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The purpose of this paper is to provide a quantitative approach to measure industry clusters competitiveness.

An attempt has been made to construct a composite indicator backed up by a conceptually grounded framework, by means of Analytical Hierarchical Process technique. Four industry clusters from auto sector in India are chosen for manifestation of the methodology.

The proposed methodology sufficiently emphasises on the order of significance of the factors/indicators that make a cluster competitive. The study demonstrates the comparative competitiveness performance of four select industry clusters from India.

The methodology only focusses on auto clusters from India, application of the model/methodology needs to be extended to other set of industries that follows tier structure, or belong to other developing nations to corroborate the findings.

The proposed approach is a useful tool to provide guidance to policy-makers and in monitoring industry clusters progress.

The paper offers an empirical approach for measuring competitiveness of industry clusters. So far there has been only a minuscule research on cluster competitiveness using empirical methods specifically in case of developing countries like India. Because of the heterogeneity of actors in industry clusters and absence of cluster relevant databases, its performance has been mostly captured via means of case studies. This study is one of its kind that renders comparison of competitiveness across industry clusters by combining secondary data with the perception of cluster actors.

A distinction must be drawn between a dismissal on the one hand, and on the other a repudiation of a contract of employment as a result of a breach of a fundamental term of that contract. When such a repudiation has been accepted by the innocent party then a termination of employment takes place. Such termination does not constitute dismissal (see London v. James Laidlaw & Sons Ltd (1974) IRLR 136 and Gannon v. J. C. Firth (1976) IRLR 415 EAT).

Most companies rarely work on sustainable development as a whole, which includes environmental, social, governance, and economic pillars. The purpose of this paper is to explore causal relationships between pillar scores and overall score of sustainability and identify the most critical pillar to which policy makers should allot limited resources with the highest priority.

Based on Thomson Reuters ASSET4 database of global corporate sustainability, this paper examines the causal relations between pillar scores and overall score of sustainability by using the three-stage integrative methodology consisting of cluster analysis, data mining, and partial least square path modeling.

This paper finds that each pillar has unequal effects on the overall corporate sustainability and that the overall score is affected by not only the direct effects from pillar scores but also the indirect effects from the causal interrelations among pillars. Moreover, the patterns of causal directions and the most critical pillar are sensitive to industries. Social performance is the most critical pillar for the majority of industries, followed by environmental performance, and economic performance, respectively. The governance performance, however, is not the most critical pillar in any industry.

To construct a roadmap for reform priorities, policy makers should follow the top-down approach which involves hierarchical decisions. Using the three-stage methodology, the policy makers first decide on the most critical pillar score before selecting the most critical category score underneath.

Relaxing traditional assumptions of simple average overall score of corporate sustainability, the three-stage integrative framework allows for causal interrelations among pillars and different weights on individual pillars.

During the 1980’s and 1990’s deregulation had become the “recipe” for many countries’ economies to obtain increased efficiency and lower prices. Yet many empirical and theoretical studies of deregulation show that expectations rarely became fulfilled. The purpose of this paper is to develop the model of competition by introducing static and dynamic competition, which has different consequences for market performance. We claim that the development of static and/or dynamic competition post deregulation can be explained by structural conditions, both regarding entry barriers and customer influence. Four different competitive conducts are identified based on an explorative study of four deregulated industries: static, dynamic, hyper, and unheated competition.

This research outlines the technological structure of the entire Japanese manufacturing and service industry using the patent information from research and development (R&D) activities to set R&D goals.

By analyzing the technological development capability of individual companies, the direction of the companies' R&D activities and current state of technological fusion between them can be understood. A group of companies participating in a particular product/service market must have the same technological development capabilities. As a result, the ratio of patent applications by a company to the total number of applications in a technical field will be similar across companies. This study uses the inter-company correlation coefficient of the ratio of patent applications by technical field as an index of technological development capability. A total of 167 major companies covering the major industries of Japan were analyzed. The analysis period was 15 years from 2004 to 2018, and the technical fields were rearranged to 42 fields with reference to the International Patent Classification (IPC)-Technology Concordance used by the World Intellectual Property Organization (WIPO). Considering the fluctuation in patent application opportunities, the number of patent applications was collected for at least three years for the analysis of patent applications by technical field, company and industry.

