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Cities remain an understudied unit of analysis for understanding the motives of multinational enterprises’ (MNE) foreign direct investment (FDI), with subnational locations in International Business (IB) research to date predominantly captured via the phenomenon of agglomeration. As regional integration projects, such as the European Union and to a lesser degree NAFTA, increasingly reduce the importance of national institutional environments, this paper argues regional and subnational levels become more important for studying MNE location choice. This paper aims to evaluate the explanatory contribution of regional and subnational levels of analysis to understanding MNE location choice.

A qualitative deductive bottom-up multiple-case study research design is adopted to study the city location choices and FDI motives of six automotive and six commercial banking companies. These purposefully sampled manufacturing and service MNEs have different home countries and regional orientations. Data on their foreign investments across the extended Triad of Europe, North America and Asia-Pacific were collected for the time period of 2000–2021.

Findings suggest that different classes of city tend to attract specific types of FDI and that these patterns might vary across sectors and be influenced by the regional strategic orientations of MNEs. Industry-specific findings reveal the importance of related and support industries and partners in a city location for the automotive MNEs, while the commercial banks seek investment opportunities in cities that allow acquisition targets that have an attractive customer based and will improve their local market knowledge.

The findings provide evidence in support of MNEs in manufacturing and service industries perceiving the attractiveness of three city types in different ways across the Triad regions.

The sharing economy has enjoyed rapid growth in recent years, and entered many traditional industries such as accommodation, transportation and lending. Although researchers in information systems and marketing have attempted to examine the impacts of the sharing economy on traditional businesses, they have not yet studied the rental housing market. Thus, this research aims to investigate the impact of the sharing economy (i.e. home-sharing) on traditional businesses (i.e. rental housing market).

The authors assemble rich data from multiple sources about the entry of a leading Chinese home-sharing platform (i.e. Xiaozhu.com) and local housing rental price index. Then, econometric models (i.e. linear panel-level data models) are employed for empirical investigation. Instrumental variables are used to account for potential endogeneity issues. Various robustness checks are adopted to establish the consistency of the findings.

Overall, the estimation results show that the entry of a home-sharing platform will decrease the local housing rental price. Moreover, this impact would be strengthened in a more developed city. Additionally, this impact would be strengthened with higher prices of new houses or second-hand houses.

First, this research is one of the first to study the impact of the sharing economy (i.e. home-sharing) on traditional markets (i.e. housing rentals). Second, it contributes to the relevant literature by documenting that the impact of a platform's entry is not uniform but contingent on city and housing market characteristics. Third, practically, the findings also offer important implications for platform operators and policy makers.

China is the world’s largest user market for digital technologies and experiencing unprecedented rates of rural-urban migration set to create the world’s first “urban billion”. This is an important context for studying nuanced adoption behaviours that define a digital divide. Large-scale studies are required to determine what behaviours exist in such populations, but can offer limited ability to draw inferences about why. The purpose of this paper is to report a large-scale study inside China that probes a nuanced “digital divide” behaviour: consumer demographics indicating ability to pay by electronic means but behaviour suggesting lack of willingness to do so, and extends current demographics to help explain this.

The authors report trans-national access to commercial “Big Data” inside China capturing the demographics and consumption of millions of consumers across a wide range of physical and digital market channels. Focusing on one urban location we combine traditional demographics with a new measure that reflecting migration: “Distance from Home”, and use data-mining techniques to develop a model that predicts use behaviour.

Use behaviour is predictable. Most use is explained by value of the transaction. “Distance from Home” is more predictive of technology use than traditional demographics.

Results suggest traditional demographics are insufficient to explain “why” use/non-use occurs and hence an insufficient basis to formulate and target government policy.

The authors understand this to be the first large-scale trans-national study of use/non-use of digital channels within China, and the first study of the impact of distance on ICT adoption.

The purpose of this paper is to investigate the relationship between the characteristics of the incubation industry, government funding, and the intensity of funding for different services. Because the incubation industry has particular characteristics, government funding varies for different services, and its intensity varies with service.

Government funding is classified as incubation subsidy and incubation incentive. Besides, incubation services include property management, business mentoring as well as investment and financing. Based on this, this study examines the influence mechanism of different subsidy and incentive on incubation services by using the generalized propensity score matching method.

The empirical results show that subsidy and incentive have an inverse-U shape effect on property management service, but a linear effect on business guidance service. Furthermore, subsidy does not affect investment and financing service, but incentive that can have a significant impact.

The theme of government funding and incubator services plays an important role in helping entrepreneurs expand their businesses. Incubation subsidy and incentive can provide important support to help enterprises obtain more preferential loans, technical services and technical support in the incubator. Applying it to incubator services can provide better technology and entrepreneurship guidance. These services can help new entrepreneurs understand products and markets, and how to develop more successfully in the early stage. In short, incubators supported by government funds can provide important support to entrepreneurs to help them successfully realize their business plans.

While it is essential to further research the growing diversity in western metropolitan cities, little is currently known about how the members of various ethnic communities acculturate to multicultural societies. The purpose of this paper is to explore immigrants’ cosmopolitanism and acculturation strategies through an analysis of the food consumption behaviour of ethnic consumers in multicultural London.

The study was set within the socio-cultural context of London. A number of qualitative methods such as in-depth interviews, observation and photographs were used to assess consumers’ acculturation strategies in a multicultural environment and how that is influenced by consumer cosmopolitanism.

