Human resource management in the Indian public and private sectors:an empirical comparison

This study compares human resource management (HRM) practices in Indian public- and private-sector organizations. The investigation is based on a questionnaire survey of 137 large manufacturing firms (public sector = 81: private sector = 56). The key areas of analysis include the structure of human resource (HR) department, the role of HR function in corporate change, recruitment and selection, pay and benefits, training and development, employee relations and emphasis on key HRM strategies. Internal labour markets (ILMs) are used to make the comparative analysis. The statistical results show a number of similarities and differences in the HRM systems of Indian public-and private-sector organizations. Against the established notion, the results of this study reveal that the gap between Indian private- and public-sector HRM practices is not very significant. Moreover, in a few HR functional areas (for example, compensation and training and development), Indian private-sector firms have adopted a more rational approach than their public-sector counterparts. © 2004 Taylor and Francis Ltd.

A comparison of linear forecasting models and neural networks:an application to Euro inflation and Euro Divisia

Linear models reach their limitations in applications with nonlinearities in the data. In this paper new empirical evidence is provided on the relative Euro inflation forecasting performance of linear and non-linear models. The well established and widely used univariate ARIMA and multivariate VAR models are used as linear forecasting models whereas neural networks (NN) are used as non-linear forecasting models. It is endeavoured to keep the level of subjectivity in the NN building process to a minimum in an attempt to exploit the full potentials of the NN. It is also investigated whether the historically poor performance of the theoretically superior measure of the monetary services flow, Divisia, relative to the traditional Simple Sum measure could be attributed to a certain extent to the evaluation of these indices within a linear framework. Results obtained suggest that non-linear models provide better within-sample and out-of-sample forecasts and linear models are simply a subset of them. The Divisia index also outperforms the Simple Sum index when evaluated in a non-linear framework. © 2005 Taylor & Francis Group Ltd.

A Franco-British comparison of patterns of working hours in large-scale grocery retailing, with specific reference to part-time work

In this thesis patterns of working hours in large-scale grocery retailing in Britain and France are compared. The research is carried out using cross-national comparative methodology, and the analysis is based on information derived from secondary sources and empirical research in large-scale grocery retailing involving employers and trade unions at industry level and case studies at outlet level. The thesis begins by comparing national patterns of working hours in Britain and France over the post-war period. Subsequently, a detailed comparison of working hours in large-scale grocery retailing in Britain and France is carried out through the analysis of secondary sources and empirical data. Emphasis is placed on analyzing part-time working hours. They are contrasted and compared at national level and explained in terms of supply and demand factors. The relationships between the structuring of, and satisfaction with, working hours and factors determining women's integration in the workforce in Britain and France are investigated. Part-time hours are then compared and contrasted in large-scale grocery retailing in the context of the analysis of working hours. The relationship between the structuring of working hours and satisfaction with them is examined in both countries through research with women part-timers in case study outlets. The cross-national comparative methodology is used to examine whether dissimilar national contexts in Britain and France have led to different patterns of working hours in large-scale grocery retailing. The principal conclusion is that significant differences are found in the length, organization and flexibility of working hours and that these differences can be attributed to dissimilar socio-economic, political, and cultural contexts in the two countries.

Determinants of sales performance in insurance industry:a cross-cultural comparison between the UK and Malaysia

