Managing organizational entry in China: the roles of newcomer–supervisor exchange,incumbent support,and perceived insider status

The socialization literature has long recognized the important role of perceived insider status in facilitating newcomer adjustment, but knowledge on how to cultivate this desirable psychological state is limited. This study examines newcomer–supervisor exchange as an antecedent of perceived insider status of newcomers, which in turn facilitates their social adjustment and task mastery during organizational entry. We also explore incumbent support as a boundary condition for these effects. We conducted a survey study on 320 new employees and 92 supervisors in China. This study revealed that newcomer–supervisor exchange was related positively to newcomers’ perceived insider status, and perceived insider status mediated the positive relationships of newcomer–supervisor exchange with social adjustment and task mastery. In addition, incumbent support moderated the positive relationship between newcomer–supervisor exchange and perceived insider status, as well as the positive mediated relationships of newcomer–supervisor exchange with social adjustment and task mastery through perceived insider status. Specifically, these relationships were weaker when incumbent support was higher. Theoretical and practical implications were discussed.     

Testing the asymmetric effects of exchange rate pass‐through in BRICS countries: Does the state of the economy matter?

This paper investigates not only the question of whether there is exchange rate pass‐through (ERPT) but also the extent to which the pass‐through is asymmetric or state‐dependent in the BRICS countries. Using monthly data from 1999:M1 to 2019:M12 and non‐linear smooth transition vector autoregressive (STVAR) model, our results provide evidence of period‐specific ERPT between the upper and lower regime periods, governed by the selected transition variables. The results further suggest that the pass‐through of exchange rate is higher when the economy is experiencing large appreciations and expansions as well as large depreciations and recessions. Theimplication for these findings is that ERPT is strongly affected by the state of the economy.     

Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities

Artificial intelligence (AI) has captured substantial interest from a wide array of marketing scholars in recent years. Our research contributes to this emerging domain by examining AI technologies in marketing via a global lens. Specifically, our lens focuses on three levels of analysis: country, company, and consumer. Our country-level analysis emphasizes the heterogeneity in economic inequality across countries due to the considerable economic resources necessary for AI adoption. Our company-level analysis focuses on glocalization because while the hardware that underlies these technologies may be global in nature, their application necessitates adaptation to local cultures. Our consumer-level analysis examines consumer ethics and privacy concerns, as AI technologies often collect, store and process a cornucopia of personal data across our globe. Through the prism of these three lenses, we focus on two important dimensions of AI technologies in marketing: (1) human–machine interaction and (2) automated analysis of text, audio, images, and video. We then explore the interaction between these two key dimensions of AI across our three-part global lens to develop a set of research questions for future marketing scholarship in this increasingly important domain.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

Lend me your wallets: the effect of charismatic leadership on external support for an organization

We argue that charismatic leadership can influence external support for the organization, particularly in making the company more attractive to outside investors. Two studies were conducted to test this general hypothesis. First, an archival study demonstrated that the stock of companies headed by charismatic leaders appreciated more than the stock of comparable companies, even after differences in corporate performance were controlled. It was also found that the effect of charismatic leadership was heightened under more difficult economic conditions. Second, an experiment was conducted in which the salience of charismatic leadership was manipulated, along with information about the prospects for an organization's turnaround. Results showed that appeals from a charismatic leader led to increased investment in the firm, and the leader's influence was greater when the prospects for an organizational turnaround were more difficult. It was also found that an endowment of stock enhanced the influence of charismatic appeals and that charismatic leadership may have affected the general risk propensities of followers. These findings were interpreted in terms of an external perspective on leadership, illustrating how leaders can manage the firm's economic and social environment. Copyright © 2004 John Wiley & Sons, Ltd.     

Negotiation support using the Graph Model for Conflict Resolution

The Graph Model for Conflict Resolution is a flexible methodology for systematically studying strategic conflicts in the real world, and is therefore a natural tool for negotiation support. The basic definitions underlying the graph model are reviewed, and the techniques for analysis and interpretation are discussed. The modeling and analysis of a case study, an international trade negotiation concerning the export of Canadian softwood lumber to the United States, are used to demonstrate the practical application of the Graph Model for Conflict Resolution as a negotiation support tool. The modeling and analysis is carried out using the GMCR software system. The ability of the Graph Model for Conflict Resolution to provide insights and advice to negotiators is emphasized.     

制约东北振兴的金融因素分析与建议

区域经济增长离不开区域金融发展的必要支持。东北地区金融发展水平的相对落后，不仅制约了金融推动经济发展功能的发挥，而且在一定程度上也阻碍经济的进一步发展。本文主要探讨了制约东北地区经济发展的金融支持因素，在此基础上有针对性地提出一些对策建议。论文指出，要促进东北地区的经济发展，就必须加强区域金融合作，要形成金融产业群，协调发展各类金融机构，合理配置区域金融资源，加强金融创新等。     

A conceptual framework of anonymity in Group Support Systems

As the development and use of automated systems for collaborative work grows, the need for a better understanding of these systems becomes more important. Our focus is on one type of system, a Group Support System (GSS) and, in particular, on one important aspect of a GSS—anonymity. A conceptual framework for the study of anonymity in a GSS is presented, which describes the general classes of variables and their relationships. These variables include the factors that influence anonymity in a GSS, types of anonymity, and the effects of anonymity on a message sender, receiver, group process, and outcome. Each of these variables is discussed with working propositions presented for important group process and outcome measures. The objectives of this article are to highlight the importance and complexity of anonymity, to act as a guide for empirical investigations of anonymity, and to influence future GSS development and use.     

The “swing of the pendulum” from public to market support for science and technology: Is the U.S. leading the way?

The structure and financing of science and technology activities are undergoing a slow, but profound, change. This change can be briefly characterized as a shift from relying and supporting public science to a stronger emphasis on “market-based” incentives for science and technology. In this paper we analyze this shift in a historical perspective, discussing both the theoretical explanations and the empirical trends of the ongoing change. While we do not claim to provide a comprehensive and exhaustive identification of the causes of this shift, we argue that it is largely driven by the perception of a shift of the U.S. policy towards market-based, rather than publicly supported, incentives for science and technology. This, in turn—given the strong economic performance of the United States over the 1990s—has influenced policies in most OECD countries, especially in Europe.We conclude by analyzing the evolution of research in U.S. higher education and find two major trends: an increasing diversity in the number of institutions of different types other than universities and a steady and continuous public funding of the leading U.S. universities. This has allowed the construction of an infrastructure now used largely by the private sector, but it also noted that the United States has not compromised public support for core areas or in those fields in which there is a clear perception that market incentives are not sufficient for meeting the strategic targets of the U.S. policy. The implication is that there is a considerable policy diversity in the U.S. practice and that all aspects of this diversity should be considered when using the United States as a reference.     

The Structural Relation Between Mortgage and Market Interest Rates

This paper analyzes the dynamic relationship between primary and secondary mortgage markets and the short-term and long-term market interest rates. Using a series of monthly data on fixed rate mortgage rates and GNMA rates, we explore the dependence and speed of adjustment in these primary and secondary mortgage rates to each other as well as to the long and short-term government rates. The results indicate that residential mortgage rates in general, appear to follow the long-term rate and are not very sensitive to movements in the short-term interest rate.