MY PAPERS

Prior work on leadership in information systems development (ISD) teams has assumed that all developers are treated equally by their team leader and ignored the possibility that differentiated leader-member exchange (LMX) may be an important instrument for team leaders to influence self-organizing, agile ISD teams. We conducted a concurrent mixed methods inquiry to understand how LMX differentiation is associated with developer satisfaction in agile ISD teams and through which team processes agile ISD teams address LMX differentiation. We ran a multilevel, multistage survey of 1,894 software developers in 217 teams and an embedded case study of five ISD teams drawing on qualitative data from 40 interviews of developers and team leaders. Two focus groups (one with 10 developers and one with 10 team leaders) helped to substantiate the meta-inferences from the quantitative and qualitative studies. The results showed that LMX differentiation was positively associated with developer satisfaction, especially in teams with high-quality team-member exchange (TMX). We identify three team processes (i.e., collectivization of resources, visible appreciation of privileges, and freeing up leader capacities) that are enacted through agile ISD practices and allow ISD teams to leverage benefits from LMX differentiation for all their members.

Artificial intelligence (AI) is becoming a critical engine that powers a range of technology solutions. It is rapidly playing a vital role in supply chain and operations management. In this paper, we present a research agenda of how researchers and practitioners alike can study the potential adoption of AI-powered tools for benefits in the supply chain. We draw on the unified theory of acceptance and use of technology and suggest directions for research that leverage a mixed-methods research approach. Because these technologies are in a nascent stage and evolving rapidly, a mixed-methods approach will allow for a careful examination of potential features and how their adoption can be fostered, with specific benefits in mind. We present three-specific research directions rooted in the developmental, completeness, and expansion purposes of mixed-methods research.

Achieving the promised benefits of a new technology is closely tied to its sustained use. The dominant approach has been to predict use based on behavioral intention. Central to this approach is the assumption that use comes from conscious decision making, resulting from “thinking”, “reflecting”, and “cognition”. However, related work has shown that, over time and with increasing experience, use becomes habitual (routine, automatic) and when this happens, it is not subject to conscious decision making. This paper extends the technology adoption and use literature by testing the relative effects of intention and habit as determinants of use. We conducted a longitudinal field study over the period of one year, with 4 points of measurement in 7 organizations among 1235 users, to examine the effects of intention and habit from the formative stage of experience with a new technology through to an established, stable stage. The results provided strong support for habit as a predictor of use, especially over time, as use became well-rehearsed, and habit was a stronger determinant of use than intention was. In fact, habit dominates intention as a predictor of use, as experience increases. We discuss key implications for research and practice.

Employees' nonwork use of information technology (IT), or cyberslacking, is of growing concern due to its erosion of job performance and other negative organizational consequences. Research on cyberslacking antecedents has drawn on diverse theoretical perspectives, resulting in a lack of cohesive explanation of cyberslacking. Further, prior studies generally overlooked IT-specific variables. To address the cyberslacking problems in organizations and research gaps in the literature, we used a combination of a literature-based approach and a qualitative inquiry to develop a model of cyberslacking that includes a 2x2 typology of antecedents. The proposed model was tested and supported in a three-wave field study of 395 employees in a Fortune-100 US organization. For research, this work organizes antecedents from diverse research streams and validates their relative impact on cyberslacking, thus providing a cohesive theoretical explanation of cyberslacking. This work also incorporates contextualization (i.e., IT-specific factors) into theory development and enriches IS literature by examining the nonwork aspects of IT use and their negative consequences to organizations. For practice, the results provide practitioners with insights into nonwork use of IT in organizations, particularly on how they can take organizational action to mitigate cyberslacking and maintain employee productivity.

The sales and use of electric vehicles (EVs) have been growing in recent times in Europe, with the hopes of mitigating CO2 emissions and enabling more sustainable transportation. Considering the growth of the EV market, the main goal of this research is to shed light on what drives electric vehicles’ satisfaction and continuance intention. We collected data from 290 EV drivers in Europe. Grounded in the task-technology fit model and expectation-confirmation theory, we explained 22% and 40% of the variance in EV satisfaction and continuance intention, with green self-identity as a moderator. EV satisfaction and continuance intention are primarily driven by adequate infrastructure and environmental concerns.

The use of multilevel analysis has steadily increased in information systems (IS) research. Many studies are doing an admirable job of integrating two-level models into their examination of IS phenomena. However, two-level models are limited in how well they enable researchers to (1) more explicitly incorporate context into theory development and testing and (2) bridge the existing gap between micro- and macro-level research by focusing on intervening mechanisms that link hierarchically distal levels of analysis. Three-level models have emerged as a potential way to address these limitations. While literature has clearly outlined the mechanics of how to estimate three-level models, there is very little, if any, guidance on when and how to integrate the use of such models with theory development. Consequently, IS researchers have little guidance to inform their decisions about integrating the use of three-level models with their theory development and testing. In this article, we identify the circumstances under which IS researchers should consider the use of three-level models, develop guidelines about how to map the use of three-level model estimation to the theoretical objectives, and provide an illustration of how to implement the guidelines.

