Knowledge that Transforms

User resistance to information systems implementation has been identified as a salient reason for the failure of new systems and hence needs to be understood and managed. While previous research has explored the reasons for user resistance, there are gaps in our understanding of how users evaluate change related to a new information system and decide to resist it. In particular, missing in the explanation of user decision making is the concept of status quo bias, that is, that user resistance can be due to the bias or preference to stay with the current situation. Motivated thus, this study develops a model to explain user resistance prior to a new IS implementation by integrating the technology acceptance and resistance literatures with the status quo bias perspective. The results of testing the model in the context of a new enterprise system implementation indicate the central role of switching costs in increasing user resistance. Further, switching costs also mediate the relationship between other antecedents (colleague opinion and self-efficacy for change) and user resistance. Additionally, perceived value and organizational support for change are found to reduce user resistance. This research advances the theoretical understanding of user acceptance and resistance prior to a new IS implementation and offers organizations suggestions for managing such resistance.

In striving to learn about an information technology innovation, organizations draw on knowledge resources available in the community of diverse interests that convenes around that innovation. But even as such organizations learn about the innovation, so too does the larger community. Community learning takes place as its members reflect upon their learning and contribute their experiences, observations, and insights to the community’s on-going discourse on the innovation. Community learning and organizational learning thus build upon one another in a reciprocal cycle over time, as the stock of interpretations, adoption rationales, implementation strategies, and utilization patterns is expanded and refined. We advance an overall model of this learning cycle, drawing on two community-level theories (management fashion and organizing vision), both of which complement the dominant emphases of the literature on IT innovation and learning. Relative to this cycle, we then empirically examine, in particular, the dependence of community learning on organizational learning. Sampling the public discourse on enterprise resource planning (ERP) over a 14-year period, we explore how different kinds of organizational actors can play different roles, at different times, in contributing different types of knowledge to an innovation’s public discourse. The evidence suggests that research analysts and technology vendors took leadership early on in articulating the “know-what” (interpretation) and “know-why” (rationales) for ERP, while later on adopters came to dominate the discourse as its focus shifted to the “know-how” (strategies and capabilities). We conclude by identifying opportunities for further inquiry on and strategic management of community learning and its interactions with organizational learning.

Within the Information Systems literature, there has been an emerging interest in the use of formative measurement in structural equation modeling (SEM). This interest is exemplified by descriptions of the nature of formative measurement (e.g., Chin 1998a), and more recently the proper specification of formatively measured constructs (Petter et al. 2007) as well as application of such constructs (e.g., Barki et al. 2007). Formative measurement is a useful alternative to reflective measurement. However, there has been little guidance on interpreting the results when formative measures are employed. Our goal is to provide guidance relevant to the interpretation of formative measurement results through the examination of the following six issues: multicollinearity; the number of indicators specified for a formatively measured construct; the possible co-occurrence of negative and positive indicator weights; the absolute versus relative contributions made by a formative indicator; nomological network effects; and the possible effects of using partial least squares (PLS) versus covariance-based SEM techniques. We provide prescriptions for researchers to consider when interpreting the results of formative measures as well as an example to illustrate these prescriptions.

Within the emerging context of the digitization of health care, electronic health records (EHRs) constitute a significant technological advance in the way medical information is stored, communicated, and processed by the multiple parties involved in health care delivery. However, in spite of the anticipated value potential of this technology, there is widespread concern that consumer privacy issues may impede its diffusion. In this study, we pose the question: Can individuals be persuaded to change their attitudes and opt-in behavioral intentions toward EHRs, and allow their medical information to be digitized even in the presence of significant privacy concerns? To investigate this question, we integrate an individual’s concern for information privacy (CFIP) with the elaboration likelihood model (ELM) to examine attitude change and likelihood of opting-in to an EHR system. We theorize that issue involvement and argument framing interact to influence attitude change, and that concern for information privacy further moderates the effects of these variables. We also propose that likelihood of adoption is driven by concern for information privacy and attitude. We test our predictions using an experiment with 366 subjects where we manipulate the framing of the arguments supporting EHRs. We find that an individual’s CFIP interacts with argument framing and issue involvement to affect attitudes toward the use of EHRs. In addition, results suggest that attitude toward EHR use and CFIP directly influence opt-in behavioral intentions. An important finding for both theory and practice is that even when people have high concerns for privacy, their attitudes can be positively altered with appropriate message framing. These results as well as other theoretical and practical implications are discussed.

Prior research has extensively studied individual adoption and use of information systems, primarily using beliefs as predictors of behavioral intention to use a system that in turn predicts system use. We propose a model of acceptance with peer support (MAPS) that integrates prior individual-level research with social networks constructs. We argue that an individual’s embeddedness in the social network of the organizational unit implementing a new information system can enhance our understanding of technology use. An individual’s coworkers can be important sources of help in overcoming knowledge barriers constraining use of a complex system, and such interactions with others can determine an employee’s ability to influence eventual system configuration and features. We incorporate network density (reflecting “get-help” ties for an employee) and network centrality (reflecting “give-help” ties for an employee), drawn from prior social network research, as key predictors of system use. Further, we conceptualize valued network density and valued network centrality, both of which take into account ties to those with relevant system-related information, knowledge, and resources, and employ them as additional predictors. We suggest that these constructs together are coping and influencing pathways by which they have an effect on system use. We conducted a 3-month long study of 87 employees in one business unit in an organization. The results confirmed our theory that social network constructs can significantly enhance our understanding of system use over and above predictors from prior individual-level adoption research.

