Discovering and Managing Interdependence with Customer‐Entrepreneurs

Research on inter‐organizational relationships has largely focused on the interdependence between formal organizations. In recent years, firms have encountered a new logic in which interdependent parties are not formal organizations but platform‐based ‘customer‐entrepreneurs’ that create value through illegal means. Drawing on a 5‐year‐long qualitative study, we examine how firms recognize and instantiate this new logic and, consequently, respond to their interdependencies with customer‐entrepreneurs. Viewed through the lens of institutional logic, we find that, with the benefit of hindsight, firms recognize the existence of the logic of customer entrepreneurship, which triggers organizational sensemaking that is made up of three elements: interpretation of legitimacy compatibility; interpretation of efficiency compatibility; and integration of stakeholder perspectives. This sensemaking results in either a determined account concluding on the compatibility calculus in a top‐down manner, or an open‐ended account avoiding the construction of a resolute, synthesized view. A determined account leads to a defiance strategy, by which firms attempt to remove the source of interdependencies with customer‐entrepreneurs, whereas an open‐ended account guides firms to espouse a decoupling strategy, whereby firms covertly resort to efficiency maximization enabled by the interdependence. Our results offer implications for the research on inter‐organizational relationships and institutional logic.     

Testing an Economic Model of Nonprofit Growth: Analyzing the Behaviors and Decisions of Nonprofit Organizations,Private Donors,and Governments

This study draws an economic model of the growth of nonprofit organizations by analyzing the behaviors of three major actors—nonprofit organizations, private donors, and governments—in making decisions on the allocation of limited resources for nonprofit services. Since decisions made by each actor affect resource allocation, it is important to understand what drives these decisions. The model was tested using an unbalanced, 463 panel dataset collected from 28 OECD countries over a 23-year period. The results indicated that macro- and micro-economic trends and government policies framed the decision premises of the three major actors, which led them to leverage the supply and demand for goods and services and, in turn, determined how they allocated limited resources for nonprofit services. This result implies that understanding the interdependencies of all sectors of the economy is critical to comprehending the size and development of the nonprofit sector. Effective management of micro-economic policies and macro-economic stability is necessary. More important, however, is understanding how a decision in one part of the economy will have intended and unintended effects on the nonprofit sector.     

Reliability analysis of load-sharing systems with memory

The load-sharing model has been studied since the early 1940s to account for the stochastic dependence of components in a parallel system. It assumes that, as components fail one by one, the total workload applied to the system is shared by the remaining components and thus affects their performance. Such dependent systems have been studied in many engineering applications which include but are not limited to fiber composites, manufacturing, power plants, workload analysis of computing, software and hardware reliability, etc. Many statistical models have been proposed to analyze the impact of each redistribution of the workload; i.e., the changes on the hazard rate of each remaining component. However, they do not consider how long a surviving component has worked for prior to the redistribution. We name such load-sharing models as memoryless. To remedy this potential limitation, we propose a general framework for load-sharing models that account for the work history. Through simulation studies, we show that an inappropriate use of the memoryless assumption could lead to inaccurate inference on the impact of redistribution. Further, a real-data example of plasma display devices is analyzed to illustrate our methods.

     

Religious Attendance,Stress, and Happiness in South Korea: Do Gender and Religious Affiliation Matter?

Is religious attendance positively associated with happiness in South Korea? If yes, can religious attendance buffer against the harmful effect of stress on happiness? Moreover, do gender and religious affiliation modify these associations? This study addresses these questions with data from 2009 Korean General Social Survey which is a nationally representative survey (N = 1,599). Ordinal least square regression analyses reveal that although the effect size is relatively small, religious attendance is associated with a higher level of happiness in South Korea. However, this positive effect holds only for women and only for Protestants. In addition, an interaction effect between religious attendance and stress is observed for women only; the negative association between stress and happiness is weakened among those women who report more frequent church attendance. In this regard, a high level of church attendance buffers against the deleterious effects of stress on happiness for women. I discuss the implications of the findings with regard to theories about religion, mental health, and gender in South Korean context.     

Comparison of leadership styles in Confucian Asian countries

The purpose of this study was to examine the overall impact of four leadership styles (charismatic, directive, participative, and supportive) in four Confucian Asian countries, China, Japan, South Korea, and Singapore, by reviewing recent studies conducted in those countries. By reviewing 38 studies selected from 430 studies published between 2005 and 2016, we identify the impact of the four leadership styles in the four Asian countries. We start with the research question and purpose, and then propose criteria for selecting the relevant recent studies followed by a review of cultural heritage and its influence on leadership in the four Asian countries. We summarize and review the studies and, finally, discuss the theoretical and practical contributions of this study.     

Utilizing Bayesian predictive power in clinical trial design

The Bayesian paradigm provides an ideal platform to update uncertainties and carry them over into the future in the presence of data. Bayesian predictive power (BPP) reflects our belief in the eventual success of a clinical trial to meet its goals. In this paper we derive mathematical expressions for the most common types of outcomes, to make the BPP accessible to practitioners, facilitate fast computations in adaptive trial design simulations that use interim futility monitoring, and propose an organized BPP-based phase II-to-phase III design framework.     

The Effects of Corporate Elitism and Groupthink on Organizational Empathy in Crisis Situations

This study is to empirically testify that corporate elitism is an antecedent to groupthink, which prevents organizational empathy from working effectively in crises. The current study is expected to expand the boundary of crisis management literature into a company’s crisis-preparedness before a crisis occurs. The findings offer the useful guidance to organizations on how to better manage their crisis proneness through identifying the early signs of corporate elitism and groupthink.     

Likelihood ratio tests for multivariate normality

This paper presents some powerful omnibus tests for multivariate normality based on the likelihood ratio and the characterizations of the multivariate normal distribution. The power of the proposed tests is studied against various alternatives via Monte Carlo simulations. Simulation studies show our tests compare well with other powerful tests including multivariate versions of the Shapiro–Wilk test and the Anderson–Darling test.     

On nonparametric variogram estimation

A critical step for geostatistical prediction is estimation of variogram from the data. One of the popular methods estimating variogram is a smoothed version of classical nonparametric variogram estimator. In this paper we investigate its theoretical and empirical properties to provide useful information for using it. The main results are based on asymptotic theories (i.e., risk and central limit theorem) under nearly infill domain sampling. Simulation is also employed to make our points.