Survivor reactions to organizational downsizing: The influence of justice perceptions and the psychological contract.

The present study examined the relationships of organizational justice and the psychological contract with four outcome variables in a downsizing context. Multinational data were gathered from survivors representing a variety of organizations and industries. The main focus of the current study examined the relationships between survivors' perceptions of procedural, interpersonal, and informational justice and organizational commitment, job satisfaction, turnover intentions, and trust in management. Correlational data indicated that procedural, interpersonal, and informational justice all demonstrated significant correlations with the outcome variables with interpersonal justice demonstrating higher correlations with the outcome variables than procedural justice. Additionally, the results of two structural models indicated that, although both models fit the data equally well, interpersonal justice was the dominant predictor of the outcome variables. Finally, moderated multiple regression analyses indicated that the psychological contract did not act as a moderator on the relationships between the justice and the outcome variables. However, supplemental confirmatory factor analysis suggested that the justice variables might act as a mediator of the psychological contract - outcome variable relationships. Possible explanations of the results as well as implications for practice and future research are provided

Collaborative-demographic hybrid for financial: product recommendation

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsDue to the increased availability of mature data mining and analysis technologies supporting CRM processes, several financial institutions are striving to leverage customer data and integrate insights regarding customer behaviour, needs, and preferences into their marketing approach. As decision support systems assisting marketing and commercial efforts, Recommender Systems applied to the financial domain have been gaining increased attention. This thesis studies a Collaborative- Demographic Hybrid Recommendation System, applied to the financial services sector, based on real data provided by a Portuguese private commercial bank. This work establishes a framework to support account managers’ advice on which financial product is most suitable for each of the bank’s corporate clients. The recommendation problem is further developed by conducting a performance comparison for both multi-output regression and multiclass classification prediction approaches. Experimental results indicate that multiclass architectures are better suited for the prediction task, outperforming alternative multi-output regression models on the evaluation metrics considered. Withal, multiclass Feed-Forward Neural Networks, combined with Recursive Feature Elimination, is identified as the topperforming algorithm, yielding a 10-fold cross-validated F1 Measure of 83.16%, and achieving corresponding values of Precision and Recall of 84.34%, and 85.29%, respectively. Overall, this study provides important contributions for positioning the bank’s commercial efforts around customers’ future requirements. By allowing for a better understanding of customers’ needs and preferences, the proposed Recommender allows for more personalized and targeted marketing contacts, leading to higher conversion rates, corporate profitability, and customer satisfaction and loyalty

DATA ANALYTICS FOR CRISIS MANAGEMENT: A CASE STUDY OF SHARING ECONOMY SERVICES IN THE COVID-19 PANDEMIC

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data

Data Analytics for Crisis Management: A Case Study of Sharing Economy Services in the COVID-19 Pandemic

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data

Why Do Women Have Longer Unemployment Durations than Men in Post-Restructuring Urban China?

This paper provides the first systematic analysis of the reasons why women endure longer unemployment durations than men in post-restructuring urban China using data obtained from a national representative household survey. Rejecting the view that women are less earnest than men in their desire for employment, the analysis shows that women's job search efforts are handicapped by lack of access to social networks, social stereotyping (that married women are unreliable employees), unequal access to social reemployment services stemming from sex segregation prior to the displacement, and wage discrimination in the post-restructuring labor market.Gender inequality, unemployment duration, Oaxaca-decomposition

Executive perks: Compensation and corporate performance in china

Many studies have examined CEO compensation in developed countries, where a long tradition of disclosure renders data readily available. In emerging economies, particularly in China, where market-based compensation is a relatively new phenomenon, there are few studies of CEO compensation. In addition, information on the use of non-cash compensation is almost absent. Building on the general literature on CEO compensation, and Chinese economic and management studies, this article singularly contributes to the extant literature by (1) examining the motivational determinants of CEO perk compensation, on the one hand, and (2) exploring the relative contribution of perks to performance. We anticipate that perks can serve two roles in China: (1) to provide incentives to deter managerial shirking, and (2) to facilitate work and improve production. We find that perks are positively associated with current and future returns on assets, supporting the view that some types of perks may improve firm profitability and/or that perks are paid as a bonus to reward performance. Our findings from stratified samples suggest that perks may incentivize managers, even after controlling for firm fundamentals, such as firm size, growth opportunity, and leverage

* ILR School Theses and Dissertations: A Listing

Compiled by Susan LaCette.revILRThesesComplete.pdf: 4443 downloads, before Oct. 1, 2020

Determinants and Effect of Firm-level Adjustment on Productivity

This thesis examines the motivations for and impacts of different channels of adjustment on firm-level productivity. It specifically focuses on how firms systematically choose between different paths of adjustment and the impact of these choices on firm-level productivity. The first empirical chapter of this thesis examines the determinants of firms’ choice of adjustment. Using a multinomial logit model, it considers the role of the following 4 characteristics: firm size, adjustment size, firm-level variables (R&D, age, multi-plant and foreign ownership) and other factors. The chapter shows that large firms tend to rely more on external forms of adjustment – greenfield investment and mergers and acquisition for expanding firms and; plant closure and plant sale for contracting firms - than small firms. It also shows that firms tend to rely more (less) on external forms of expansion (contraction) when the desired size of adjustment is large. With regards to the firm-level variables considered, this chapter shows that R&D is negatively related to greenfield investment with no/negligible effect on mergers and acquisition, plant closure and plant sale. Age has a negative (no) impact on greenfield investment (mergers and acquisition) and plant closure (plant sale). Multi-plant firms tend to rely more on external forms of adjustment. Lastly, we find that foreign-owned firms are more likely to acquire and close existing plants. The second empirical chapter studies the impact of alternative forms of adjustment on firm-level productivity. This chapter uses the system GMM approach to tackle two sources of bias: simultaneity of adjustment paths-productivity relationship and endogeneity of factor inputs (and self-selection of firms in and out of an industry) in the production function. This chapter shows that there is no statistical relationship between adjustment paths and the long-run productivity of firms. However, given our choice of appropriate control groups and the fact that we use the system GMM approach to alleviate endogeneity concerns, we view our finding of no long-run adjustment effect as novel