Novel information technologies (ITs), such as mobile devices or third-party cloud services, offer users an increasing variety of action possibilities, i.e., affordances. Organizational IT policies, however, often specify their actualization, i.e., turning those affordances into action, as undesired. Organizations face the challenge that their employees, to reach their goals, still frequently take advantage of those affordances by using those very ITs and thereby deviate from the IT policies. Although prior work has extensively studied how goal-oriented users actualize affordances that are associated with outcomes that support organizational goals, little attention has been paid to the structures, mechanisms, and conditions underlying affordances that deviate from organizational IT policies. We conceptualize those affordances as deviant affordances. Leveraging the orders of change framework and using a multimethod research design integrating interview and experimental studies, we identify three key mechanisms underlying deviant affordances�??i.e., tension, deadlock, and actualization mechanisms�??that can link together to produce a deviant outcome supporting the individual goal and an organizational goal. Our work explains the importance of users�?? perceived deadlock in stimulating the generation of deviant outcomes that support the organizational goals through improving task, contextual, and innovative job performance.

Accelerated technological innovations have led to shorter product life cycles. Yet, consumers often decide not to discard the incumbent technology in favor of a new technology. To explain this decision-making process, a rich research stream has investigated subconscious motivations. However, there is little understanding of the role of conscious motivations and, more importantly, their interplay with subconscious motivations in their decisions. Using the value appreciation perspective, we controlled for the predictors of the second version of the unified theory of acceptance and use of technology (UTAUT2) and leverage prospect theory to enrich our understanding of users continuance intentions. We conceptualize status quo preference with two new constructs�??namely, the value of status quo and users�?? commitment to status quo�??and integrate them with other known predictors of continuance intentions (related to an incumbent system)�??i.e., trust and habit. We empirically test our model in two studies. Study 1 was conducted among 2,096 users of smartphones in Hong Kong. Study 2 reports longitudinal data, across three waves of data collection over a period of six months, from 240 analysts introduced to a new technology to support their work in a large financial services organization. The findings support our model and suggest that (1) users�?? status quo preferences significantly determine continuance intentions; and (2) trust and habit have an effect on users�?? status quo preferences. These findings advance knowledge on continuance intentions by integrating the effects of users�?? conscious and subconscious intentions to continue using the incumbent technology.

As information technology (IT) continues to be an integral yet evolving component in work settings, organizations need to ensure that they realize value from IT. Prior studies examining the postadoption consequences of IT use in terms of employee job outcomes have been inconclusive with respect to the magnitude and direction of these impacts�??i.e., the positive, negative, and nonsignificant impacts of IT use on job outcomes. The question of under what conditions IT use leads to favorable job outcomes over time thus remains largely unanswered. We develop a model of IT-related contingencies that integrates core constructs from the IT adoption research with two key job outcomes: job satisfaction and job performance. We hypothesize that in the post-adoption phase, technology-job fit is a key moderator of the relationships between IT use for supporting sales operations and job outcomes. Further, we suggest a theoretical extension of the classical predictors of IT adoption�??perceived usefulness and perceived ease of use�??as we expect them to moderate the effect of IT use on job performance over time. We tested our model in a longitudinal field study among 295 field sales personnel over a 24-month period. We found that although IT use had a negative effect on job satisfaction during the post-adoption phase, this effect was moderated by technology-job fit such that the negative effect was significantly attenuated by technology-job fit. We also found that perceived usefulness, perceived ease of use, and technology-job fit enhanced the positive effect of IT use on job performance. Our findings offer insights into the mechanisms and conditions related to the post-adoption impacts of IT use on key job outcomes.

Purpose �?? Consumer adoption of online shopping continues to increase each year. At the same time, online retailers face intense competition and few are profitable. This suggests that businesses and researchers still have much to learn regarding key antecedents of online shopping adoption and success. Based on extensive past research that has focused on the importance of various online shopping antecedents, this work seeks to provide an integrative, comprehensive nomological network. Design/methodology/approach �?? The authors employ a mixed-methods approach to develop a comprehensive model of consumers online shopping behavior. To that end, in addition to a literature review, qualitative data are collected to identify a broad array of possible antecedents. Then, using a longitudinal survey, the model of consumer shopping intentions and behaviors is validated among 9,992<br/>consumers. Findings �?? The authors identified antecedents to online shopping related to culture, demographics, economics, technology and personal psychology. Our quantitative analysis showed that the main drivers of online shopping were congruence, impulse buying behavior, value consciousness, risk, local shopping, shopping enjoyment, and browsing enjoyment. Originality/value �?? The validated model provides a rich explanation of the phenomenon of online shopping that integrates and extends prior work by incorporating new antecedents.