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The paper aims to illuminate the relationship between attitude toward the hotel’s website (e-servicescape) and purchase intention. It proposes modeling the dimension of the hotel’s website, its impact on building attitude toward the website and the relations with purchase intention. Further, the role of social risk arising from COVID-19 on the relationship between attitude and purchase intention is explored.

The authors conducted empirical research using an online questionnaire based on a comprehensive evaluation of prior literature. The online questionnaire has 15 questions, and data is gathered from guests who have visited the hotel’s website in the preceding year. SmartPLS was used to analyze the data. Attitude toward the website has been identified as a higher-order factor, and a two-stage embedding technique was adopted for analysis.

The paper gives empirical evidence about how different dimensions of a hotel’s website influence one’s attitude. The finding indicates that the ambience factor, i.e. how effectively a website provides a pleasant and interesting atmosphere for online visitors, is the most critical component for attitude development. Additionally, the finding reveals the negative moderating effect of social risk on the relationship between attitude and purchase intention.

Since demographic factors play a significant role in the social-risk component, the study findings may lack generalizability. As a result, scholars are urged to do more study on the offered ideas.

The paper includes implications for the development of a better e-servicescape, which should incorporate the dimensions discussed in the study to entice the customers for hotel booking and can remove the fear of social risk.

This paper addresses an established need by examining how social risk affects the relationship between attitude toward a website and purchase intention.

This paper aims to create and evaluate a model for cryptocurrency adoption by investigating how age, education, and gender impact Behavioural Intention. A hybrid approach that combined partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) was used for the purpose.

This study uses a multi-analytical hybrid approach, combining PLS-SEM and ANN to illustrate the impact of various identified variables on behavioral intention toward using cryptocurrency. Multi-group analysis (MGA) is applied to determine whether different data groups of age, gender and education have significant differences in the parameter estimates that are specific to each group.

The findings indicate that Social Influence (SI) has the greatest impact on Behavioral Intention (BI), which suggests that the viewpoints and recommendations of influential and well-known individuals can serve as a motivating factor to invest in cryptocurrencies. Furthermore, education was found to be a moderating factor in the relationship found between behavioral intention and design.

Prior studies on technology adoption have utilized superficial SEM and ANN methods, whereas a more effective outcome has been suggested by implementing a dual-stage PLS-SEM and ANN approach utilizing a deep neural network architecture. This methodology can enhance the accuracy of nonlinear connections in the model and augment the deep learning capacity.

The research is based on the Unified Theory of Acceptance and Use of Technology (UTAUT2) and expands upon this model by integrating elements of design and trust. This is an important addition, as design can influence individuals' willingness to try new technologies, while trust is a critical factor in determining whether individuals will adopt and use new technology.

Cryptocurrencies are a relatively new phenomenon in India, and their use and adoption have grown significantly in recent years. However, this development has not been without controversy, as the implications of cryptocurrencies for society, the economy and governance remain uncertain. The results reveal that social influence is an important predictor for the adoption of cryptocurrency in India, and this can help financial institutions and regulators in making policy decisions accordingly.

Given the emerging nature of cryptocurrency adoption in India, there is certainly a need for further empirical research in this area. The current study aims to address this research gap and achieve the following objectives: (a) to determine if a dual-stage PLS-SEM and ANN analysis utilizing deep learning techniques can yield more comprehensive research findings than a PLS-SEM approach and (b) to identify variables that can forecast the intention to adopt cryptocurrency.

The use of natural resources by organizations has a significant impact on society, resulting in business firms playing a vital role in developing sustainable development. As a result, corporate sustainability has become an integral aspect of a company’s vision and policy in the modern days. Companies issue sustainability reports based on various criteria and metrics, thereby attempting to gain a competitive edge. This study aims to assess and prioritize the numerous factors that influence corporate sustainability performance.

The analytic hierarchy process (AHP) is used to prioritize the identified determinants of corporate sustainability performance based on the literature. Several experts from business and academia were engaged to build a comparison matrix for assessing the various aspects of corporate sustainability performance. As a result, various corporate sustainability determinants were determined and prioritized following an extensive literature review using the AHP.

