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This study examines how quality signals sent by technology ventures jointly affect investors' decisions under information asymmetry. We categorize signal contents as concerning technology development, venture officers, or early investors. Because similar information may not much reduce information asymmetry, different signals of the same content substitute for one another in enabling ventures to raise capital in their initial public offerings (IPOs). In contrast, signals of different contents collectively reduce information asymmetry, and thus complement each other. Furthermore, public investors may be more capable of assessing, and therefore give more weight to, signals based on the abilities and commitment of venture officers and early investors than to signals based on the viability and appropriability of technology development. We employ fuzzy set qualitative comparative analysis (fsQCA) and find evidence for these mechanisms from data on 268 IPOs of biotechnology ventures in the United States.

The value of the networks that MBA students develop is often limited by the tendency of people to favor connections with similar others, resulting in self-segregation among identity groups. To iden...

We examine the role of self-monitoring personality in shaping network change in two important types of social relationships. In a two-wave social network study, we find that individuals with higher levels of self-monitoring derive persistent personality-linked in-degree centrality benefits in the general socializing network but have fading benefits over time in the close friendship network. Simultaneous examination of the formation and dissolution of relationships over time (network churn) reveals that this pattern of network change is shaped by differential reactions of relationship partners to individuals based upon level of self-monitoring in the two network types. Overall, by incorporating the dynamic reactions of relationship partners, the findings contribute to the understanding of the complex relationship between personality and social network development.

Summary By integrating social network theory and leader–member exchange (LMX) theory, we explore the effects of three types of social relationships on employee innovative behavior: weak ties outside the group, LMX, and strong ties within the group. The results from a sample in a high-tech firm showed that LMX fully mediated the positive relationship between out-group weak ties and innovative behavior. Furthermore, within-group strong ties negatively moderated the second stage of this indirect relationship, such that LMX was positively and significantly related to innovative behavior only when the number of within-group strong ties was low. The theoretical and practical implications of these findings are discussed. Copyright © 2015 John Wiley & Sons, Ltd.

In a social network study, this research investigates proactive personality dissimilarity as a basis for friendship ties over time. It also examines the moderating role of proactive personality on the relationship between network centrality and satisfaction/stress. Longitudinal network data (two periods) were collected from business students (T1, n = 197; T2, n = 212). We captured the early stages of network formation and observed the changes in network structure over time. Findings demonstrate proactive individuals develop ties with less proactive individuals over time, providing evidence of personality heterophily. In addition, proactive personality positively moderates the relationship between network centrality and satisfaction/stress. Interestingly, people’s perceptions of their network position (out-degree ties) were more strongly associated with their personal outcomes than their number of ties as nominated by others. This research is among the first to provide evidence of personality heterophily over time (relationships form because of differences in personality). Moreover, proactive personality is important to both the benefits and costs associated with network participation, pointing to paradoxical effects of proactive personality.

Although e-commerce adoption and customers’ initial purchasing behavior have been well studied in the literature, repeat purchase intention and its antecedents remain understudied. This study proposes a model to understand the extent to which trust mediates the effects of vendor-specific factors on customers’ intention to repurchase from an online vendor. The model was tested and validated in two different country settings. We found that trust fully mediates the relationships between perceived reputation, perceived capability of order fulfillment, and repurchasing intention, and partially mediates the relationship between perceived website quality and repurchasing intention in both countries. Moreover, multi-group analysis reveals no significant between-country differences of the model with regards to the antecedents and outcomes of trust, except the effect of reputation on trust. Academic and practical implications and future research are discussed.

We extend research in institutional theory that has highlighted the importance of boundaries and boundary work to create institutional impact, but has not foregrounded the power aspects inherent in...

Social inequity threatens the long-term welfare and sustainability of society. Caste systems are inequitable social systems with a long history. Affirmative action quotas in public institutions ens...

Using latent growth models (LGM), we investigated how repeated reciprocal attributions between leaders and subordinates influence the development of leader-member exchange (LMX) quality over time. ...

In a longitudinal social network study, we examine whether benefits of self-monitoring personality orientation in the structuring of networks persist over time and whether these benefits are realiz...

Multigroup or between-group analyses are common in the information systems literature. The ability to detect the presence or absence of between-group differences and accurately estimate the strength of moderating effects is important in studies that attempt to show contingent effects. In the past, IS scholars have used a variety of approaches to examine these effects, with the partial least squares (PLS) pooled significance test for multigroup becoming the most common (e.g., Ahuja and Thatcher 2005; Enns et al. 2003; Zhu et al. 2006). In other areas of social sciences (Epitropaki and Martin 2005) and management (Mayer and Gavin 2005; Song et al. 2005) research, however, there is greater emphasis on the use of covariance-based structural equation modeling multigroup analysis. This paper compares these two methods through Monte Carlo simulation. Our findings demonstrate the conditions under which covariance-based multigroup analysis is more appropriate as well as those under which there either is no difference or the component-based approach is preferable. In particular, we find that when data are normally distributed, with a small sample size and correlated exogenous variables, the component-based approach is more likely to detect differences between-group than is the covariance-based approach. Both approaches will consistently detect differences under conditions of normality with large sample sizes. With non-normally distributed data, neither technique could consistently detect differences across the groups in two of the paths, suggesting that both techniques struggle with the prediction of a highly skewed and kurtotic dependent variable. Both techniques detected the differences in the other paths consistently under conditions of non-normality, with the component-based approach preferable at moderate effect sizes, particularly for smaller samples.