This paper presents a meta-analysis-based technique to estimate the effect of common method variance on the validity of individual theories. The technique explains between-study variance in observed correlations as a function of the susceptibility to common method variance of the methods employed in individual studies. The technique extends to mono-method studies the concept of method variability underpinning the classic multitrait–multimethod technique. The application of the technique is demonstrated by analyzing the effect of common method variance on the observed correlations between perceived usefulness and usage in the technology acceptance model literature. Implications of the technique and the findings for future research are discussed.

In this paper, the authors show that PLS path modeling can be used to assess a hierarchical construct model. They provide guidelines outlining four key steps to construct a hierarchical construct model using PLS path modeling. This approach is illustrated empirically using a reflective, fourth-order latent variable model of online experiential value in the context of online book and CD retailing. Moreover, the guidelines for the use of PLS path modeling to estimate parameters in a hierarchical construct model are extended beyond the scope of the empirical illustration. The findings of the empirical illustration are used to discuss the use of covariance-based SEM versus PLS path modeling. The authors conclude with the limitations of their study and suggestions for future research.

This paper describes the development of the technology threat avoidance theory (TTAT), which explains individual IT users’ behavior of avoiding the threat of malicious information technologies. We articulate that avoidance and adoption are two qualitatively different phenomena and contend that technology acceptance theories provide a valuable, but incomplete, understanding of users’ IT threat avoidance behavior. Drawing from cybernetic theory and coping theory, TTAT delineates the avoidance behavior as a dynamic positive feedback loop in which users go through two cognitive processes, threat appraisal and coping appraisal, to decide how to cope with IT threats. In the threat appraisal, users will perceive an IT threat if they believe that they are susceptible to malicious IT and that the negative consequences are severe. The threat perception leads to coping appraisal, in which users assess the degree to which the IT threat can be avoided by taking safeguarding measures based on perceived effectiveness and costs of the safeguarding measure and self-efficacy of taking the safeguarding measure. TTAT posits that users are motivated to avoid malicious IT when they perceive a threat and believe that the threat is avoidable by taking safeguarding measures; if users believe that the threat cannot be fully avoided by taking safeguarding measures, they would engage in emotion-focused coping. Integrating process theory and variance theory, TTAT enhances our understanding of human behavior under IT threats and makes an important contribution to IT security research and practice.

A number of models and theories in information systems research include concepts of a match between two variables or states. The development of measures for this concept can present problems, because decisions must be made about the nature of the comparison. Should indirect measures of the match be employed, then methodological issues arise about how to best handle the measure when testing the model. Difference scores are commonly used to measure a match between variables or states in IS research, but these have implicit assumptions about the theory and data characteristics that are often false. Not unexpectedly, false assumptions can lead to erroneous conclusions about the relationships among the variables that are used to determine a match in a research model. The implicit assumptions restrict the form of the relationships and limit the IS researcher’s ability to understand the possible interplay among theoretical concepts. We suggest some guidelines for the formation and testing of models that measure the match. In addition, we recommend polynomial regression analysis as one means of analyzing the more complex relationships in IS studies. We then use an IS service quality example to illustrate the issues involved in the use of matching variables and make suggestions with regard to using or avoiding difference scores.

In the past few years, we have witnessed the increasing ubiquity of user-generated content on seller reputation and product condition in Internet-based used-good markets. Recent theoretical models of trading and sorting in used-good markets provide testable predictions to use to examine the presence of adverse selection and trade patterns in such dynamic markets. A key aspect of such empirical analyses is to distinguish between product-level uncertainty and seller-level uncertainty, an aspect the extant literature has largely ignored. Based on a unique, 5-month panel data set of user-generated content on used good quality and seller reputation feedback collected from Amazon, this paper examines trade patterns in online used-good markets across four product categories (PDAs, digital cameras, audio players, and laptops). Drawing on two different empirical tests and using content analysis to mine the textual feedback of seller reputations, the paper provides evidence that adverse selection continues to exist in online markets. First, it is shown that after controlling for price and other product, and for seller-related factors, higher quality goods take a longer time to sell compared to lower quality goods. Second, this result also holds when the relationship between sellers’ reputation scores and time to sell is examined. Third, it is shown that price declines are larger for more unreliable products, and that products with higher levels of intrinsic unreliability exhibit a more negative relationship between price decline and volume of used good trade. Together, our findings suggest that despite the presence of signaling mechanisms such as reputation feedback and product condition disclosures, the information asymmetry problem between buyers and sellers persists in online markets due to both product-based and seller-based information uncertainty. No consistent evidence of substitution or complementarity effects between product-based and seller-level uncertainty are found. Implications for research and practice are discussed.