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This paper explores how the manufacturer’s strategic choice (acquisition or investment) impacts product quality in a supply chain comprising two complementary suppliers and a common manufacturer.

The manufacturer faces six strategic choices to improve product quality: acquiring or investing in the high-capable supplier, the low-capable supplier, or both. As the Stackelberg leader, the manufacturer determines which strategy is adopted, while suppliers are separately responsible for components’ quality and wholesale prices. The authors use game theory and calculate the model with Mathematica.

The authors develop analytical models to analyze how acquisition costs, investment proportions, component importance and quality improvement coefficients influence decision-makers. The results show that the highest quality may not benefit the manufacturer. Investing in or acquiring a low-capable supplier is better than a high-capable supplier under certain conditions. If the gaps between two suppliers’ quality improvement coefficients and the importance of two components are dramatic, the manufacturer should choose an investment strategy.

This study contributes to the complementary supply chain management by comparing two kinds of strategies-acquisition and investment, with a high-capable supplier and a low-capable supplier.

Financial asset return series usually exhibit nonnormal characteristics such as high peaks, heavy tails and asymmetry. Traditional risk measures like standard deviation or variance are inadequate for nonnormal distributions. Value at Risk (VaR) is consistent with people's psychological perception of risk. The asymmetric Laplace distribution (ALD) captures the heavy-tailed and biased features of the distribution. VaR is therefore used as a risk measure to explore the problem of VaR-based asset pricing. Assuming returns obey ALD, the study explores the impact of high peaks, heavy tails and asymmetric features of financial asset return data on asset pricing.

A VaR-based capital asset pricing model (CAPM) was constructed under the ALD that follows the logic of the classical CAPM and derive the corresponding VaR-β coefficients under ALD.

ALD-based VaR exhibits a minor tail risk than VaR under normal distribution as the mean increases. The theoretical derivation yields a more complex capital asset pricing formula involving β coefficients compared to the traditional CAPM.The empirical analysis shows that the CAPM under ALD can reflect the β-return relationship, and the results are robust. Finally, comparing the two CAPMs reveals that the β coefficients derived in this paper are smaller than those in the traditional CAPM in 69–80% of cases.

The paper uses VaR as a risk measure for financial time series data following ALD to explore asset pricing problems. The findings complement existing literature on the effects of high peaks, heavy tails and asymmetry on asset pricing, providing valuable insights for investors, policymakers and regulators.

Owing to highly volatile and chaotic external events, predicting future movements of cryptocurrencies is a challenging task. This paper advances a granular hybrid predictive modeling framework for predicting the future figures of Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT) during normal and pandemic regimes.

Initially, the major temporal characteristics of the price series are examined. In the second stage, ensemble empirical mode decomposition (EEMD) and maximal overlap discrete wavelet transformation (MODWT) are used to decompose the original time series into two distinct sets of granular subseries. In the third stage, long- and short-term memory network (LSTM) and extreme gradient boosting (XGB) are applied to the decomposed subseries to estimate the initial forecasts. Lastly, sequential quadratic programming (SQP) is used to fetch the forecast by combining the initial forecasts.

Rigorous performance assessment and the outcome of the Diebold-Mariano’s pairwise statistical test demonstrate the efficacy of the suggested predictive framework. The framework yields commendable predictive performance during the COVID-19 pandemic timeline explicitly as well. Future trends of BTC and ETH are found to be relatively easier to predict, while USDT is relatively difficult to predict.

The robustness of the proposed framework can be leveraged for practical trading and managing investment in crypto market. Empirical properties of the temporal dynamics of chosen cryptocurrencies provide deeper insights.

Risk perception and ethnocentrism are recognized as significant psychological factors influencing tourism behaviors. However, the impact of tourist ethnocentrism (TE) on tourism and hospitality-related behaviors has mainly been overlooked in previous research. Hence, the objective of the present study is to propose a comprehensive TE model by exploring the influence of TE and risk perception on the domestic hospitality and tourism industry in Israel.

A convenience sample of 204 Israeli respondents 18 years of age or older took part in the study. Structural equation modeling (SEM) was employed to assess hypothesized relationships in the proposed model.

The data confirmed five out of the eight hypotheses tested. The study findings suggest that TE is unrelated to willingness to pay (WTP) a price premium for local travel or dine in local restaurants. Similarly, the authors found no association between risk perception and willingness to dine in local restaurants.

