems Research (15:4), pp. 336-355. |
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H1: Internet users’ information privacy concerns will have a negative effect on trusting beliefs. |
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H2: Internet users’ information privacy concerns will have a positive effect on risk beliefs. |
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H3: Trusting beliefs will have a negative effect on risk beliefs. |
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H4: Trusting beliefs will have a positive effect on intention to reveal personal information. |
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H5: Risk beliefs will have a negative effect on intention to reveal personal information. |
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H6: A marketer’s request for more sensitive information will have a negative effect on trusting beliefs. |
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H7: A marketer’s request for more sensitive information will have a positive effect on risk beliefs. |
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H8: A marketer’s request for more sensitive information will have a negative effect on intention to reveal personal information. |
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Malhotra, A., Gosain, S., and El Sawy, O. A. 2005. “Absorptive Capacity Configurations in Supply Chains: Gearing for Partner-enabled Market Knowledge Creation,” MIS Quarterly (29:1), pp. 145-187. |
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P1: Enterprises engaged in collector type partnerships will |
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P1a: Attain limited knowledge creation potential due to a lack of joint decision-making activities with their partner, which inhibits their ability to acquire and assimilate information that is of high quality and privileged in nature. |
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P1b: Be able to acquire a broad range of information aided by standard electronic business interfaces, but lack the capability (information systems for information interpretation) to transform the information exchanged to create operational efficiency and new knowledge. |
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P1c: Exchange higher amounts of coordination information due to a lack of modular organization of processes interlinked with their partner, potentially overwhelming their capability to assimilate and transform more value-added information. |
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P2: Enterprises engaged in connector type partnerships will |
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P2a: Have a low ability to create market knowledge, even though they have well-developed capabilities to acquire information, due to minimal capacity to assimilate and transform information resulting from the low level of development and utilization of partner interface-directed information systems. |
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P2b: Achieve high operational efficiency through modular design of their interlinked processes, resulting in the reduced need for coordination information exchange. |
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P3: Enterprises engaged in cruncher type partnerships |
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P3a: Exchange a narrow range of information that is not privileged in nature and exhibit moderate acquisition and assimilation capacities due to a lack of well-developed integrative process mechanisms. |
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P3b: Are only able to create a moderate level of market knowledge from high quality information exchanged with partners due to moderate acquisition and assimilation capacities, even though their well-developed information systems capabilities enable high transformation capacity. |
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P4: Enterprises engaged in coercive type partnerships achieve operational efficiency and market-related knowledge creation through mandated information sharing dictates, based on substantial power differences between the two partners. However, in the long term, coercive type partnerships may be unstable due to potential deleterious effects. |
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P4a: The coerced partners may refuse to adopt new interface formats or invest in new information sharing systems in the future, thereby impeding the development of coercing enterprises’ assimilation capacity. |
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P4b: The coercing enterprises may suffer from a progressive reduction in their assimilation capacity, because they do not collaborate intensively through joint decision making activities, engaging only in a limited range of information exchange with specific partners. |
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P5: Enterprises engaged in collaborator type partnerships will achieve high operational efficiency and knowledge creation by |
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P5a: Creating high absorptive capacity (acquisition, assimilation, and transformation) through the development of inter-organizational integrative process mechanisms as well as the complementary partner interface-directed information systems. |
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P5b: Using joint decision making to exchange privileged information and signal collaborative intent, as well as using standard interfaces to reduce information processing needs. |
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McKeen, J. D., Guimaraes, T., and Wetherbe, J. C. 1994. “The Relationship between User Participation and User Satisfaction: an Investigation of Four Contingency Factors,” MIS Quarterly (18:4), pp. 427-45. |
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H1: A positive relationship exists between user participation and user satisfaction. |
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H2: The greater the task complexity, the greater the relationship between user participation and user satisfaction. |
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H3: The greater the system complexity, the greater the relationship between user participation and user satisfaction. |
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H4: The greater the user influence, the greater the relationship between user participation and user satisfaction. |
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H5: The greater the quality of user-developer communication, the greater the relationship between user participation and user satisfaction. |
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Melville, N., Kraemer, K., and Gurbaxani, V. 2004. “Review: Information Technology and Organizational Performance: An Integrative Model of IT business Value,” MIS Quarterly (28:2), pp. 283-322. |
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P1a: The IT resource including both technology and human expertise creates economic value for a focal firm by conferring operational efficiencies that vary in magnitude and type depending upon the organizational and technological context. |
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P1b: Human IT expertise complementary to technological IT resources may create temporary competitive advantages that underlie performance differences among firms. |
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P2a: Certain organizational resources are complementary to the IT resource in the generation of IT business value for the focal firm; the existence and magnitude of the complementarity between any two specific instantiations of these resources varies depending upon the organizational and technological contexts. |
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P2b: The greater the inimitability of rare organizational resources that are complementary to IT and lacking substitutes, the greater the degree to which the focal firm can obtain a sustained competitive advantage. |
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P3a: Industry characteristics moderate the ability of firms to apply IT for improved organizational performance and to capture the resulting benefits. |
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P3b: The greater the degree of competition in an industry, the greater the extent to which firms achieve efficiency gains via IT. |
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P3c: The greater the degree of competition in an industry, the lower the extent to which firms are able to capture the benefits of efficiency gains and achieve profitability gains via IT. |
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P4a: The IT and non-IT resources and the business processes of electronically connected trading partners shape the focal firms ability to generate and capture organizational performance impacts via IT. |
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P4b: The greater the degree of focal firm power relative to its trading partners connected via inter-organizational information systems, the greater its share of net value from deployment of the systems. |
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P5a: The macro environment shapes the degree to which firms can apply IT for organizational improvement. |
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P5b: Telecommunications infrastructure a complementary and potentially co-specialized asset with the IT resource moderates the economic value of an inter-organizational information system to the focal firm and its trading partners; the extent of moderation varies depending on the organizational and technological context. |
