Conducting Research in a Developing Country: A Reflection-in-Action Perspective

ICT4D researchers acknowledge the instrumental role of cultural differences in determining project outcomes. Rarely, however, do they acknowledge culture’s role in the actual research process. This study explores the impact of cultural differences on research conducted by Western-based researchers in a developing country. In mid-2015, we went to Uganda to conduct research on mobile payment systems and technology use in healthcare. This study recounts our data collection process, particularly the unique challenges and opportunities that we experienced. We employ the theory of reflection-in-action to interpret our responses to disruptions to our research project. As part of our contribution to ICT4D research, we offer several recommendations for conducting research in a developing country.

ICT4D Research – Literature Review and Conflict Perspective

ICTs can positively influence development outcomes. However, ICT4D projects have achieved limited success in achieving their development objectives. In this study, we conduct a literature review of ICT4D studies conducted over the period 2006 - 2016. We find that theory linking ICT use and development impact is lacking, and that mobile devices might offer high positive impact possibilities than personal computers. We also find that studies characterize development in different ways: 1) development as expanded digital inclusion, 2) development as increased economic productivity, 3) development as expanded freedom, and 4) development as increased well-being. Across these development perspectives, however, conflict among stakeholders impacts development outcomes. By examining the literature under the lens of postcolonial theory, we find that power asymmetry among stakeholders might increase the resource dependency of beneficiary communities on donors, and might lead to the former resisting further ICT interventions

The Effect of Turnover Intention on Tie Formation in Online Organization Networks

Turnover is costly for organizations. While existing research identifies the antecedents and effects of turnover, little research exists on how to identify individuals intending to leave an organization. We hypothesize that individuals with high turnover intention will participate in fewer communication relationships than average, and that individuals prefer communicating with others of similar levels of turnover intention. We use exponential random graph modeling (ERGM) to test our hypotheses on the email and advice networks of a technology company. ERGM allows us to simultaneously examine the effect of individual and dyadic level attributes on network formation. The results support our hypotheses in the email network, but not in the advice network. Our findings imply that organizations should examine their email networks to identify individuals with high turnover intention, and intervene with incentives if they wish to retain the employees

Why do Students not Major in MIS? An Application of the Theory of Planned Behavior

A McKinsey & Company report states that a gap in information technology skills remains in the U.S. and globally. Combined with continued projections for high growth in MIS positions such as Systems Analysts and Software Applications Developers, increasing student enrollment in MIS continues to be a focus for MIS academicians and professionals. Although studies addressing MIS enrollment issues abound, the manner in which relevant factors are collected is often not systematized. The current study uses established theory and instruments to examine student perceptions of majoring in MIS. Using the Theory of Planned Behavior (TPB), we employ an elicitation-based study uncovering beliefs about majoring in MIS. We subsequently use Partial Least Squares to analyze the importance of these beliefs in influencing intentions to major in MIS. The results lead to specific recommendations for improving MIS enrollments in the U.S. and international settings

Chartering Predictive Analytics: A Case Study

Many organizations are implementing analytics projects. Unfortunately, most big data projects fail, and the reasons are not well understood (LaValle et al. 2013). This paper studies the chartering phase of a predictive analytics project to further our understanding of big data projects. We conduct a qualitative study at a large supply chain company. We employ a variety of qualitative research techniques, including participant observation and interviews of key project personnel. In particular, we conduct a case study of the company’s predictive analytics journey and the challenges it encountered. These include corporate governance, vendor relationships, and lack of data. This research adds insight into why companies implement predictive analytics projects and the challenges they face. The study makes several contributions to the IS literature by adding a qualitative perspective to the big data field, which is dominated by quantitative studies, and focusing on the chartering phase of project implementation

The Social Structure of the Information Systems Collaboration Network: Centers of Influence and Antecedents of Tie Formation

In this study, we examine the historical information systems research collaboration network. We build the network using coauthorship information in the Senior Scholars’ basket of eight journals from the publication of MISQ’s first issue in April, 1977, to November, 2015. The different journals vary widely in their network configurations. We examine the influence of gender homophily, geographic homophily, and field tenure heterophily on coauthorship in the network. From using exponential random graph modeling (ERGM) on a randomly selected subset of the network, we present preliminary evidence that suggests that ties in the IS collaboration network exhibit homophily according to gender and geography. Conversely, coauthorship seems to exhibit heterophily along the temporal dimension: short-tenured researchers in the field prefer to collaborate with long-tenured researchers. ERGM enables one to make statistical inferences concerning the influence of node attributes and structural variables on network formation, which is hard to do with logistical regression because network relationships violate the independence of observations assumption. We also reveal the current center of the IS collaboration network. Based on this center, we propose a metric to measure a researcher’s connectedness in the network

Quantum Computing and IS - Harnessing the Opportunities of Emerging Technologies

Emerging technologies have high potential for impact and are worthy of attention by the Information Systems (IS) community. To date, IS has not been able to lead the research and teaching of emerging technologies in their early stages, arguably because: (1) IS researchers often lack knowledge of the foundational principles of such emerging technologies, and (2) during the emerging phase, there is insufficient data on adoption, use, and impact of these technologies. To overcome these challenges, the IS discipline must be willing to break its own disciplinary research boundaries to go beyond software applications and their related management issues and start studying emerging technologies before they are massively adopted by industry. In this paper, we use quantum computing as an exemplar emerging technology and outline a research and education agenda for IS to harness its opportunities. We propose that IS researchers may conduct rigorous research in emergent technologies through collaboration with researchers from other disciplines. We also see a role for IS researchers in the scholarship of emerging technologies that is of introducing emerging technology in IS curricula

Ideological variation in preferred content and source credibility on Reddit during the COVID-19 pandemic

In this exploratory study, we examine political polarization regarding the online discussion of the COVID-19 pandemic. We use data from Reddit to explore the differences in the topics emphasized by different subreddits according to political ideology. We also examine whether there are systematic differences in the credibility of sources shared by the subscribers of subreddits that vary by ideology, and in the tendency to share information from sources implicated in spreading COVID-19 misinformation. Our results show polarization in topics of discussion: the Trump, White House, and economic relief topics are statistically more prominent in liberal subreddits, and China and deaths topics are more prominent in conservative subreddits. There are also significant differences between liberal and conservative subreddits in their preferences for news sources. Liberal subreddits share and discuss articles from more credible news sources than conservative subreddits, and conservative subreddits are more likely than liberal subreddits to share articles from sites flagged for publishing COVID-19 misinformation

All Together Now: A Framework for Research on Mob Programming

Mob programming (MP) is a relatively new phenomenon in software development. So far, the academic literature has not published in this domain. The goal of this paper is to develop a framework for researching the MP phenomenon. We first outline current practitioner descriptions of and justifications for MP. We then examine concepts from team theory and practices from agile development to identify the team processes and taskwork involved in MP. Based on these, we present a research framework for the academic study of MP. We conclude by considering how the framework can be used by IS researchers