The current investigation contributes to the literature by proposing a model conceptualizing the influence of both TE and risk perception on hospitality (i.e. dine in a local restaurant) and tourism (i.e. domestic travel). The present research findings contribute to the tourism ethnocentrism literature and shed new light on social identity theory (SIT) by pointing to the importance of considering value for money in future conceptualizations and suggest monetary considerations may overshadow other in-group considerations as conceptualized in SIT.

Organizations experience various issues with the optimum use of data. This study is qualitative research to identify and provide a helpful pattern for increasing the performance of sustainable supply chain management (SSCM).

The statistical population in the qualitative section includes managers and experts in the supply chain (SC) and food production. The data were collected via semi-structured interviews, and data saturation happens after the tenth interview. Then, the data were coded using grounded theory and qualitative research analysis. 384 questionnaires were distributed among employees via random sampling. SmartPLS software is used to investigate and analyze the relationships in the mentioned model through 13 core categories.

The findings indicate that organizational productivity and SC deficiencies are among the effective factors in the SSCM primarily identified by this study. Moreover, the findings propose that industry SC, macro policies, organizational performance, social factors, economic factors, organizational factors, political factors, technological factors, production and customer are likely to positively impact the SSCM, which have previously been documented by studies.

The model and concepts extracted from the responses of research participants show well that there are reasons and motivations for increasing the performance of SSCM. Also, the designed model shows well that the motives and reasons for turning to this system are satisfied due to its implementation.

Global brands have become increasingly vulnerable to external disruptions that have negative spillover effects on consumers, business and brands. This research area has recently garnered interest post-pandemic yet remains fragmented. The purpose of this paper is to recognize the most impactful exogenous brand crisis (EBC) and its affective and behavioural impact on consumers.

In Study 1, we applied repertory grid technique (RGT), photo elicitation method and ANOVA comparisons, to identify the most significant EBC, in terms of repercussions on consumer purchases. In Study 2, we performed collage construction and content analysis to ascertain the impact of the identified significant crisis (from Study 1) on consumer behaviour in terms of affective and behavioural changes.

Study 1 results reveal Spread-of-diseases and Natural disaster to be the most impactful EBC based on consumer’s purchase decisions. Study 2 findings uncover three distinct themes, namely, deviant demand, emotional upheaval and community bonding that throws light on the affective and behavioural changes in consumer behaviour during the two significant EBC events.

The collated results of the two studies draw insights towards understanding the largely unexplored conceptualisation of EBC from a multi-level (micro-meso-macro) perspective. The integrated framework drawn, highlight the roles and influences of different players in exogenous brand crisis management and suggests future research agendas based on theoretical underpinnings.

To the best of our knowledge, this is the first study which identifies the most important EBC and explicates its profound impact on consumer purchase behaviour, providing critical insights to brand managers and practitioners to take an inclusive approach towards exogenous crises.

This conceptual study investigates the adoption of education technology (EdTech) products among college students, focusing on identifying the key factors influencing the adoption process within educational institutions. Technology integration in education has rapidly gained prominence, with EdTech offering innovative solutions to enhance teaching and learning experiences. However, understanding the determinants that affect EdTech adoption remains critical for its successful implementation and impact. This paper aims (1) to identify the factors influencing the adoption of EdTech by college students (2) to create a conceptual model that shows the connections between the elements that lead to college students adopting EdTech.

The research employed a mixed-methods approach, combining qualitative data analysis and conceptual modeling to achieve the objectives. The underlying knowledge required to create a qualitative data gathering tool was obtained through a thorough literature analysis on innovation dissemination, educational psychology and technology adoption. College students, teachers and administrators participated in semi-structured interviews, focus groups and surveys to provide detailed perspectives on their attitudes about and experiences with EdTech. The Scopus and Web of Science databases are searched for relevant information in an organized manner in order to determine the factors influencing the adoption of EdTech. Second, an extended version of the technology adoption model is adopted to develop a qualitative data-based conceptual framework to analyze EdTech adoption in the Indian context.

Overall, by highlighting the critical components that emotionally influence college students' adoption of EdTech products in educational institutions, this course adds to the body of information already in existence. The conceptual framework model serves as a roadmap for educational stakeholders seeking to leverage EdTech effectively to enrich the learning environment and improve educational outcomes. By recognizing the significance of the identified factors, academic institutions can make informed decisions to foster a climate conducive to successful EdTech integration.

A comprehensive conceptual framework model was developed based on qualitative data analysis to illustrate the interrelationships between the identified factors influencing EdTech adoption. This model presents a valuable tool for educational institutions, policymakers and EdTech developers to comprehend the complex dynamics of implementing these technological solutions.