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Nissen, M. E., and Sengupta, K. 2006. “Incorporating Software Agents into Supply Chains: Experimental Investigation with a Procurement Task,” MIS Quarterly (30:1), pp. 145-166. |
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H1a: [in tasks with low specification ambiguity] The use of information retrieval agents will result in higher quality procurement choices than those made manually (i.e., higher procurement economy and accuracy). |
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H1b: [in tasks with low specification ambiguity] The use of advisory agents will result in higher quality procurement choices than those made manually (i.e., higher procurement economy and accuracy). |
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H1c: [in tasks with low specification ambiguity] The use of performative agents will result in higher quality procurement choices than those made manually (i.e., higher procurement economy and accuracy). |
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H1d: [in tasks with low specification ambiguity] The use of performative agents will result in higher quality procurement choices than those supported by either information retrieval or advisory agents (i.e., higher procurement economy and accuracy). |
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H2a: [in tasks with moderate specification ambiguity] The use of information retrieval agents will result in higher quality procurement choices than those made manually (i.e., higher procurement economy and accuracy). |
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H2b: [in tasks with moderate specification ambiguity] The use of advisory agents will result in higher quality procurement choices than those made manually (i.e., higher procurement economy and accuracy). |
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H2c: [in tasks with moderate specification ambiguity] The use of performative agents will result in higher quality procurement choices than those made manually (i.e., higher procurement economy and accuracy). |
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H2d: [in tasks with moderate specification ambiguity] The use of performative agents will result in higher quality procurement choices than those supported by either information retrieval or advisory agents (i.e., higher procurement economy and accuracy). |
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H3a: [in tasks with high specification ambiguity] The use of information retrieval agents will result in lower quality procurement choices than those made manually (i.e., lower procurement economy and accuracy). |
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H3b: [in tasks with high specification ambiguity] The use of advisory agents will result in lower quality procurement choices than those made manually (i.e., lower procurement economy and accuracy). |
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H3c: [in tasks with high specification ambiguity] The use of performative agents will result in lower quality procurement choices than those made manually (i.e., lower procurement economy and accuracy). |
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H3d: [in tasks with high specification ambiguity] The use of performative agents will result in lower quality procurement choices than those supported by either information retrieval or advisory agents (i.e., lower procurement economy and accuracy). |
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Pavlou, P. A., and Gefen, D. 2004. “Building Effective Online Market- places with Institution-Based Trust,” Information Systems Research (15:1), pp. 37-59. |
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H1a: The perceived effectiveness of the feedback mechanism increases buyer trust in the community of sellers. |
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H1b: The perceived effectiveness of the feedback mechanism reduces buyers’ perceived risk of transacting with the community of sellers. |
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H2a: The perceived effectiveness of escrow services increases buyer trust in the community (not negated) of sellers. |
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H2b: The perceived effectiveness of escrow services reduces buyer perceived risk of transacting with the community of sellers. |
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H3a: The perceived effectiveness of credit card guarantees increases buyer trust in the community of sellers. |
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H3b: The perceived effectiveness of credit card guarantees reduces buyer perception of risk of transacting with the community of sellers. |
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H4a: Trust in the intermediary increases buyer trust in the community of sellers. |
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H4b: Trust in the intermediary reduces buyer perceived risk of transacting with the community of sellers. |
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H5: Trust in the community of sellers reduces the perceived risk of transacting with sellers in online marketplaces. |
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H6: Trust in the community of sellers increases buyer intentions to transact with sellers in the specific online marketplace. |
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H7: Perceived risk in the community of sellers decreases intentions to transact with sellers in an online marketplace. |
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H8: Buyer intentions to transact with sellers in an online marketplace are positively related to transaction behavior. |
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Pavlou, P. A., and Gefen, D. 2005. “Psychological Contract Violation in Online Marketplaces: Antecedents, Consequences, and Moderating Role,” Information Systems Research (16:4), pp. 372-399. |
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H1: Psychological contact violation due to an individual seller in a positively influences Psychological Contract Violation with the entire with the entire community of sellers in the online marketplace. |
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H2: Psychological Contract Violation with the community of sellers decreases a buyer’s trust in the community of sellers. |
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H3: Psychological Contract Violation with the community of sellers increases a buyer’s perceived risk from transacting with the community of sellers. |
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H4: Psychological Contract Violation with the community of sellers reduces buyers’ intentions to transact with the community of sellers. |
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H5: Psychological Contract Violation with the community of sellers results in lower price premiums for an identical product. |
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H6: Transaction intentions increase a buyer’s actual transaction behavior with the community of sellers. |
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H7a: Price premiums increase a buyer’s actual transaction behavior with the community of sellers. |
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H7b: Higher transaction intentions result in higher price premiums for an identical product. |
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H8: Psychological Contract Violation with the community of sellers in a marketplace decreases the buyer’s perceived effectiveness of the marketplace’s institutional structures. |
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H9a: PCV with the community of sellers negatively moderates (attenuates) the relationship between the perceived effectiveness of institutional structures and the trust in the community of sellers. |
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H9b: PCV with the community of sellers positively moderates (reinforces) the negative relationship between the perceived effectiveness of institutional structures and the perceived risk from the community of sellers. |
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H10a: PCV with the community of sellers negatively moderates (attenuates) the relationship between trust in the community of sellers and buyer intentions to transact with the community of sellers. |
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H10b: PCV with the community of sellers positively moderates (reinforces) the negative relationship between perceived risk from the community of sellers and buyer intentions to transact with the community of sellers. |
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H11a: A buyer’s positive experience decreases the perception of Psychological Contract Violation with the community of sellers. |
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H11b: Sellers’ positive past performance decreases the perception of Psychological Contract Violation with the community of sellers. |
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Pavlou, P. A., Liang, H., and Xue, Y. 2007. “Understanding and Mitigating Uncertainty in Online Environments: A Principal-Agent Perspective,” MIS Quarterly (31:1), pp. 105-136. |
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H1: Perceived uncertainty negatively influences a buyer’s intentions to purchase products online. |
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H2: A buyer’s purchase involvement positively moderates (reinforces) the negative relationship between perceived uncertainty and intentions to purchase products online. |
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H3: Intention to purchase online positively influences a buyer’s actual online purchases. |