Examining the entire Japanese industry, the research found that chemicals, ceramics, non-ferrous metals and electrical/electronic equipment act as intermediaries between the respective groups and are linked to the transportation equipment, electrical/electronic equipment and information and communication services industries that are currently driving the Japanese economy. However, the technical connections between these groups are relatively loose. Over the last 15 years, the propagation structure of technical knowledge information has not changed. The progress of technological fusion remains within the scope of commerce and is conditioned by commerce.

Studies focusing on the technological development capability between companies and the technological structure of the Japanese manufacturing and service industries are almost non-existent since 2000 when Japan's economic growth slowed. The analytical methods presented in this research can be applied to individual companies to gain an understanding of technical positions of companies and can be useful for planning a technical environment, business or R&D strategy.

The aim of this paper is to investigate differences in capital structures across industries in Egypt paying particular attention to: corporate characteristics, such as liquidity, asset structure, growth, and size; fiscal characteristics, namely, the application of differential corporate tax rates; and stock market activity.

Comparisons are made between the four main industrial sectors: food, heavy industries, contracting and services. For each industry four aspects of capital structure are assessed. Firms are also classified according to whether their shares are actively traded on the Egyptian stock market. Multiple regressions are run to test a range of hypotheses. ANOVA and multiple comparison procedures are also employed.

Across Egyptian firms, higher business risks do not generally result in lower levels of long‐term capital structure. The contracting sector is significantly different from food, heavy industries and services in its determinants of its short‐term financing and interest ratios. The sector also has a higher level of debt, and so a hypothesised tax‐induced higher debt level for the services sector, which has the highest corporate tax rate, is rejected. Asset‐backing is particularly important in heavy industries, and in non‐actively traded firms. Size and growth are positively related to short‐term financing in heavy industries and services.

The value lies in the comprehensiveness of the study, covering both short‐ and long‐term capital structures across industries, both income measures and capital indebtedness, and distinctions according to whether the shares are actively traded or not.

The objective of this paper is to explore and determine a set of factors that are critical to the success of business‐to‐business (B2B) e‐markets in the aerospace and defence, healthcare, higher education and local government industry sectors, in order to advance our current understanding of what factors facilitate e‐market adoption and success. The paper examines critical success factors (CSFs) for e‐markets from a strategic fit perspective.

The study adopted a semi‐inductive qualitative approach based on a review of the literature, followed by a pilot study and 58 indepth semi‐structured interviews with senior level executives in buyer, supplier, e‐market and third‐party organisations. Qualitative data analysis software, QSR N6, was used to code and analyse the interview data for citations that corresponded with the candidate e‐market CSFs that had been identified either in the literature, pilot study or during the course of the interviews with respondents. The CSFs for e‐markets were ranked by the frequency of respondents citing a particular CSF.

The study found eight factors that are critical to e‐market success and four factors (critical mass, integration issues, value proposition, and leadership participation) were found to be conducive to e‐market success in all four industry sectors. Likewise, four factors (industry knowledge, revenue model, branding and reputation, and rich content) were found to be only conducive to e‐market success in three of the four industry sectors.

The paper can help academic researchers, managers, consultants, practitioners and other professionals better understand what factors are critical to the success of e‐markets and other online enterprises operating in the B2B marketspace.

There have been numerous calls for more empirical research on the dynamics of e‐market adoption for more than a decade. To date, research on the CSFs for e‐markets has been largely anecdotal and sporadic with a paucity of studies noting factors that are likely to be favourable to e‐market success. This study addresses the call for more research on e‐markets and imparts empirical evidence on factors that are perceived to be conducive to the success of e‐markets. It contributes to the base of knowledge on e‐markets by relating the concept of CSFs with the theory of strategic fit as, to date, no known study has examined CSFs for e‐markets from a strategic fit perspective. The study also presents the benefits capabilities‐industry participants’ needs fit conceptual model as a precursor for theory building in future studies on B2B e‐markets and informs stakeholders involved in developing e‐markets or other online B2B ventures to better comprehend the conditions and determinants of success.

