Online consumer reviews are known for influencing consumers toward a purchase decision. Reviews contain a variety of features (e.g., star ratings) that have motivated researchers to investigate features of a review leading consumers to accept and act upon the shared information. What is lacking in research are the social components between a consumer and reviewer that may spark a consumer to perceive a social closeness to a reviewer. The perceived social relationship between the consumer and the reviewer, we believe, influences the acceptance of information in a review. Using Construal Level Theory as a lens to examine the social psychological distance between consumers and reviewers, this study investigates this social relationship to determine how people process information in a review, thus impacting their acceptance of the information to form a purchase decision. Our findings suggest there is a significant role in this social relationship and the acceptance of information.

With the increased volume of and access to electronically held assets, ethical behavior of information technology (IT) professionals is critical. The importance of ethical behavior in the workplace has emphasized the need for coverage of ethics in IT-related academic programs. To better understand how academic IT programs support the teaching of ethics, this study examines people’s desires and intentions to act ethically within specific IT-related contexts by developing a survey. These contexts, derived from industry codes of conduct, include intellectual property rights (IPR), software piracy (SWP), and privacy (PR). Hypotheses are developed and tested based on the Theory of Planned Behavior and dual process cognitive models. The results show that people’s intent to behave ethically with IT decreases their intentions to behave unethically with IPR and SWP and that people’s desire to behave ethically with IT decreases their intentions to behave unethically with IPR and PR.

Data science is a relatively new and emerging field with strong job growth projections. In this work, we develop a new theoretical model based on the theory of planned behavior and the IS Success Model in order to understand public perceptions about data science. Specifically, we aim to determine the potential impact and if the public views data science as beneficial to organizations and society and whether this in turn leads to an intent to use data science. In order to answer the aforementioned questions, we develop a definition of data science derived from current, state-of-the-art literature. Next, we test our theoretical model via a survey instrument that adapts relevant constructs from academic literature. Results indicate support for our model and subsequent hypotheses which show that information quality and system quality impact social norms and behavioral control which in turn influences perceived benefits of data science which influences the intent to use data science. Our model can be employed to advance the adoption of data science as a tool for business and data driven decision-making as well as position academia to train future generations of data scientists.

The technology behind big data, although still in its nascent stages, is inspiring many companies to hire data scientists and explore the potential of big data to support strategic initiatives, including developing new products and services. To better understand the skills and knowledge that are highly valued by industry for jobs within big data, this study reports on an analysis of 1216 job advertisements that contained “big data” in the job title. Our results are presented within a conceptual framework of big data skills categories and confirm the multi-faceted nature of big data job skills. Our research also found that many big data job advertisements emphasize developing analytical information systems and that soft skills remain highly valued, in addition to the value placed on emerging hard technological skills.