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H4a: Perceived information asymmetry positively influences a buyer’s perceived uncertainty. |
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H4b: Fears of seller opportunism positively influence a buyer’s perceived uncertainty. |
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H4c: Information privacy concerns positively influence a buyer’s perceived uncertainty. |
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H4d: Information security concerns positively influence a buyer’s perceived uncertainty. |
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H5a: Trust mitigates a buyer’s perceived information asymmetry. |
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H5b: Trust mitigates a buyer’s fears of seller opportunism. |
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H5c: Trust mitigates a buyer’s information privacy concerns. |
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✓ |
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H5d: Trust mitigates a buyer’s information security concerns. |
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✓ |
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H6a: Website informativeness mitigates a buyer’s perceived information asymmetry. |
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✓ |
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✓ |
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H6b: Website informativeness mitigates a buyer’s fears of seller opportunism. |
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H6c: Website informativeness mitigates a buyer’s information privacy concerns. |
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H6d: Website informativeness mitigates a buyer’s information security concerns. |
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✓ |
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H7a: Product diagnosticity mitigates a buyer’s perceived information asymmetry. |
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✓ |
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H7b: Product diagnosticity mitigates a buyer’s fears of seller opportunism. |
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✓ |
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H8a: Social presence mitigates a buyer’s information privacy concerns. |
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H8b: Social presence mitigates a buyer’s information security concerns. |
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Potter, R. E. and Balthazard, P. 2004. “The Role of Individual Memory and Attention Processes during Electronic Brainstorming,” MIS Quarterly (28:4), pp. 621-643. |
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H1: Participants exposed to input cueing will generate significantly fewer ideas than those who are not. |
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H2: Participants exposed to cause cueing will generate significantly more ideas than participants who are not. |
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H3: Participants exposed to both cause cueing and input cueing will generate significantly fewer ideas than participants exposed to cause cueing but not input cueing. |
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H4: The number of high quality ideas generated will be positively correlated with the number of ideas generated in each condition. |
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Rai, A., Patnayakuni, R., and Seth, N. 2006. “Firm Performance Impacts of Digitally Enabled Supply Chain Integration Capabilities,” MIS Quarterly (30:2), pp. 225-246. |
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H1: IT Infrastructure Integration for SCM => Supply Chain Process Integration (“IT infrastructure integration is conceptualized as a formative construct with two sub-constructs: data consistency and cross-functional SCM application systems integration.” p. 231) |
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H1a: Data consistency => IT Infrastructure Integration for SCM (“Data consistency in supply chains....should enable process integration including the integration of information, financial, and physical flows.” p. 231) |
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H1b: Cross-functional application integration => IT Infrastructure Integration for SCM (“We consider integration of applications for supply chain planning and execution, and their integration with ERP and CRM systems; together they characterize the applications infrastructure for end-to-end management of supply chains...Finally, integrated supply chain, ERP, and CRM applications should facilitate the coordination of supplier- and customer-facing processes with internal firm processes.” p. 232) |
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H2: Supply Chain Process Integration => Firm Performance (“We examine whether aggregate performance of a firm, as assessed by operations excellence, revenue growth, and customer relationships, is influenced by supply chain process integration.” p. 230), (“…supply chain process integration is conceptualized as a formative construct with three sub-constructs: information flow integration, physical flow integration, and financial flow integration…” p. 230) |
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H2a: Information flow integration => Supply Chain Process Integration (“By improving the precision of demand estimation through collaborative forecasting, and facilitating supply and demand alignment, information sharing can strengthen bonds with customers and generate increased revenues from existing products and new products and markets (Anderson et al. 1994; Mohr and Nevin 1990).” p. 230) |
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H2b: Physical flow integration => Supply Chain Process Integration (“By increasing responsiveness to customer demand through strategies such as postponement of differentiation (Feitzinger and Lee 1997) physical flow integration can improve customer relationships and customer service (Ellingeretal. 1999; Gustinetal. 1995). Finally, such integration is expected to improve long-term competitiveness and growth (Goldhar and Lei 1991).” p. 231) |
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H2c: Financial flow integration => Supply Chain Process Integration (“Financial flow integration can impact revenue growth by improving cash flow availability for production ramp-up when demand swings upward, or for exploration of new product lines.” p. 231) |
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✓ |
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H3a: Operational excellence => Firm performance |
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H3b: Customer excellence => Firm performance |
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H3c: Revenue excellence => Firm performance (“Operations excellence, revenue growth, and customer relationships, recognized as important dimensions of firm performance (Slywotzky et al. 2000), are three aspects of performance that we consider.” p. 229) |
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✓ |
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NOTE: The authors did not present their research hypotheses explicitly. The hypotheses presented here are derived from Figure 2 of their paper. |
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Ray, G., Muhanna, W. A., and Barney, J. B. 2005. “Information Technology and the Performance of the Customer Service Process: A Resource-Based Analysis,” MIS Quarterly (29:4), pp. 625-652. |
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H1a: Technical IT skills will not explain variance in the performance of the customer service process across firms in the North American insurance industry. |
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H1b: Generic information technologies used to support the customer service process will not explain variance in the performance of the customer service process across firms in the North American insurance industry. |
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H1c: The level of IT spending will not explain variance in the performance of the customer service process across firms in the North American insurance industry. |
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H2a: The level of shared knowledge will explain variance in the performance of the customer service process across firms in the North American insurance industry. |
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H2b: The flexibility of IT infrastructure will explain variance in the performance of the customer service process across firms in the North American insurance industry. |
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H3a: Technical IT skills, in the presence of a high level of shared knowledge, will explain variance in the performance of the customer service process across firms in the North American insurance industry. |
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H3b: Generic information technologies used to support the customer service process, in the presence of a high level of shared knowledge, will explain variance in the performance of the customer service process across firms in the North American insurance industry. |
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Salaway, G. 1987. “An Organizational learning Approach to Information Systems Development,” MIS Quarterly (11:2), pp. 245-265. |
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Traditional interactions display primarily: |