The findings of this study demonstrated a number of important variables that affect the uptake of EdTech products in educational settings. These factors encompassed technological infrastructure, ease of use, perceived usefulness, compatibility with existing academic practices, institutional support, financial constraints and individual attitudes towards technology. Additionally, the research explored the significance of institutional preparation for embracing technological advancements as well as the influence of socio-cultural elements.

The purpose of this study is to present a systematic review of the effects of COVID-19 on the conventional textile production subsector. The emergence of the COVID-19 virus in 2019 has subsequently caused many problems, such as unemployment, business closures, economic instability and high volatility in the global capital markets amongst others within the wider manufacturing industry including textile production.

Relevant secondary data are obtained from the Scopus database and Statista. Based on the data analysis of 21 seed articles, three research themes are identified: challenges in the textile industry, new material innovations or solutions and the textile industry performance.

The results reveal that the COVID-19 pandemic has affected the textile industry, disrupted the supply chains of this industry, affected profit margins, stopped employment and impacted the retail of products to customers. Aside from the negative repercussions, there are also good sides to the pandemic which, for instance, range from advanced material innovations to textiles with anti-microbial, self-cleaning and anti-bacterial properties that would limit the transfer of the virus.

Findings reinforced the need for effective strategies and investments in the research and development departments of the various firms in the textile industry to innovate operations and novel materials for the next global pandemic.

Many companies have adopted novel strategies and practices that are helping them to survive the pandemic. This study, therefore, recommends further investigation into material innovations and reimagining strategies by companies and the supply chain within the textile industry so that it is protected against future crises.

This study aims to examine the influence of two significant product labelling strategies (geographical indication [GI] vs country-of-origin [COO]) on shaping customer product attitude and purchase likelihood, considering consumers’ ethnocentric and cosmopolitan tendencies. The authors also investigate the boundary conditions and intervening mechanisms to manage the adverse consumer product evaluations and present mitigating procedures which reinstate favourable product evaluations and purchase likelihood.

The collected data from these all these studies were analysed using ANOVA and mediation anlaysis. The study tests the proposed hypotheses using three follow-up experimental investigations.

The study found that GI (vs COO) labels have a more significant impact on customers’ product evaluation and likelihood of purchase and supported the dispositional effect of ethnocentric and cosmopolitan inclinations. Further, the results indicated that self-product congruence can efficiently regulate consumer dispositions. Also, the results confirmed the significant impact of product identification on influencing consumer attitudes.

The above-said insights add practical insights, particularly concerning product labelling. Also, the insights on product attitudes and purchase likelihood intricacies in the context of product labelling enable companies to comprehend better the significance of GI labels, COO labels and self-product congruence.

To the best of the authors’ knowledge, this is the first time a study has compared the role of two significant product labelling strategies (GI vs COO) in shaping customer product evaluations, confirmed its boundary conditions and shown how to transform them into helpful customer product outcomes.

Recent scholarship has explored higher education institutions’ (HEIs) role in transitioning to a sustainable society, but empirically, questions remain regarding their impact on the sustainability of surrounding areas. This study aims to examine the correlation between HEIs’ sustainability actions and local sustainability performance.

This study uses a linear regression model and principal component analysis to examine the sustainability performance of 105 US metropolitan statistical areas (MSAs) using the US cities sustainable development goal (SDG) index, which hosts 427 HEIs known for sustainability efforts. The weighted HEI sustainability performance score is calculated based on the QS sustainability universities ranking.

The correlation between MSA and HEI sustainability performance exhibits a mix of positive and negative associations, with individual and interlinked SDGs serving as proxies. These correlations encompass a wide range of goals, from economic aspects of SDG 1, 2, 3, 7, 9, social aspects of SDG10 and 16, to socio-environmental aspects of SDG12.

Further exploration is needed to identify the causal mechanisms behind associations between SDG measures and HEI sustainability performance, whether influenced by the institution, the individual or both.

This study suggests that HEIs are already associated with some aspects of community sustainability, but greater contributions to a broader array of sustainability measures are possible.

The correlation found between HEI sustainability actions and SDG10, 12 and 16 index performance in an MSA highlights a connection between HEIs and the attainment of societal goals.

To the best of the authors’ knowledge, this study is the first to examine the correlation between HEI and MSA sustainability performance in the US through individual and interlinked SDG proxies. It provides novel empirical evidence that demonstrates an association between HEI and some aspects of community sustainability performance.