Ethnic consumers’ food consumption behaviour reflects their acculturation strategies, which can be classified into four groups: rebellion, rarefaction, resonance and refrainment. This classification demonstrates ethnic consumers’ multi-directional acculturation strategies, which are also determined by their level of cosmopolitanism.

The taxonomy presented in this paper advances current acculturation scholarship by suggesting a multi-directional model for acculturation strategies as opposed to the existing uni-directional and bi-directional perspectives and explicates the role of consumer cosmopolitanism in consumer acculturation. The paper did not engage host communities and there is hence a need for future research on how and to what extent host communities are acculturated to the multicultural environment.

The findings have direct implications for the choice of standardisation vs adaptation as a marketing strategy within multicultural cities. Whilst the rebellion group are more likely to respond to standardisation, increasing adaptation of goods and service can ideally target members of the resistance and resonance groups and more fusion products should be exclusively earmarked for the resonance group.

The paper makes original contribution by introducing a multi-directional perspective to acculturation by delineating four-group taxonomy (rebellion, rarefaction, resonance and refrainment). This paper also presents a dynamic model that captures how consumer cosmopolitanism impinges upon the process and outcome of multi-directional acculturation strategies.

The purpose of this paper is to examine innovation in the resource-constrained context of India’s healthcare industry. It is argued that the process of innovation in addressing healthcare management challenges in such a context occurs through organisational ambidexterity and that human resource management (HRM) plays an important role.

A qualitative research methodology is applied to explore the role of HR practices in facilitating contextual ambidexterity and subsequent innovations in healthcare in India. The unit of analysis is the “case” of healthcare providers in India and in-depth interview and documentary data in two case sites are analysed to reveal the role of HRM in facilitating contextual ambidexterity and innovation. Data analysis was undertaken first at a within-case and then at a cross-case analysis level using interpretive manual coding based on how the data explained the role of HRM in delivering innovative outcomes and supporting organisational ambidexterity.

The authors found evidence of the use of sets of high-involvement HRM practices for exploration of new ideas and efficiency-driven HRM practices for creating contextual ambidexterity in the case organisations. Further, managerial/leadership style was found to play an important role in creating cultures of trust, openness, risk-taking and employee empowerment, supported by an appropriate mix of intrinsic and extrinsic rewards. Finally, training was also reported as being central to creating an ambidextrous context for delivering on various innovations in these healthcare providers.

This study represents an exploration of innovation in the context of India’s healthcare sector through intersecting literatures of ambidexterity, innovation and HRM practices. In light of the emerging economy research context, an important empirical contribution is palpable. Moreover, through a study design which included collecting data from multiple informants on the role of human resources in facilitating innovative outcomes, the authors reveal the role of HR-related initiatives, beyond formal HR practices in creating contextual ambidexterity. This study also reveals the degree to which contextual idiosyncrasies enhance our understanding of the role of HR in facilitating innovation in emerging economies.

The purpose of the paper is to highlight the role of Artificial Intelligence (AI) in the healthcare industry through the Ayushman Bharat health protection scheme by analyzing various technologies being integrated to improve the customer service and experiences in India. The key focus lies on the understanding of the influence of AI in the healthcare system services, the clinical treatment, and the facilities to progress with accurate and precise health screening in India.

A systematic study on the emerging technologies of AI and the applications in the healthcare sector is presented in the form of a viewpoint.

AI certainly enhances experiential services; however, it cannot surpass the human touch which is an essential determinant of experiential healthcare services. AI acts as an effective complementary dimension to the future of healthcare.

This viewpoint discusses the applications and role of AI with the help of relevant examples. It highlights the different technologies being applied and how they will be used in the future focusing upon the Ayushman Bharat health protection scheme in India.

Students pursuing different professional courses at the higher education level during 2021–2022 saw the first-time occurrence of a pandemic in the form of coronavirus disease 2019 (COVID-19), and their mental health was affected. Many works are available in the literature to assess mental health severity. However, it is necessary to identify the affected students early for effective treatment.

Predictive analytics, a part of machine learning (ML), helps with early identification based on mental health severity levels to aid clinical psychologists. As a case study, engineering and medical course students were comparatively analysed in this work as they have rich course content and a stricter evaluation process than other streams. The methodology includes an online survey that obtains demographic details, academic qualifications, family details, etc. and anxiety and depression questions using the Hospital Anxiety and Depression Scale (HADS). The responses acquired through social media networks are analysed using ML algorithms – support vector machines (SVMs) (robust handling of health information) and J48 decision tree (DT) (interpretability/comprehensibility). Also, random forest is used to identify the predictors for anxiety and depression.

The results show that the support vector classifier produces outperforming results with classification accuracy of 100%, 1.0 precision and 1.0 recall, followed by the J48 DT classifier with 96%. It was found that medical students are affected by anxiety and depression marginally more when compared with engineering students.

The entire work is dependent on the social media-displayed online questionnaire, and the participants were not met in person. This indicates that the response rate could not be evaluated appropriately. Due to the medical restrictions imposed by COVID-19, which remain in effect in 2022, this is the only method found to collect primary data from college students. Additionally, students self-selected themselves to participate in this survey, which raises the possibility of selection bias.

The responses acquired through social media networks are analysed using ML algorithms. This will be a big support for understanding the mental issues of the students due to COVID-19 and can taking appropriate actions to rectify them. This will improve the quality of the learning process in higher education in Oman.

Furthermore, this study aims to provide recommendations for mental health screening as a regular practice in educational institutions to identify undetected students.

Comparing the mental health issues of two professional course students is the novelty of this work. This is needed because both studies require practical learning, long hours of work, etc.