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H1: Model 1 causal thinking patterns => Current user/analyst causal thinking is predominantly Model 1 (In Model 1, problems and situations are most often identified as caused by another person and/or the external environment. There is little identification of how one's own behavior may be problematic or impact the situation in a negative way. Model 2 calls for intentional evaluation of how one's own thoughts and actions impact situations, so that one can generate new, more effective actions.) |
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H2: Model 1 strategic thinking patterns => Current user/analyst strategic thinking is predominantly Model 1 (Model 1 strategies identify what must happen in the environment or what one must do unilaterally to ensure that one's own goals are satisfied. In Model 2 strategic thinking, Attention is focused on what must happen to resolve goal incompatibilities and satisfy other’s as well as ones own goals.) |
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H3: Advocacy rather than inquiry patterns => Advocacy (to support publicly, defend a position, idea or action) characterizes most of the verbal action most people produce most of the time |
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H4: Model 1 advocacy patterns not including specifics or data => The form of Model 1 advocacy is to publicly state a position without giving data (something that can be seen, heard, or touched in a straightforward way), or specific examples that can be disconfirmed (H4) |
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H5: Model 1 advocacy patterns not including specifics, data or inquiry => The form of Model 1 advocacy is to publicly state a position without giving data (something that can be seen, heard, or touched in a straightforward way), or specific examples that can be disconfirmed and without inviting inquiry to test the validity of the position. |
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H6: Model 1 inquiry patterns (loaded and inferential inquiry) => A strategy of unilateral control implies that inquiry be presented in such a way that the answers are implied in the question so that they are more likely to agree with the predetermined answers in the mind of the inquirer. |
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H7: Model 2 error-detecting inquiry patterns that generate new information rather than disconfirm existing information => Where error-detecting inquiry is used, it will be used to gather information, rather than actually test existing information for errors. |
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H8: Model 1 confrontability patterns about negative impacts => In Model 1, confrontability is done by confronting another person’s actions, ideas or feelings. Confronting others by challenging the validity of their positions tends to elicit a defensive response and keep important information from being volunteered. |
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H9: Model 1 confrontability patterns about requests => Requesting information or action from another person can also be done in either a confronting or nonconfronting manner. A Model 1 approach makes demands of others. |
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H10: Model 1 discussability patterns. |
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H11: Topics discussed that are not interpersonal. |
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Quote from the paper: “A steady flow of thoughts, feelings, theories, assumptions, etc., exist in one's head and manifest in one's actions both directly and indirectly. In Model 1, the unilateral control strategy causes many such thoughts to be kept private (H10), especially those of an interpersonal nature (H11).” |
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H12: Percentage of Model 2 advocacy patterns (includes both data and inquiry) |
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H13: Percentage of advocacy patterns with specifics |
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H14: Percentage of advocacy patterns with data |
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Quote from the paper: “In Model 2, advocacy is backed up by directly observable data (H14) or at least a specific example (H13) that can be disconfirmed, as well as inquiry designed to test the advocacy (H12).” |
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H15: Percentage of inquiry patterns |
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H16: Percentage of Model 2 inquiry patterns (open ended, grounded in data, and testing inquiry) |
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H17: Percentage of Model 2 inquiry patterns for testing (testing inquiry) |
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Quote from the paper: “In Model 2, the use of inquiry is intentionally increased (H15) and the focus is on clear open inquiry to discover the meanings attributed to actions, inferences, assumptions and evaluations (H16), rather than to reinforce ideas already established (H17).” |
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H18: Percentage of Model 2 confrontability patterns about negative impact (make self confrontable) => One way of generating more valid information (although much more difficult) is to make oneself confrontable instead. This requires that one identify his or her own personal responsibility in the situation and discuss this publicly in order to create a less threatening environment for other people's responses. |
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H19: Percentage of Model 2 confrontability patterns about requests (express preferences, giving choice) => Requesting information or action from another person can also be done in either a confronting or nonconfronting manner. In a Model 2 approach, one expresses preferences and ensures others the opportunity to suggest alternatives and make choices. |
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H20: Percentage of interpersonal topics discussed => In Model 2, a conscious effort is made to identify such thoughts, feelings, assumptions, theories, etc. and state them publicly so they can be challenged by others. An increase in interpersonal topics should also follow (H20). This allows theories to be frequently added, updated, or deleted, and facilitates creative, lasting solutions to problems. |
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H21: Percentage of active confirmation patterns (confirming another person's advocacy giving data/specifics as proof) |
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H22: Percentage of active disconfirmation patterns (disconfirming another person's advocacy giving data/specifics as proof) |
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H23: Percentage of patterns directly continuing the topic just previously discussed |
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Quote from the paper: “Model 2 theories of action increase the occurrence of verbal patterns designed to continue addressing a relevant topic (H23) until ideas can be actively confirmed (H21) or disconfirmed (H22) so that errors can be surfaced and corrected before systems installation.” |
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H24: Percentage of advocacy patterns with specifics or data displayed by persons not trained in the organizational learning method => With more questions asked by users and analysts practicing Model 2, others should be encouraged to provide more data and specific examples in response to such inquiry |
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Sambamurthy, V., Bharadwaj, A., and Grover, V. 2003. “Shaping Firm Agility through Digital Options: Reconceptualizing the Role of IT in Contemporary Firms,” MIS Quarterly (27:2), pp. 237-263. |
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P1: The impact of IT competence on digital options will be positively moderated by the entrepreneurial alertness of the firm. |
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P2: The impact of digital options (i.e., process reach and richness and knowledge reach and richness) on agility will be positively moderated by entrepreneurial alertness. |
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P3: The impact of agility on the number of competitive actions and action repertoire complexity will be positively moderated by entrepreneurial alertness. |
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P4: Well-developed digital options (i.e., greater process reach and richness and greater knowledge reach and richness) will contribute to higher levels of IT competence. |
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P5: Well-developed digital options (i.e., greater process reach and richness and greater knowledge reach and richness) will contribute to higher levels of entrepreneurial alertness. |
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P6: Higher levels of agility will further enhance digital options. |
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P7: Higher levels of agility will further enhance entrepreneurial alertness. |
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P8: Greater number of competitive actions and action repertoire complexity will enhance agility. |
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P9: Greater number of competitive actions and action repertoire complexity will enhance entrepreneurial alertness. |
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Straub, D. and Welke, R. J. 1998. “Coping with Systems Risk: Security Planning Models for Management Decision Making,” MIS Quarterly (22:4), pp. 441-469. |
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P1: Managers are aware of only a fraction of the full spectrum of actions that can be taken to reduce systems risk. |
