Search results

The main aim of the study is to identify some critical microeconomic determinants of financial distress and to design a parsimonious distress prediction model for an emerging economy like India. In doing so, the authors also attempt to compare the forecasting accuracy of alternative distress prediction techniques.

In this study, the authors use two alternatives accounting information-based definitions of financial distress to construct a measure of financial distress. The authors then use the binomial logit model and two other popular machine learning–based models, namely artificial neural network and support vector machine, to compare the distress prediction accuracy rate of these alternative techniques for the Indian corporate sector.

The study’s empirical results suggest that five financial ratios, namely return on capital employed, cash flows to total liability, asset turnover ratio, fixed assets to total assets, debt to equity ratio and a measure of firm size (log total assets), play a highly significant role in distress prediction. The study’s findings suggest that machine learning-based models, namely support vector machine (SVM) and artificial neural network (ANN), are superior in terms of their prediction accuracy compared to the simple binomial logit model. Results also suggest that one-year-ahead forecasts are relatively better than the two-year-ahead forecasts.

The findings of the study have some important practical implications for creditors, policymakers, regulators and other stakeholders. First, rather than monitoring and collecting information on a list of predictor variables, only six most important accounting ratios may be monitored to track the transition of a healthy firm into financial distress. Second, our six-factor model can be used to devise a sound early warning system for corporate financial distress. Three, machine learning–based distress prediction models have prediction accuracy superiority over the commonly used time series model in the available literature for distress prediction involving a binary dependent variable.

This study is one of the first comprehensive attempts to investigate and design a parsimonious distress prediction model for the emerging Indian economy which is currently facing high levels of corporate financial distress. Unlike the previous studies, the authors use two different accounting information-based measures of financial distress in order to identify an effective way of measuring financial distress. Some of the determinants of financial distress identified in this study are different from the popular distress prediction models used in the literature. Our distress prediction model can be useful for the other emerging markets for distress prediction.

The purpose of this paper is to investigate the nexus between export participation and productivity performance of transport manufacturing firms in India, for the period 1994‐2006.

The relative performance of exporting vis‐à‐vis non‐exporting firms in the industry is examined by utilizing a semi‐parametric test based on the principle of first order stochastic dominance. Subsequently, the causal relation between export and productivity is tested by mainly focusing on learning‐by‐exporting and self‐selection hypotheses.

The authors' results suggest that productivity performance of firms does not directly affect the probability of exporting. However, the results do provide some evidence which indicates that good firms self‐select into the export market. Furthermore, it was also found that sunk costs of exporting are the key determinants of probability of exporting in the industry. Finally, the authors tested the effect of exporting on productivity and found that past exporting experience or history has a significant and positive impact on firms' productivity.

In the light of the findings of this study, it can be suggested that the trade policy in India should focus on encouraging firms to increase export participation. At the same time, the authors' evidence also advocates that the economic policies should also aim on technology enhancement (i.e. more incentive for R&D activities and training) of firms, to help them achieve higher levels of productivity and efficiency, which in turn will increase the probability of their survival in the highly‐competitive international export market.

The paper provides new evidence on the export‐productivity nexus from the Indian manufacturing industry by testing the empirical validity of the learning‐by‐exporting and self‐selection hypotheses, along with the role of sunk costs in export decisions of firms.

This paper aims to explore the impact of metaverse technology on the hospitality and tourism industry. The introduction of metaverse technology has revolutionised the way the hospitality and tourism industry works. In the present study, the authors have investigated the role of social media marketing in the adoption of metaverse technology in hotel booking in India.

An extended technology acceptance model was proposed for an empirical investigation in the Indian context. Sample of 344 respondents was collected across India using a purposive sampling technique for the purpose of data analysis. The structural model analysis is used to analyse the data collected from the respondents using the SmartPLS software to check the structural and the measurement fit of the model.