The objective of this paper is to explore and determine a set of factors that are critical to the success of business-to-business (B2B) e-markets in the aerospace and defence, healthcare, higher education and local government industry sectors, in order to advance our current understanding of what factors facilitate e-market adoption and success. The paper examines critical success factors (CSFs) for e-markets from a strategic fit perspective.

The study adopted a semi-inductive qualitative approach based on a review of the literature, followed by a pilot study and 58 indepth semi-structured interviews with senior level executives in buyer, supplier, e-market and third-party organisations. Qualitative data analysis software, QSR N6, was used to code and analyse the interview data for citations that corresponded with the candidate e-market CSFs that had been identified either in the literature, pilot study or during the course of the interviews with respondents. The CSFs for e-markets were ranked by the frequency of respondents citing a particular CSF.

The study found eight factors that are critical to e-market success and four factors (critical mass, integration issues, value proposition, and leadership participation) were found to be conducive to e-market success in all four industry sectors. Likewise, four factors (industry knowledge, revenue model, branding and reputation, and rich content) were found to be only conducive to e-market success in three of the four industry sectors.

The paper can help academic researchers, managers, consultants, practitioners and other professionals better understand what factors are critical to the success of e-markets and other online enterprises operating in the B2B marketspace.

There have been numerous calls for more empirical research on the dynamics of e-market adoption for more than a decade. To date, research on the CSFs for e-markets has been largely anecdotal and sporadic with a paucity of studies noting factors that are likely to be favourable to e-market success. This study addresses the call for more research on e-markets and imparts empirical evidence on factors that are perceived to be conducive to the success of e-markets. It contributes to the base of knowledge on e-markets by relating the concept of CSFs with the theory of strategic fit as, to date, no known study has examined CSFs for e-markets from a strategic fit perspective. The study also presents the benefits capabilities-industry participants’ needs fit conceptual model as a precursor for theory building in future studies on B2B e-markets and informs stakeholders involved in developing e-markets or other online B2B ventures to better comprehend the conditions and determinants of success.

In manufacturing industries, the levels of inventories at all stages (i.e. raw material, work-in-process and finished goods inventories) indicate the firm's competitive positioning, strategies, internal processes and relationships with suppliers and downstream customers. The authors identify patterns of manufacturing industries based on levels of raw material and finished goods inventories to classify inbound and outbound supply chain strategies.

The authors review literature on supply chain inventory strategy and perform cluster analysis to analyze patterns of manufacturing industries based on manufacturing industry data collected from US Census of Bureau. Following Porter's Five Forces Model, the authors perform in-depth case studies of four representative industries to analyze factors driving supply chain strategies, including industry intensity of rivalry, threat of new entrants, threat of substitutes, bargaining power of suppliers, and bargaining power of buyers.

This study identifies three streams of research on supply chain strategy: Fisher's model and its variations, lean and agile paradigms, and push/pull systems. It finds that whether an industry shows low or high raw materials or finished goods inventories depending on its products, processes, and the dynamics of all forces described in the Five Forces Model.

This study is not able to include supplier selection, production strategies, warehousing and distribution, and even product design into the analysis of supply chain strategy due to data limitation. This study classifies industries based on average inventory levels of raw materials and finished goods, while inventory levels and supply chain strategies for specific firms may vary significantly within each industry.

This study contributes to the supply chain management literature by providing a parsimonious framework of mapping inbound and outbound supply chain inventory strategies, and the results based on the analyses of all US manufacturing industries provide a baseline picture for supply chain management professionals with manufacturing firms.

In recent research on service quality it has been argued that the relationship between perceived service quality and service loyalty is an issue which requires conceptual and empirical elaboration through replication and extension of current knowledge. Focuses on the refinement of a scale for measuring service loyalty dimensions and the relationships between dimensions of service quality and these service loyalty dimensions. The results of an empirical study of a large sample of customers from four different service industries suggest that four dimensions of service loyalty can be identified: purchase intentions, word‐of‐mouth communication; price sensitivity; and complaining behaviour. Further analysis yields an intricate pattern of service quality‐service loyalty relationships at the level of the individual dimensions with notable differences across industries.