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P2: Managers exposed to theory-grounded security planning techniques will be inclined to employ these in their planning processes. |
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Subramani, M. 2004. “How Do Suppliers Benefit from Information Technology Use in Supply Chain Relationships,” MIS Quarterly (28:1), pp. 45-73. |
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H1: The higher the level of SCMS use for exploitation, the greater the level of business-process specificity in the exchange. |
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H2: The higher the level of SCMS use for exploration, the greater the level of domain-knowledge specificity in the exchange. |
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H3a: The association of IT Exploit [IT Exploitation] with business-process specificity is stronger than the association of IT Explore [IT Exploration] with business-process specificity. |
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H3b: The association of IT Explore with domain-knowledge specificity is stronger than the association of IT Exploit with domain-knowledge specificity. |
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H4 The higher the level of SCMS use for exploitation, the higher the levels of operational and strategic benefits achieved through the leverage of relationship-specific business processes. |
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H5: The higher the level of SCMS use for exploration, the higher the levels of operational and strategic benefits achieved through the leverage of relationship-specific domain knowledge. |
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H6: Higher levels of operational benefits in the exchange are associated with higher levels of competitive performance. |
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H7: Higher levels of strategic benefits in the exchange are associated with higher levels of competitive performance. |
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Sussman, S. W., and Siegal, W. S. 2003. “Informational Influence in Organizations: An Integrated Approach to Knowledge Adoption,” Information Systems Research (14:1), pp. 47-65. |
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H1: The higher the perceived argument quality of a message, the more useful the message will be perceived to be. |
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H2: The higher the perceived credibility of a message source, the more useful the message will be perceived to be. |
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H3: Messages perceived to contain information of high usefulness will be associated with higher levels of information adoption than messages perceived to contain information of low usefulness. |
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H4: Usefulness will mediate the effects of source credibility on information adoption, and argument quality on information adoption. |
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H5a: The greater the recipient’s expertise in the topic of the message, the more argument quality affects perceived message usefulness. |
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H5b: The greater the recipient’s expertise in the topic of the message, the less source credibility factors affect perceived message usefulness. |
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H6a: The greater the recipient’s involvement in the topic of the message, the more argument quality affects perceived message usefulness. |
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H6b: The greater the recipient’s involvement in the topic of the message, the less source credibility factors affect perceived message usefulness. |
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Tanriverdi, H. 2005. “Information Technology Relatedness, Knowledge Management Capability, and Performance of Multibusiness Firms,” MIS Quarterly (29: 2), pp. 311-334. |
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H1: Complementarity of product KM capability, customer KM capability, and managerial KM capability has a positive effect on corporate financial performance of a multi-business firm. |
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✓ |
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H2: Complementarity of the four dimensions of IT relatedness is positively associated with cross-unit KM capability of a multi-business firm. |
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Te’eni, D. 2001. “Review: A Cognitive-affective Model of Organizational Communication for Designing IT,” MIS Quarterly (25:2), pp. 251-312. |
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P1: Beyond some minimal value of communication complexity, a reduction in it will result in higher levels of mutual understanding and relationship. |
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P2a: Contextualization is selected for communication goals characterized by high cognitive complexity. |
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P2b: Affectivity is selected for communication goals characterized by high affective complexity. |
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P2c: Control by testing and adjusting is selected for communication goals characterized by high cognitive complexity coupled with high dynamic complexity. |
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P2d: Control by planning is selected for communication goals characterized by high cognitive complexity coupled with low dynamic complexity. |
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Corollary: Contextualization is selected over control when mutual understanding decreases. |
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P3a: For control by testing and adjusting, high, rather than low, interactivity is more effective. |
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P3b: For contextualization, high, rather than low, channel capacity is more effective. |
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P3c: For affectivity, high, rather than low, channel capacity is more effective. |
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P3d: For perspective taking, high, rather than low, adaptiveness is more effective. |
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P4a: Senders will adapt to low channel capacity coupled with high interactivity by increasing control through testing and adjusting. |
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P4b: Senders will adapt to low channel capacity coupled with low interactivity by increasing control through planning. |
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P4c: Senders will adapt to low channel capacity coupled with low interactivity by decreasing affectivity. |
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P4d: Senders will adapt to low channel capacity coupled with high interactivity by increasing affectivity. |
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P5a: For affectivity, a small, rather than wide, distribution is more effective. |
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P5b: For contextualization, high, rather than low, message organization is more effective. |
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P5c: For control by planning, high, rather than low, message organization is more effective. |
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P5d: For affectivity, low, rather than high, formality is more effective. |
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P5e: For control, high, rather than low, formality is usually more effective. |
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P5f: For contextualization, low, rather than high, formality is usually more effective. |
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P6a: Senders will adapt to a long message by increasing attention focusing. |
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P6b: Senders will adapt to low message organization by increasing control through testing and adjusting, provided media interactivity is high. |
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P7a: When interactivity is high, senders will exchange shorter, rather than longer, messages. |
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P7b: When channel capacity is low, senders will exchange messages of higher, rather than lower, formality. |
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P8a: Higher task variety increases the frequency of requesting information. |
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P8b: Lower task analyzability increases the use of contextualization. |
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P8c: A short time-to-complete the task increases the use of control by testing and adjusting. |
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P8d: A very long time-to-complete the task increases the use of control by planning. |
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P9a: Greater cognitive distance increases the use of contextualization. |
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P9b: Greater cognitive distance increases the frequency of requesting information. |
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P9c: Greater affective distance reduces the frequency of requesting information. |
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P10a: Individuals in interdependent cultures tend to engage in more frequent communication for managing relationships, provided the affective and cognitive distance does not exceed some threshold. |