The adoption intentions were largely influenced by the utility, attitude (ATT) and ease of use of the technology, and social media marketing plays a major role in influencing the perceived usefulness (PU) and ease of use (PEU). The study finds positive ATTs of the customers for using metaverse technology for booking their hotels. PU and PEU significantly influence the ATT of the consumer indicating the traveller’s perception of the usefulness and ease of metaverse technology influence their ATTs towards adoption.

Influence of metaverse technology is at a nascent stage in India specifically for hotel booking and tourism. The authors have used discriminant validity by using the criteria for both the square root of the average variance extracted and heterotrait–monotrait ratio tests, and the results suggest that the constructs in the research are distinct from other.

This study aims to identify the critical barriers to women’s leadership in Indian corporate sector using the interpretive structural modeling (ISM) approach.

Through data obtained from extant literature and the expert opinion of women seeking higher managerial positions in the Indian corporate sector, this study identified total 18 barriers to women’s leadership. Thereafter, this study used the Delphi technique to identify the most critical barriers and ISM to understand the causal relationship among them, and then ranked them based on relevance.

Of the 13 critical barriers identified, corporate policies, conscious organizational bias and family responsibilities had the highest driving power. By contrast, inadequate career opportunities and the lack of risk-taking ability and assertiveness had the highest dependence power. Unconscious organizational bias and occupational segregation were other prominent barriers.

This study establishes the interrelationships between women’s leadership barriers. It provides a well-defined model which helps to get theoretical insight considering barriers for women leaders in their career progression in the Indian context. Based on the ISM model, these findings can help academicians and researchers gain deep insights into the barriers to women’s leadership in the Indian context, as no studies have been found in the literature concerning the given subject.

Based on the findings, corporations and policymakers can design inclusive leadership policies to support women as they climb the corporate ladder and to enhance their contribution to organizational success.

To the best of the authors’ knowledge, this is the first study to identify barriers to women’s leadership in India using ISM analysis.

This paper aims to identify and model barriers to internationalising automotive small and medium-sized enterprises (SMEs) from emerging market perspectives using the interpretive structural modelling (ISM) approach.

In this paper, 13 critical barriers are identified through an exhaustive literature review and the Delphi method. The ISM tool is then used to establish interrelationships among the identified barriers to expose and discuss the key barriers having high-driving power.

It was found that barriers such as trade agreements and export documentation, exchange rates and material inadequacies were relatively less challenging than the other barriers. At the next level, there are barriers such as supply chain, high international quality standards, legal barriers, skilled labour marketing capacity and information and logistics and infrastructure. Finally, barriers such as government policies, entrepreneurial orientation and technology and finance availability posed the most significant challenge for the internationalisation of Indian SMEs. These barriers warrants immediate and considerable attention.

This study developed a model based on experts’ opinions, which may be biased and influence the final model as proposed in this study. This research will help the owners/managers of the SMEs and policymakers identify and understand the significance and relevance of automotive sector barriers while strategizing.

To the best of the authors’ knowledge, this is the first time an attempt has been made to apply ISM methodology to explore the interdependencies among the critical barriers of internationalisation for SMEs of Indian automotive industries. This study will guide the owner–managers management practices to overcome ineffective practices and move towards successful internationalisation.

In recent times, the growing use of electronic word of mouth (eWOM) has attracted consumers, organizations and marketers alike. The objective of this study is to summarize and compare the current mass of eWOM research published in leading hospitality and tourism journals with research published in the other fields of both business and management.

This study uses multiple bibliometric analysis methods, including citation, co-citation, keyword and co-word analysis. It compares various assessments of eWOM research published in 399 selected business publications and 398 selected hospitality/tourism publications (ABDC A and above and ABS 3 and above) between 2003 and 2021.