The most critical component for companies is an economic indicator. Developing environmentally and socially responsible suggestions must be a priority for the organization’s economic performance. Economic performance is believed to be most heavily influenced by innovation and in the other category like environmental and social performance are highly influenced by environment management policies and shareholder responsibility, respectively. Researchers’ findings suggest that to promote sustainability, top executives should devote all of their attention to innovation, environment management policies and shareholder responsibility.

These studies are primarily concerned with developed countries. And, the other fundamental constraint of the AHP study is that the rating scale adopted in the analysis is conceptual. Therefore, pairwise comparisons with a variety of indicators have the potential to introduce bias into the results.

This paper proposes a methodical and operational technique for selecting the ultimate priority of sustainability indicators when it comes to exploiting competitive information on business performance. The paper also recommended sustainability rating technique might be carried out once every six months to improve the overall quality and performance of the organization.

The advantages of adopting and executing corporate sustainability are enormous, and managers must determine whether sustainability is profitable or not. This question prompts further inquiries. For example, what are the numerous critical determinants of corporate sustainability performance? Several scholars throughout the world are looking at it for this reason. As a result, it is essential to comprehend and prioritize the various determinants of corporate sustainability performance.

E-commerce companies use different types of dark patterns to manipulate choices and earn higher revenues. This study aims to evaluate and prioritize dark patterns used by e-commerce companies to determine which dark patterns are the most profitable and risky.

The analytic hierarchy process (AHP) prioritizes the observed categories of dark patterns based on the literature. Several corporate and academic specialists were consulted to create a comparison matrix to assess the elements of the detected dark pattern types.

Economic indicators are the most significant aspect of every business. Consequently, many companies use manipulative methods such as dark patterns to boost their revenue. The study revealed that the revenue generated by the types of dark patterns varies greatly. It was found that exigency, social proof, forced action and sneaking generate the highest revenues, whereas obstruction and misdirection create only marginal revenues for an e-commerce company.

The limitation of the AHP study is that the rating scale used in the analysis is conceptual. Consequentially, pairwise comparisons may induce bias in the results.

This paper suggests methodical and operational techniques to choose the priority of dark patterns to drive profits with minimum tradeoffs. The dark pattern ranking technique might be carried out by companies once a year to understand the implications of any new dark patterns used.

The advantages of understanding the trade-offs of implementing dark patterns are massive. E-commerce companies can optimize their spent time and resources by implementing the most beneficial dark patterns and avoiding the ones that drive marginal profits and annoy consumers.

The purpose of this paper is to understand non-vegetarian food consumption behavior, and factors affecting the same of the consumers of middle-sized market, where organized retailing is still at infancy.

A total of 182 households of two middle-sized cities of India were personally surveyed with a structured questionnaire. Simple statistical analysis such as frequency distribution, factor analysis and analysis of variance, logit regression were carried out to infer the required information.

Although an important constituent of the food, for most the consumers, purchase of non-vegetarian products were weekly, well planned, and family affairs. Assured good quality, followed by the meat preparation in front of the customers’ eye emerged important market attributes for selecting the store by the consumers. The study revealed that consumers were mainly dependent on search and credence attributes of the product for non-vegetarian food purchase decisions.

This paper analyses non-vegetarian food consumption food behavior of those customers, for whom non-vegetarian food is still considered as special food, and who belong to middle-sized cities where organized food retailing has just started.

The subject is relatively less researched in emerging markets where organized food retail is still at infancy, and where non-vegetarian foods are considered special.

This study proposes a digital humanitarianism dynamic capability (DHDC) paradigm that explores the direct effects of DHDC on disaster risk reduction (DRR) and the mediating effects of process-oriented dynamic capabilities (PODC) on the relationship between DHDC and DRR.

To validate the proposed model, the authors used an offline survey to gather data from 260 district magistrates in India managing the COVID-19 pandemic.

The results affirm the importance of the DHDC system for DRR. The findings depict that the impact of PODC on DRR in the DHDC system is negligible. This study can help policymakers in planning during emergencies.

