Knowledge that Transforms

Partial least squares (PLS) is one of the most popular statistical techniques in use in the Information Systems field. When applied to data originating from a common factor model, as is often the case in the discipline, PLS will produce biased estimates. A recent development, consistent PLS (PLSc), has been introduced to correct for this bias. In addition, the common practice in PLS of comparing the ratio of an estimate to its standard error to a t distribution for the purposes of statistical inference has also been challenged. We contribute to the practice of research in the IS discipline by providing evidence of the value of employing bootstrap confidence intervals in conjunction with PLSc, which is a more appropriate alternative than PLS for many of the research scenarios that are of interest to the field. Such evidence is direly needed before a complete approach to the estimation of SEM that relies on both PLSc and bootstrap CIs can be widely adopted. We also provide recommendations for researchers on the use of confidence intervals with PLSc.

Although mergers and acquisitions (M&A) are a common strategy to reduce costs and pursue growth, the variance in returns from M&A is very high. This research examines how information technology (IT) infrastructure flexibility affects M&A. We use a combination of secondary as well as matched-pair survey data from 100 midsize firms in Spain to investigate this relationship. The empirical analysis suggests that IT infrastructure flexibility affects M&A through two key pathways: (1) a flexible IT infrastructure facilitates the development of business flexibility that provides the responsiveness to seize M&A opportunities and make acquisitions, and (2) a flexible IT infrastructure facilitates the development of post-M&A IT integration capability that provides the control to integrate the IT and business resources of the acquired firm and realize the economic benefits from M&A.

Opinion mining of microblog messages has become a popular application of business analytics in recent times. Opinions reflected in microblogs have provided businesses with great opportunities to acquire insights into their operating environments in real time. In particular, the relationship between microblog sentiment and stock returns is of great interest to investment professionals and academic researchers across multiple disciplines. We empirically test this complex relationship in a comprehensive study. We perform vector autoregression on a data set containing close to 18 million microblog messages spanning 4 years at the market and the individual stock levels, and at the daily and the hourly frequencies. The results show that the influence of microblog sentiment on stock returns is both statistically and economically significant at the hour level. Microblog sentiment is also largely driven by movements in the market. Moreover, stock returns have a stronger influence on negative sentiment than on positive sentiment. These findings have important implications for both research and practice.

Corporate involvement in open source software (OSS) communities has increased substantially in recent years. Often this takes the form of company employees devoting their time to contribute code to the efforts of projects in these communities. Ideology has traditionally served to motivate, coordinate, and guide volunteer contributions to OSS communities. As employees represent an increasing proportion of the participants in OSS communities, the role of OSS ideology in guiding their commitment and code contributions is unknown. In this research, we argue that OSS ideology misfit has important implications for companies and the OSS communities to which their employees contribute, since their engagement in such communities is not necessarily voluntary. We conceptualize two different types of misfit: OSS ideology under-fit, whereby an employee embraces an OSS ideology more than their coworkers or OSS community do, and OSS ideology over-fit, whereby an employee perceives that their coworkers or OSS community embrace the OSS ideology more strongly than the employee does. To develop a set of hypotheses about the implications of these two types of misfit for employee commitment to the company and commitment to the OSS community, we draw on self-determination theory. We test the hypotheses in a field study of 186 employees who participate in an OSS community. We find that OSS ideology under-fit impacts the company and the community in the same way: it decreases employee commitment to the company and commitment to the OSS community. In contrast, we find that OSS ideology over-fit increases commitment to the company but decreases commitment to the OSS community. Finally, we find that employees’ commitment to their company reinforces the impact of their commitment to the OSS community in driving ongoing code contributions. This provides a holistic view of OSS ideology and its impacts among an increasingly pervasive yet understudied type of participant in OSS research. It provides insights for companies that are considering assigning their employees to work in OSS communities as well as for OSS communities that are partnering with these companies.

Information systems security (ISS) behavioral research has produced different models to explain security policy compliance. This paper (1) reviews 11 theories that have served the majority of previous information security behavior models, (2) empirically compares these theories (Study 1), (3) proposes a unified model, called the unified model of information security policy compliance (UMISPC), which integrates elements across these extant theories, and (4) empirically tests the UMISPC in a new study (Study 2), which provided preliminary empirical support for the model. The 11 theories reviewed are (1) the theory of reasoned action, (2) neutrali- zation techniques, (3) the health belief model, (4) the theory of planned behavior, (5) the theory of interpersonal behavior, (6) the protection motivation theory, (7) the extended protection motivation theory, (8) deterrence theory and rational choice theory, (9) the theory of self-regulation, (10) the extended parallel processing model, and (11) the control balance theory. The UMISPC is an initial step toward empirically examining the extent to which the existing models have similar and different constructs. Future research is needed to examine to what extent the UMISPC can explain different types of ISS behaviors (or intentions thereof). Such studies will determine the extent to which the UMISPC needs to be revised to account for different types of ISS policy violations and the extent to which the UMISPC is generalizable beyond the three types of ISS violations we examined. Finally, the UMISPC is intended to inspire future ISS research to further theorize and empirically demonstrate the important differences between rival theories in the ISS context that are not captured by current measures.