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P10b: Individuals in interdependent cultures tend to use more perspective taking. |
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Teo, H. H., Wei, K. K., and Benbasat, I. 2003. “Predicting Intention to Adopt Interorganizational Linkages: An Institutional Perspective,” MIS Quarterly (27:1), pp. 19-49. |
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H1: Greater mimetic pressures will lead to greater intent to adopt FEDI [financial electronic data interchange]. |
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H1a: Greater extent of adoption of FEDI among its competitors will lead to greater intent to adopt FEDI. |
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H1b: Greater perceived success of competitors that have adopted FEDI will lead to greater intent to adopt FEDI. |
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H2: Greater coercive pressures will lead to greater intent to adopt FEDI. |
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H2a: Greater perceived dominance of its suppliers that have adopted FEDI will lead to greater intent to adopt FEDI. |
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H2b: Greater perceived dominance of its customers that have adopted FEDI will lead to greater intent to adopt FEDI. |
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H2c: Adoption of FEDI by parent corporation will lead to greater intent to adopt FEDI. |
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H3: Greater normative pressures will lead to greater intent to adopt FEDI. |
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H3a: Greater extent of adoption of FEDI among its suppliers will lead to greater intent to adopt. |
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H3b: Greater extent of adoption of FEDI among its customers will lead to greater intent to adopt. |
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H3c: Participation in associations that promote and disseminate information on FEDI will lead to greater intent to adopt FEDI. |
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H4: Mimetic pressures will have a more significant impact on intention to adopt FEDI when perceived complexity is higher than when it is lower. |
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Vance, A., Lowry, P. B., Eggett, D. 2015. “A New Approach to the Problem of Access Policy Violations: Increasing Perceptions of Accountability Through the User Interface,” MIS Quarterly (39:2), pp. 345–366. |
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H1: UI artifacts that manipulate identifiability will increase accountability. |
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H2: UI artifacts that manipulate expectation of evaluation will increase accountability. |
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H3: UI artifacts that manipulate awareness of monitoring will increase accountability. |
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H4: UI artifacts that manipulate social presence will increase accountability. |
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H5: UI artifacts that manipulate accountability mechanisms will increase accountability. |
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H6: Perceived accountability decreases intention to commit access-policy violations. |
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H7: Accountability will mediate the effects of UI artifacts for (1) identifiability, (2) expectation of evaluation, (3) awareness of monitoring, and (4) social presence on intention to commit access-policy violations. |
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van der Heijden, H. 2004. “User Acceptance of Hedonic Information Systems,” MIS Quarterly (28:4), pp. 695-704 |
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H1: For hedonic systems, perceived enjoyment is a stronger predicator of behavioral intention to use than perceived usefulness. |
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H2: For hedonic systems, perceived ease of use is a stronger predicator of behavioral intention to use than perceived usefulness. |
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✓ |
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Venkatesh, V. and Morris, M. G. 2000. “Why Don't Men ever Stop to Ask for Directions? Gender, Social Influence, and their Role in Technology Acceptance and Usage Behavior,” MIS Quarterly (24:1), pp. 115-139. |
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H1: Perceived usefulness will influence behavioral intention to use a system more strongly for men than it will influence women. |
✓ |
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H2a: Perceived ease of use will influence behavioral intention to use a system more strongly for women than it will influence men. |
✓ |
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H2b: Perceived ease of use will influence perceived usefulness more strongly for men than it will influence women. |
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H3: Subjective norm will influence behavioral intention to use a system more strongly for women than it will influence men. |
✓ |
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H4: With increasing direct experience with the technology, perceived usefulness will influence behavioral intention to use a system more strongly for men than it will influence women. |
✓ |
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H5a: With increasing direct experience with the technology, perceived ease of use will influence behavioral intention to use a system more strongly for women than it will influence men. |
✓ |
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H5b: With increasing direct experience with the technology, perceived ease of use will influence perceived usefulness more strongly for men than it will influence women. |
✓ |
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H6: With increasing direct experience with the technology, subjective norm will not influence behavioral intention to use a system for either women or men. |
✓ |
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Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. 2003. “User Acceptance of Information Technology: Toward a Unified View,” MIS Quarterly (27:3), pp. 425-478. |
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H1. The influence of performance expectancy on behavioral intention will be moderated by gender and age, such that the effect will be stronger for men and particularly for younger men. |
✓ |
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H2.The influence of effort expectancy on behavioral intention will be moderated by gender, age, and experience, such that the effect will be stronger for women, particularly younger women, and particularly at early stages of experience. |
✓ |
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H3. The influence of social influence on behavioral intention will be moderated by gender, age, voluntariness, and experience, such that the effect will be stronger for women, particularly older women, particularly in mandatory settings in the early stages of experience. |
✓ |
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H4a. Facilitating conditions will not have a significant influence on behavioral intention. |
✓ |
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H4b. The influence of facilitating conditions on usage will be moderated by age and experience, such that the effect will be stronger for older workers, particularly will increasing experience. |
✓ |
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H5a. Computer self-efficacy will not have a significant influence on behavioral intention. |
✓ |
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H5b: Compute anxiety will not have a significant influence on behavioral intention. |
✓ |
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H5c: Attitude toward using technology will not have a significant influence on behavioral intention. |
✓ |
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H6. Behavioral intention will have a significant positive influence on usage. |
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✓ |
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Vessey, I., and Galleta, D. 1991. “Cognitive Fit: An Empirical Study of Information Acquisition,” Information Systems Research (2:1), pp. 63-84. |
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P1: More effective and efficient problem solving results when the problem representation matches the task to be accomplished. |
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✓ |
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P2: Problem-solving performance is improved when problem-solving skills match both problem representation and task, or either the problem representation or the task. Problem-solving performance is improved more if the skills match both the problem representation and the task, rather than matching only the problem representation or the task. |
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✓ |
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P3: Problem solvers formulate a mental representation of the problem consistent with the type of information in the first problem-solving element examined. |
✓ |
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P4: The problem representation has a greater effect than task type on the mental representation formulated. |
✓ |
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P5: Symbolic problem-solving elements (problem representation and task) have a greater effect on the mental representation formulated than spatial problem-solving elements. |
✓ |