This thesis is concerned with an empirical investigation of the factors that predict a successful salesperson, using a cross-cultural comparison of two countries: the UK and Malaysia. Besides collecting quantitative data, qualitative data on organisational, environmental and cultural factors were also collected through interviews, personal and case observations. The quantitative data consist of sixteen independent factors and three dependent factors. The independent variables include self-efficacy, self-esteem, locus of control, self-monitoring, extrinsic motivation, intrinsic motivation, experience, training perception, role ambiguity, role conflict, role inaccuracy, gender, age, education, race and religion. The dependent variables are performance target achieved, performance earnings and performance ratings. Questionnaires were distributed to about 500 salespersons in each country, from three insurance companies in the UK and two insurance companies in Malaysia. Response rates were 75 and 50 percent from the UK and Malaysia respectively. The survey results indicated that a salesperson's performance in the UK is predicted by self-efficacy, internal locus of control, self-esteem, extrinsic motivation, experience, training perceptions, role conflict and gender. In Malaysia, a salesperson's performance is predicted by self-efficacy, self-monitoring, experience, role conflict, role ambiguity, education, gender, race and religion. Self-efficacy, experience, role conflict and gender are common predictors of salespersons' performance in both cultures. The likely explanation for these results is culture differences, i.e. UK has a homogeneous culture, while Malaysia has a heterogeneous one. Results from the case observations, such as organisational and environmental factors, give supporting evidence in explaining the empirical results. Implications from the findings are discussed from two aspects: (1) theoretical implications for divergence/convergence theory, Hofstede's model, Churchill's model, and (2) managerial implications for selection, training, motivation and appraisal.

Knowledge, capabilities and manufacturing innovation: a US-Europe comparison

In this paper we report a comparative analysis of the factors which contribute to the innovation performance of manufacturing firms in the US state of Georgia, and three European regions, the UK regions of Wales and the West Midlands, and the Spanish region of Catalonia. We consider the factors which shape firms’ ability to generate new products and processes and undertake various forms of organisational and structural change. We are particularly concerned with how firms collect the knowledge on which they base their innovation and their effectiveness in translating that knowledge into new innovations. Three main empirical conclusions result. First, US firms have more widespread links to external knowledge sources than those in Europe and notably the universities make a greater contribution to innovation in the US than in Europe. Second, UK firms prove more effective at capturing synergies between their innovation activities than US and Catalan firms. Third, firms’ operating environment proves more conducive to innovation in the US than in either the UK regions or Catalonia. Our results suggest the potential for mutual learning. For the UK there are lessons in terms of the way in which the universities in Georgia are supporting innovation. For firms in Georgia and in Catalonia the potential lessons are more strategic or organisational and relate to how they can better capture potential synergies between their innovation activities.

A comparison of sales response predictions from demand models applied to store-level versus panel data

In order to generate sales promotion response predictions, marketing analysts estimate demand models using either disaggregated (consumer-level) or aggregated (store-level) scanner data. Comparison of predictions from these demand models is complicated by the fact that models may accommodate different forms of consumer heterogeneity depending on the level of data aggregation. This study shows via simulation that demand models with various heterogeneity specifications do not produce more accurate sales response predictions than a homogeneous demand model applied to store-level data, with one major exception: a random coefficients model designed to capture within-store heterogeneity using store-level data produced significantly more accurate sales response predictions (as well as better fit) compared to other model specifications. An empirical application to the paper towel product category adds additional insights. This article has supplementary material online.

A comparison of microphone and speech recognition engine efficacy for mobile data entry

The research presented in this paper is part of an ongoing investigation into how best to incorporate speech-based input within mobile data collection applications. In our previous work [1], we evaluated the ability of a single speech recognition engine to support accurate, mobile, speech-based data input. Here, we build on our previous research to compare the achievable speaker-independent accuracy rates of a variety of speech recognition engines; we also consider the relative effectiveness of different speech recognition engine and microphone pairings in terms of their ability to support accurate text entry under realistic mobile conditions of use. Our intent is to provide some initial empirical data derived from mobile, user-based evaluations to support technological decisions faced by developers of mobile applications that would benefit from, or require, speech-based data entry facilities.

A comparison of microphone and speech recognition engine efficacy for mobile data entry

The research presented in this paper is part of an ongoing investigation into how best to incorporate speech-based input within mobile data collection applications. In our previous work [1], we evaluated the ability of a single speech recognition engine to support accurate, mobile, speech-based data input. Here, we build on our previous research to compare the achievable speaker-independent accuracy rates of a variety of speech recognition engines; we also consider the relative effectiveness of different speech recognition engine and microphone pairings in terms of their ability to support accurate text entry under realistic mobile conditions of use. Our intent is to provide some initial empirical data derived from mobile, user-based evaluations to support technological decisions faced by developers of mobile applications that would benefit from, or require, speech-based data entry facilities.