The co-citation analysis identified three thematic areas under each of the domains, i.e. in the hospitality/tourism field, the three themes included eWOM and behavior; eWOM and social media; and eWOM as a marketing tool. Similarly, under the business field (encompasses remaining business and management subdisciplines), the three themes are eWOM and sales, eWOM quality and attributes; and eWOM, information and consumer. Additionally, the word and co-word analysis mapped the comparative evolution of research in these two fields. The study advocates more research focusing on less researched platforms using diverse data, recommender systems adoption and application of eWOM in the business to business (B2B) context.

This study summarizes the overall theoretical and conceptual structure of eWOM research in both business and hospitality/tourism fields; based upon which, several recommendations for future research are proposed.

By comparing the developments in the specialized hospitality/tourism sector with broader management literature using multiple, complementary techniques, this study brings out important insights for hospitality/tourism researchers.

This study explored the role of inter-organizational systems (IOS) appropriation in the form of five key IOS usage-based capabilities, namely, IOS use for communication, intelligence, integration, collaboration and coordination in the development of tourism SC agility and resilience. Furthermore, the inter-relationship among these IOS usage-based capabilities were explored.

The study collected perceptual measures from hotel managers and tour managers having sufficient experience in the tourism sector. With 209 completed responses, the data were analyzed using partial least squares.

The study found IOS use of communication and intelligence as prominent enablers of IOS use for integration, collaboration and coordination. Furthermore, IOS use for integration, collaboration and coordination was found to have a prominent influence in the development of tourism SC agility and resilience. However, the influence of IOS use for communication on collaboration was not supported. Also, the impact of IOS use for collaboration in tourism resilience development was not supported.

The study is the foremost to explore the role of IOS appropriation in the development of dynamic capabilities like agility and resilience in tourism. Furthermore, the study also contributed to extant literature on IOS appropriation through suggesting two additional factors, namely, IOS use for collaboration and coordination to the existing IOS usage-based capabilities.

The purpose of this paper is to analyze the repair of a straight and angular crack in the structure using a piezoelectric material under thermo-mechanical loading by the extended finite element method (XFEM) approach. This provides a general and simple solution for the modeling of crack in the structure to analyze the repair.

The extended finite element method is used to model crack geometry. The crack surface is modeled by Heaviside enrichment function while the crack front is modeled by branch enrichment functions.

The effectiveness of the repair is measured in terms of stress intensity factor and J-integral. The critical voltage at which patch repair is most effective is evaluated and presented. Optimal patch shape, location of patch, adhesive thickness and adhesive modulus are obtained for effective repair under thermo-mechanical loading environment.

The presented numerical modeling and simulation by the XFEM approach are of great benefit to analyze crack repair in two-dimensional and three-dimensional structures using piezoelectric patch material under thermo-mechanical loading.

This paper aims to examine the contagion effects of Greece, Ireland, Portugal, Spain and Italy (GIPSI) and US stock markets on seven Eurozone and six non-Eurozone stock markets.

In this paper, a dynamic conditional correlation (DCC) model popularly known as DCC-GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model given by Engle (2002) is applied to estimate the DCCs across sample markets.

Analyzing the Eurozone crisis period, the empirical results suggest that among GIPSI stock markets, Spain, Italy, Portugal and Ireland appear to be most contagious for Eurozone and non-Eurozone markets. The study finds that France, Belgium, Austria and Germany in Eurozone and UK, Sweden and Denmark in non-Eurozone are strongly hit by the contagion shock.

The findings of the study have significant implications for the concerned regulatory authorities, as it may provide an important direction for further policy research with regard to financial integration in the European Union (EU). From global investors’ perspective, the EU-based diversification strategies seem to be inefficient especially during Eurozone crisis.

To the best of the authors’ knowledge, this is the first study that examines the issue of financial contagion of Eurozone crisis for a large basket of stock markets of European countries comprising seven Eurozone and six non-Eurozone markets for the period 2009-2012. The study uses the Markov regime switching model to identify crisis period and utilizes the DCC estimates of DCC-GARCH to examine the patterns of financial contagion. The finding of this study is quite interesting and is different in several ways than existing studies in the literature.