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Wasko, M. M., and Faraj, S. 2005. “Why Should I Share? Examining Social Capital and Knowledge Contribution in Electronic Networks of Practice,” MIS Quarterly (20:1), pp. 35-57. |
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H1a: Individuals who perceive that participation will enhance their reparations in the profession will contribute more helpful responses to electronic networks of practice. |
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✓ |
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H1b: Individuals who perceive that participation will enhance their reputations in the profession will contribute more responses to electronic networks of practice. |
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✓ |
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H2a: Individuals who enjoy helping others will contribute more helpful responses to electronic networks of practice. |
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✓ |
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H2b: Individuals who enjoy helping others will contribute more responses to electronic networks of practice. |
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✓ |
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H3a: Individuals with higher levels of network centrality will contribute more helpful responses to electronic networks of practice. |
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✓ |
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H3b: Individuals with higher levels of network centrality will contribute more responses to electronic networks of practice. |
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✓ |
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H4a: Individuals with higher levels of expertise in the shared practice will contribute more helpful practice will contribute more helpful responses to electronic networks of practice. |
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✓ |
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H4b: Individuals with higher levels of expertise in the shared practice will contribute more responses to electronic networks of practice. |
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✓ |
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H5a: Individuals with longer tenure in the shared practice will contribute more helpful responses to electronic networks of practice. |
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✓ |
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H5b: Individuals with longer tenure in the shared practice will contribute more responses to electronic networks of practice. |
✓ |
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H6a: Individuals who are committed to the network will contribute more helpful responses to electronic networks of practice. |
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✓ |
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H6b: Individuals who are committed to the network will contribute more responses to electronic networks of practice. |
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✓ |
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H7a: Individuals guided by a norm by reciprocity will contribute more helpful responses to electronic networks of practice. |
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✓ |
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H7b: Individuals guided by a norm of reciprocity will contribute more responses to electronic networks of practice. |
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✓ |
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Watson, R. T. 1990. “Influences on the IS Manager’s Perceptions of Key Issues Information Scanning and the Relationship with the CEO,” MIS Quarterly (14:2), pp. 217-231. |
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P1: The scanning behavior of an IS manager will influence his or her judgment of key issues. |
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✓ |
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P2: The relationship of an IS manager to the CEO will influence the IS manager's judgment of key issues. |
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Weill, P. 1992. “The Relationship between Investment in Information Technology and Firm Performance: a Study of the Valve Manufacturing Sector,” Information Systems Research (3:4), pp. 307-333. |
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H1: There is no association between previous years' total investment in IT and the firm's incremental performance. |
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H2: There is a positive association between previous years’ investment in IT, categorized by management objective, and the firm's incremental performance. Transactional IT is posited to affect profitability and labor productivity of the firm. Strategic and informational IT are posited to affect the growth and profit aspects of firm performance, respectively. |
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✓ |
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✓ |
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H3: There is a positive association between incremental IT investment, categorized by management objective, and each measure of previous year's firm performance. |
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H4: Conversion effectiveness moderates any relationship between IT investment, categorized by management objective, and incremental firm performance. |
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Wixom, B. H. and Todd, P. A. 2005. “A Theoretical Integration of User Satisfaction and Technology Acceptance,” Information Systems Research (16:1), pp. 85-102. |
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H1a: Information quality is affected by completeness. |
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✓ |
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H1b: Information quality is affected by accuracy. |
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✓ |
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H1c: Information quality is affected by format. |
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✓ |
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H1d: Information quality is affected by currency. |
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✓ |
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H2a: System quality is affected by reliability. |
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✓ |
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H2b: System quality is affected by flexibility. |
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✓ |
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H2c: System quality is affected by integration. |
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✓ |
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H2d: System quality is affected by accessibility. |
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✓ |
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H2e: System quality is affected by timeliness. |
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✓ |
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H3: Information quality has a positive effect on information satisfaction. |
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✓ |
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H4: System quality has a positive effect on system satisfaction. |
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✓ |
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H5: System satisfaction has a positive relationship with information satisfaction. |
✓ |
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H6: Information satisfaction increases perceptions of usefulness. |
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✓ |
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H7: System satisfaction has a positive effect on ease of use. |
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✓ |
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H8: Ease of use has a positive relationship with perception of usefulness. |
✓ |
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H9: Usefulness forms a positive attitude to toward using. |
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✓ |
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H10: Usefulness increases intention to adopt. |
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✓ |
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H11: Ease of use forms a positive attitude toward using. |
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✓ |
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H12: Attitude toward using has a positive relationship with intention to adopt. |
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✓ |
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NOTE: The authors did not present their research hypotheses explicitly. The hypotheses presented here are derived from Figure 3 in page 90 of their paper. |
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Wixom, B. H., and Watson, H. J. 2001. “An Empirical Investigation of the Factors Affecting Data Warehousing Success,” MIS Quarterly (25:1), pp. 17-38. |
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H1a: A higher level of data quality is associated with a high level of perceived net benefits. |
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✓ |
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✓ |
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H1b: A high level of system quality is associated with a high level of perceived net benefits. |
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✓ |
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✓ |
