Information technology use is typically assumed to have positive effects for users, yet information technology use may also lead to negative consequences with various degrees of gravity. In the current work, we build on dual-systems theories to investigate negative consequences associated with mobile phones use (MPU), defined as the extent to which the use of mobile phones is perceived to create problems in managing one's personal and social life. According to dual-system theories, human behaviour is guided by two systems: reflexive (automatic) and reflective (control), which most of the time work in harmony. But when the two systems come into conflict, they will both complete to exert their influences over behaviour. Thus, we view the negative consequences associated with MPU as an outcome of the tug-of-war between the two systems influencing our day-to-day behaviours, where reflexive system is represented in our study by MPU habits and reflective system is represented by self-regulation. We hypothesise that the influence of habit and self-regulation on these negative consequences will be mediated through MPU. A partial least square analysis of 266 responses was used to validate and test our model. The study results generally support our model. The theoretical and practical implications of our study are discussed.

Online crowdsourcing markets (OCM) are becoming more popular as a source for data collection. In this paper, we examine the consistency of survey results across student samples, consumer panels, and online crowdsourcing markets (specifically Amazon's Mechanical Turk) both within the United States and outside. We conduct two studies examining the technology acceptance model (TAM) and the expectation-disconfirmation theory to explore potential differences in demographics, psychometrics, structural model estimates, and measurement invariances. Our findings indicate that (1) U.S.-based OCM samples provide demographics much more similar to our student and consumer panel samples than the non-U.S.-based OCM samples; (2) both U.S. and non-U.S. OCM samples provide initial psychometric properties (reliability, convergent, and divergent validity) that are similar to those of both student and consumer panels; (3) non-U.S. OCM samples generally provide differences in scale means compared to those of our students, consumer panels, and U.S. OCM samples; and (4) one of the non-U.S. OCM samples refuted the highly replicated and validated TAM model in the relationship of perceived usefulness to behavioral intentions. Although our post hoc analyses isolated some cultural and demographic effects with regard to the non-U.S. samples in Study 1, they did not address the model differences found in Study 2. Specifically, the inclusion of non-U.S. OCM respondents led to statistically significant differences in parameter estimates, and hence to different statistical conclusions. Due to these unexplained differences that exist within the non-U.S. OCM samples, we caution that the inclusion of non-U.S. OCM participants may lead to different conclusions than studies with only U.S. OCM participants. We are unable to conclude whether this is due to of cultural differences, differences in the demographic profiles of non-U.S. OCM participants, or some unexplored factors within the models. Therefore, until further research is conducted to explore these differences in detail, we urge researchers utilizing OCMs with the intention to generalize to U.S. populations focus on U.S.-based participants and exercise caution in using non-U.S. participants. We further recommend that researchers should clearly describe their OCM usage and design (e.g., demographics, participant filters, etc.) procedures. Overall, we find that U.S. OCM samples produced models that lead to similar statistical conclusions as both U.S. students and U.S. consumer panels at a considerably reduced cost.

Recent studies on the business impacts of information technology (IT) have examined these impacts in the context of either other organizational resources or contingency factors. In this study we integrate these perspectives to develop a contingent interaction model. This model examines how a firm’s IT investment interacts differently with resources focusing on creating value (i.e., R&D) and resources focusing on value appropriation (i.e., advertising), depending on the environmental turbulence in the firm’s industry. The results indicate that a firm’s IT interacts differently with other organizational resources depending on (a) the resource’s focus on value creation through innovation or value appropriation in the market; and (b) the extent of turbulence in the firm’s industry. Thus, managers should consider IT’s interactions with other resources while making IT investments. In turbulent and stable environments, managers should seek ways to use IT to complement R&D investments and advertising investments, respectively. Managers should also recognize that IT may erode some of the benefits of R&D and advertising investments in stable and turbulent environments, respectively. They should therefore exercise caution when making concurrent investments in IT and R&D in stable environments and exercise similar caution when making concurrent investments in IT and advertising in turbulent environments.

The dynamic information technology (IT) market—characterized by frequent new releases, designs, and changing options—requires senior IT executives to look closely at new technologies while deriving long-term benefits from the firm’s current technologies. This study focuses on the allocation of IT resources to new or current technologies, which is becoming even more important with tighter IT budgets. The results indicate that a joint consideration of a firm’s core business strategy and organizational commitment to IT provides insights into whether the firm should emphasize new IT or current IT when allocating IT investments. With an increase in organizational commitment to IT, firms pursuing stable products/markets (i.e., Defender(s)) benefit more from current IT, whereas firms seeking new products/markets (i.e., Prospector(s)) benefit more from new IT. Finally, for firms seeking some stable products/markets and some new ones (i.e., Analyzer(s)), organizational commitment to IT does not influence the benefits from emphasizing new or current IT. This study initiates a line of inquiry on the factors influencing the value firms derive from new and current IT. Senior IT executives should carefully examine their firm’s business strategy and organizational commitment to IT when prioritizing investments in new IT relative to the refinement of current IT.

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