Technological innovation has reshaped the way humanitarian organizations (HOs) respond to humanitarian crises. These organizations are able to provide immediate aid to affected communities through digital humanitarianism (DH), which involves significant innovations to match the specific needs of people in real-time through online platforms. Despite the growing need for DH, there is still limited know-how regarding how to leverage such technological concepts into disaster management. Moreover, the impact of DH on DRR is rarely examined.

The present study examines the impact of the dynamic capabilities of HOs on DRR by applying the resource-based view (RBV) and dynamic capability theory (DCT).

After working through the case and assignment questions, students will be able to: understand how product markets in a given global region are to be analysed and assessed; assess various dimensions of consumer behaviour that would impact the strategies of a firm under consideration; identify how a firm can create its brand image and value proposition in a given international market; and evaluate and categorize various threat dimensions that a firm would experience in an international market.

Bajaj Auto (BA) was India’s largest two-wheeler exporter, with ongoing exports to more than 75 countries worldwide. Besides being in other regions of the world, BA’s foray into the African market had been very successful, and it was growing from strength to strength in this market. BA’s motorcycles, three-wheeler rickshaws and small commercial vehicles had been successfully plying the roads of many countries in Africa such as Egypt, Nigeria and Kenya.

Rakesh Sharma (Sharma), the Executive Director of BA, knew very well that Africa was a high-risk-high-gain market for BA. Intense competition from Indian and international two-wheeler and three-wheeler manufacturers, global supply chain and logistics issues, various economic and legal challenges, and the threat of losing African consumer patronage were the challenging issues that Sharma was facing in this market. Would Sharma be able to effectively assess the market environment and consumer behaviour prevalent in the African countries? Would he be able to recognize BA’s brand and value-propositions and identify the international marketing challenges threatening BA’s smooth ride in this market?

The case can be taught in advanced undergraduate, MBA or executive-level programs.

Teaching notes are available for educators only.

CSS 5: International Business.

The purpose of this study is to understand the specific reasons why developed countries could easily start implementing innovative alternative fuel vehicles (e.g. electric vehicles or EVs) while the implementation in developing countries looks so far-fetched, with respect to infrastructure and downstream activities, and suggest the steps that can be taken to effectively address these issues.

This research undertakes case study – Tesla (USA), Mahindra and Mahindra (India) and Tata Motors to bring out the problems being faced by manufacturers from developing countries vis-a-vis the developed countries. The consumers’ side has been adequately represented though an in-depth survey. An analysis is also carried out as to how Tesla has accrued competitive leverage by innovating and vertical integration of up as well as downstream systems.

EV infrastructure remains grossly inadequate in developing countries like India. Two key areas that remain significantly unexplored are the installation of charging stations at parking lots and at the housing clusters and lack of competitive leverage in the services, processes and other downstream systems due to limited research and development capabilities. The performance metrics of domestic EVs lag those of conventional vehicles as well as foreign competitors like Tesla. Range anxiety is ranked as number one in the major concerns among the potential mass buyers of electric vehicles in India.

The value of the paper lies in an in-depth analysis of the relationship between horizontal and vertical perspectives as well as the impact of the product eco-system innovation on both the upstream as well as downstream nodes in the supply chain. Whereas the consumer attitudes and perspectives on e-mobility are inferred from a survey, the impact analysis matrix is used for analyzing the competitive leverage of Tesla through several features in the upstream, downstream and servitization.

The topic of Lean Six Sigma (LSS) implementation in a supply chain context is an emerging research stream comprising of diverse objectives and complex processes thereby presenting opportunities for further exploration and organizational process improvement. Thus, this study proposes an integrated multi-criteria decision-making methodology to determine what can facilitate the successful implementation of LSS as an organizational change strategy in the manufacturing supply chain.

The proposed methodology based on Decision-Making Trial and Evaluation Laboratory and hierarchical Evaluation Based on Distance to Average Solution is employed to ascertain the relative importance and priorities of an identified framework of factors with the aid of opinions of managers in the Nigerian plastics industry.

The results show a high significance of institution-based factors (e.g. government regulations) and present relevant implications to the policymakers as well as the managers and practitioners of the plastics manufacturing industry.

This study indicates a possible pathway to accurately evaluate a framework of critical factors to integrate and institutionalize LSS in the manufacturing supply chain for organizational performance improvement.