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H2a: A high level of organizational implementation success is associated with a high level of data quality. |
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✓ |
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H2b: A high level of organizational implementation success is associated with a high level of system quality. |
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H3a: A high level of project implementation success is associated with a high level of data quality. |
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H3b: A high level of project implementation success is associated with a high level of system quality. |
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H4a: A high level of technical implementation success is associated with a high level of data quality. |
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✓ |
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H4b: A high level of technical implementation success is associated with a high level of system quality. |
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✓ |
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H5: A high level of management support is associated with a high level of organizational implementation success. |
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✓ |
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H6a: A strong champion presence is associated with a high level of organizational implementation success. |
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✓ |
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H6b: A strong champion presence is associated with a high level of project implementation success. |
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✓ |
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H7a: A high level of resources is associated with a high level of organizational implementation success. |
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H7b: A high level of resources is associated with a high level of project implementation success. |
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H8a: A high level of user participation is associated with organizational implementation success. |
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✓ |
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H8b: A high level of user participation is associated with project implementation success. |
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✓ |
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H9a: A high level of team skills is associated with project implementation success. |
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✓ |
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✓ |
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H9b: A high level of team skills is associated with technical implementation success. |
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✓ |
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✓ |
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H10: High-quality source systems are associated with technical implementation success. |
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✓ |
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✓ |
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H11: Better development technology is associated with technical implementation success. |
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✓ |
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Yi, M. Y., and Davis, F. D. 2003. “Developing and Validating an Observational Learning Model of Computer Software Training and Skill Acquisition,” Information Systems Research (14:2), pp. 146-169. |
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H1: Declarative knowledge will positively influence immediate task performance. |
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✓ |
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H2: Post-training software self-efficacy will positively influence immediate task performance. |
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✓ |
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H3: Immediate task performance will positively influence delayed task performance. |
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✓ |
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H4: Declarative knowledge will positively influence delayed task performance. |
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✓ |
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H5: Post-training software self-efficacy will positively influence delayed task performance. |
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✓ |
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H6: Observational learning processes will positively influence declarative knowledge. |
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✓ |
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H7: Observational learning processes will positively influence post-training software self-efficacy. |
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✓ |
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H8: The retention enhancement intervention will positively influence the retention processes, but not the attention, production, or motivation processes, of observational learning. |
✓ |
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H9: Pre-training motivation to learn will positively influence the attention, retention, production, and motivation processes of observational learning. |
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✓ |
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H10: Pre-training software self-efficacy will positively influence post-training software self-efficacy. |
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✓ |
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Zhu, K., and Kraemer, K.L. 2005. "Post-Adoption Variations in Usage and Value of E-Business by Organizations: Cross-Country Evidence from the Retail Industry," Information Systems Research (16:1), pp. 61-84. |
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H1: Firms with greater technology competence are more likely to achieve a greater extent of e-business use. |
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✓ |
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✓ |
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H2: Controlling for resource availability effects, larger firms tend to achieve a lesser extent of e-business use. |
✓ |
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H3: Firms with greater international scope are more likely to achieve a greater extent of e-business use. |
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✓ |
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✓ |
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H4: Firms with greater financial commitment are more likely to achieve a greater extent of e-business use. |
✓ |
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H5: Firms facing higher competitive pressure are more likely to achieve a greater extent of e-business use. |
✓ |
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H6: Firms facing higher regulatory support are more likely to achieve a greater extent of e-business use. |
✓ |
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H7: Firms with greater e-business use are more likely to generate higher e-business value. |
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✓ |
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✓ |
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H8: Greater e-business capabilities, including both front-end functionality and back-end integration, are positively associated with higher e-business value. |
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✓ |
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✓ |
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H9: Back-end integration will have a stronger impact on e-business value than front-end functionality. |
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✓ |
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H10: The strength of the antecedents of e-business use and value will differ for developed and developing countries. |
✓ |
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Zigurs, I., and Buckland, B. K. 1998. “A Theory of Task/Technology Fit and Group Support Systems Effectiveness,” MIS Quarterly (22:3), pp. 313-334. |
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P1: Simple tasks should result in the best group performance (as defined for the specific task) when done using a GSS configuration that emphasizes communication support. |
✓ |
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P2: Problem tasks should result in the best group performance (as defined for the specific task) when done using a GSS configuration that emphasizes information processing. |
✓ |
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P3: Decision tasks should result in the best group performance (as defined for the specific task) when done using a GSS configuration that emphasizes information processing and process structuring. |
✓ |
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P4: Judgment tasks should result in the best group performance (as defined for the specific task) when done using a GSS configuration that emphasizes communication support and information processing. |
✓ |
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P5: Fuzzy tasks should result in the best group performance (as defined for the specific task) when done using a GSS configuration that emphasizes communication support and information processing, and includes some process structuring. |
✓ |
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