User participation is increasingly being seen as a way to mitigate the challenges that firms face in innovation, such as high costs and uncertainty of customer acceptance of their innovations. Thus, firms are establishing online platforms to support users in innovating services, such as iOS and Android platforms for mobile data service (MDS) innovation. Mobile phone platforms are characterized by technology (toolkits) and policy (rules) components that could influence user’s innovation. Additionally, attributes of user innovators (lead userness) are expected to drive their innovation behavior. Yet it is unclear how these characteristics jointly impact users’ service innovation outcomes. To address this knowledge gap, we propose a model that builds on user innovation theory and the work design literature to explain the influences of lead userness, design autonomy, toolkit support, and their interactions on user’s innovation outcomes (innovation quantity) on these platforms. We conceptualize toolkit support in terms of two constructs (i.e., ease of effort and exploration), and design autonomy in terms of three constructs (i.e., decision-making autonomy, scheduling autonomy, and work-method autonomy). The model was tested using survey and archival data from two dominant mobile phone platforms (i.e., iOS and Android). As hypothesized, lead userness, exploration through toolkits, and ease of effort through toolkits positively affect users’ innovation quantity. Additionally, decision-making autonomy and work-method autonomy influence innovation quantity, but scheduling autonomy does not. Further, the proposed three-way interactions between lead userness, toolkit support, and design autonomy constructs on users’ quantity of MDS innovation are largely supported. The findings enhance our understanding of user innovation on mobile phone platforms.

This paper compares two modes for outsourcing the development of information services projects: single-sourcing (where one vendor handles all outsourced activities) and multisourcing (where multiple vendors handle those activities). We assess the relative efficacy of these two outsourcing modes by identifying the effects of three factors: task modularity, the extent of alignment between a (verifiable) performance metric and project revenue, and the extent to which project revenue is itself verifiable. We find that if tasks are modular then multisourcing strictly dominates single-sourcing—provided the verifiable performance metric and project revenue are not completely aligned. Yet if tasks are integrated, then the choice of sourcing mode is more nuanced: the best choice depends on trade-offs among the alignment between performance metric and project revenue, the verifiability of project revenue, and moral hazard. If the verifiable performance metric and project revenue are perfectly aligned, or if project revenue is completely verifiable, then firms prefer single-sourcing because it entails less moral hazard than does multisourcing. Comparative statistics for the effects of task interdependence costs and vendors’ risk aversion reveal that multisourcing (single-sourcing) should be preferred when there are interdependence costs (/when vendors are strongly risk averse).

Privacy decision making has been examined in the literature from alternative perspectives. A dominant “normative” perspective has focused on rational processes by which consumers with stable preferences for privacy weigh the expected benefits of privacy choices against their potential costs. More recently, a behavioral perspective has leveraged theories from decision research to construe privacy decision making as a process in which cognitive heuristics and biases predictably occur. In a series of experiments, we compare the predictive power of these two perspectives by evaluating the impact of changes in the objective risk of disclosure and the impact of changes in the relative perceptions of risk of disclosure on both hypothetical and actual consumer privacy choices. We find that both relative and objective risks can, in fact, influence consumer privacy decisions. However, and surprisingly, the impact of objective changes in risk diminishes between hypothetical and actual choice settings. Vice versa, the impact of relative risk becomes more pronounced going from hypothetical to actual choice settings. Our results suggest a way to integrate diverse streams of the information systems literature on privacy decision making: in hypothetical choice contexts, relative to actual choice contexts, consumers may both overestimate their response to normative factors and underestimate their response to behavioral factors.

The paper develops a needs–affordances–features (NAF) perspective on social media use which posits that individuals’ psychological needs motivate their use of social media applications to the extent to which these applications provide affordances that satisfy these needs. Our theoretical development builds upon two psychological theories, namely self-determination and psychological ownership, to identify five psychological needs (needs for autonomy, relatedness, competence, having a place, and self-identity), that we posit are particularly pertinent to social media use. According to NAF, these psychological needs will motivate use of those social media applications that provide salient affordances to fulfill these needs. We identify such affordances through a comprehensive review of the literature and of social media applications and put forth propositions that map the affordances to the psychological needs that they fulfill. Our theory development generates important implications. First, it has implications for social media research in that it provides an overarching comprehensive framework for the affordances of social media as a whole and the related psychological needs that motivate their use. Future studies can leverage NAF to identify psychological needs motivating the use of specific social media sites based on the affordances the sites provide, and design science research can leverage NAF in the design and bundling of specific social media features to engage users. Second, it has implications for technology acceptance research in that NAF can enrich existing models by opening up the mechanisms through which psychological needs influence user perceptions of social media and their use patterns and behaviors. Finally, NAF provides a new lens and common vocabulary for future studies, which we hope can stimulate cumulative research endeavors to develop a comprehensive framework of information systems affordances in general and the psychological needs that information systems satisfy.

Top persuaders in a social network are social entities whose adoption of a product or service will result in the largest numbers of other entities in the network adopting the same product or service. Predicting top persuaders is critical to an expanding array of important social network-centric applications, such as viral marketing, customer retention, and political message promotion. This study formulates the top persuader prediction problem and develops a novel method to predict top persuaders. Our method development is rooted in eminent social network theories that reveal several forces central to social persuasion, including social influence, entity similarity, and structural equivalence. Our method innovatively integrates these forces to predict top persuaders in a social network, in contrast to existing methods that primarily focus on social influence. Specifically, we introduce persuasion probability that denotes the likelihood of persuasion conditioned on these forces. We then propose how to estimate persuasion probabilities, develop an algorithm to predict top persuaders using the estimated persuasion probabilities, and analyze the theoretical property of the algorithm. We conduct an extensive evaluation with real-world social network data and show that our method substantially outperforms prevalent methods from representative previous research and salient industry practices.