Ramesh Mohan

Institutions and Poverty

Abstract This study utilises eight alternative measures of institutions and the instrumental variable method to examine the impacts of institutions on poverty. The estimates show that an economy with a robust system to control corruption, an effective government, and a stable political system will create the conditions to promote economic growth, minimise income distribution conflicts, and reduce poverty. Corruption, ineffective governments, and political instability will not only hurt income levels through market inefficiencies, but also escalate poverty incidence via increased income inequality. The results also imply that the quality of the regulatory system, rule of law, voice and accountability, and expropriation risk are inversely related to poverty but their effect on poverty is via average income rather than income distribution.

Impact of Technological Innovation Capabilities on the Market Value of Firms

In the era of globalisation and with the advent of knowledge economies, organisational innovation has assumed a critical role in enhancing economic performance of firms. Proponents of the Resource Based View of the firm and its more recent extensions such as the Knowledge Based View and Dynamic Capabilities Theory have suggested that generation, diffusion and application of organisational knowledge could be the source of sustained competitive advantage and superior performance of firms. While there is near unanimity in accepting the vital role of innovation in a firms performance, consensus on what constitutes organisational innovation and how to measure it has proven to be elusive so far. Most previous research in this area has conceptualised innovation through one or more dimensions of a firms innovative capability using R&D of a firm only. The measurement of the construct has thus reflected this narrow conceptualisation with a single measure of R&D expenditure being the most often used proxy. This study utilises a broader definition of organisational innovation capabilities that includes the generation, dissemination and strength of innovative activity in a firm. The unique features of this study is that it uses multiple indicators of a firms innovation profile along with lagged measures of market value using fixed effects panel data analysis.

Innovation capabilities and international performance of firms: a quantile regression approach

This study examines the relationship between various dimensions of a firm’s technological innovation capabilities and its international performance. We use panel data with multiple indicators of firm level technological capabilities including generation, dissemination, strength and speed of innovation. We employ a quantile regression analysis which allowed us to test the impact of innovation capability on international performance of high, average and poor performers. Our empirical findings indicate significant disparity between the ordinary least square and quantile regression results.

Modelling heterogeneity in perceptions of stress in Indian call centres: a latent class analysis

Stress is one of the biggest human resource (HR) problems facing high turnover industries like the Indian international call centre industry. This paper provides a comprehensive study of how the attitudes of call centre employees towards different aspects of their work affect their level of stress experienced. Our specific contribution to the literature is in understanding the heterogeneity among employees and how that affects meaningful inference in studying employees’ perceptions of stress. To achieve this goal, we compare and contrast between traditional regression models used in the extant literature with latent class regression analysis. The latent class analysis suggests the presence of four distinct groups of employees, confirming the heterogeneity present in the data. This study is unique in trying to explore how individuals may differ in their experience of stress and how there may be heterogeneity in the relationships explored between various cognitive and affective variables and experiences of stress.

Modeling the determinants of turnover intentions: a Bayesian approach

Much of what we learn from empirical research is based on a specific empirical model(s) presented in the literature. However, the range of plausible models given the data is potentially larger, thus creating an additional source of uncertainty termed: model uncertainty. The purpose of this paper is to examine the effect of model uncertainty on empirical research in HRM and suggest potential solutions to deal with the same.,Using a sample of call center employees from India, the authors test the robustness of predictors of intention to leave based on the unfolding model proposed by Harman et.al. (2007). Methodologically, the authors use Bayesian Model Averaging (BMA) to identify the specific variables within the unfolding model that have a robust relationship with turnover intentions after accounting for model uncertainty.,The findings show that indeed model uncertainty can impact what we learn from empirical studies. More specifically, in the context of the sample, using four plausible model specifications, the authors show that the conclusions can vary depending on which model the authors choose to interpret. Furthermore, using BMA, the authors find that only two variables, job satisfaction and perceived organizational support, are model specification independent robust predictors of intention to leave.,The research has specific implications for the development of HR analytics and informs managers on which are the most robust elements affecting attrition.,While empirical research typically acknowledges and corrects for the presence of sampling uncertainty through p-values, rarely does it acknowledge the presence of model uncertainty (which variables to include in a model). To the best of the authors’ knowledge, it is the first study to show the effect and offer a solution to studying total uncertainty (sampling uncertainty + model uncertainty) on empirical research in HRM. The work should open more doors toward more studies evaluating the robustness of key HRM constructs in explaining important work-related outcomes.

Cultural Immersion Trip to Southeast Asia: A Study of Cross-Cultural Intelligence

ABSTRACT The primary purpose of this article is to demonstrate the execution of a successful short- term study trip to Southeast Asia and its influence on a student’s cultural intelligence. This article discusses an academic component of an international study trip that was developed for sophomores at a private university in the New England region of the United States. In an attempt to internationalize, the university led students on a 2-week international trip to Malaysia and Singapore known as the Sophomore International Experience. The authors have developed a model that combines an academically rigorous curriculum that juxtaposes domestic events/experiences with those of the foreign country and propose that the trip gives students first- hand international experience that prepares them to develop a global perception. The article evaluates the validity of the proposed theory with the help of the Cultural Intelligence Scale that measures significance improvements in cultural intelligence after taking the trip.

Endogeneity and dynamics of innovation and firm performance

Technological and innovation capabilities play an important role in determining the performance of firms, especially in knowledge intensive industries. Despite the plethora of studies testing the relationship between innovation capabilities and firm performance, little consensus has been achieved on the veracity of the theoretical claims. In this paper, we argue that the lack of consensus could be on account of the failure to take into account the endogeneity that arises from the decision process that underlies the relationship between development of technological capabilities and its impact on firm performance. We address both these issues and demonstrate that alternative empirical designs can help provide greater external validity.

Attitudinal Antecedents of Intention to Leave and Model Averaging: A Union of Two Literatures

The method of specifying models through regressions can often depict an incomplete picture due to the assumption that inference is conditional upon the specification(s) being an accurate description of the true data generating process. Studies have shown that perfect foresight on the true data generating process is a tenuous argument, especially since knowledge in various sub-fields of management is relatively eclectic with considerable variation in results from empirical research. We demonstrate that Bayesian Model Averaging BMA could be an effective mechanism to eliminate model uncertainty and produce reliable inference and apply it to the domain of attrition research.