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The changing roles of the United Nations (UN) and national institutions have made addressing climate change a critical concern for many multinational enterprises’ (MNEs) survival and growth. This chapter discusses how such institutions, which vary in their nature and characteristics, shape firm strategies for climate change adaptation. Exploring different versions of institutional theory, the chapter demonstrates how and why institutional characteristics affect typical patterns of firm ownership, governance, and capabilities. These, in turn, influence companies’ internationalisation and climate-change strategies. Climate change poses challenges to how we understand firms’ strategic decisions from both an international business (IB) (HQ–subsidiary relations) and global value chains (GVC) (buyer–supplier relations) perspective. However, climate change also provides opportunities for companies to gain competitive advantages – if firms can reconfigure and adapt faster than their competitors. Existing IB and GVC research tends to downplay the importance of climate change strategies and the ways in which coherent or dysfunctional institutions affect firms’ reconfiguration and adaptation strategies in a globally dispersed network of value creation. This chapter presents a perspective on the institutional conditions that affect firms’ climate change strategies regarding ownership, location, and internalisation (OLI), and GVCs, with ‘investment’ and ‘emerging standards’ playing a significant role. The authors illustrate the discussion using several examples from the Global South (i.e. Bangladesh) and the Global North (i.e. Denmark, Sweden, and Germany) with a special emphasis on the garment industry. The aim is to encourage future research to examine how a ‘business systems’, or varieties of capitalism, institutional perspective can complement the analysis of sustainability and climate change strategies in IB and GVC studies.

Social commerce (s-commerce) has gained widespread popularity as a social platform where customers engage in resource-sharing activities such as information exchange, advice-seeking and expressing their opinions on mutual interests. However, existing studies have not fully comprehended the drivers of electronic customer-to-customer interaction (eCCI) and how such behavior contributes to the customer “stick” on s-commerce sites. This study develops the Motivation–Opportunity–Ability (MOA) theory and investigates the impact of MOA factors on eCCI, which in turn affects customer stickiness.

A survey was used to acquire data from 455 valid respondents, and the research employed a combination of fuzzy-set qualitative comparative analysis (fsQCA) and structural equation modeling.

The results revealed associations between perceived self-efficacy, intrinsic motivation, tie strength with other customers, eCCI and customer stickiness.

Considering the limited availability of complete eCCI frameworks in existing scholarly works, the authors present valuable perspectives on the role of consumer characteristics as both antecedents and consequences of eCCI. Additionally, this study proposes a research agenda for the field of eCCI on s-commerce sites.

Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction environment. However, two major challenges exist in the field of audiovisual emotion recognition: (1) how to effectively capture representations of each single modality and eliminate redundant features and (2) how to efficiently integrate information from these two modalities to generate discriminative representations.

A novel key-frame extraction-based attention fusion network (KE-AFN) is proposed for audiovisual emotion recognition. KE-AFN attempts to integrate key-frame extraction with multimodal interaction and fusion to enhance audiovisual representations and reduce redundant computation, filling the research gaps of existing approaches. Specifically, the local maximum–based content analysis is designed to extract key-frames from videos for the purpose of eliminating data redundancy. Two modules, including “Multi-head Attention-based Intra-modality Interaction Module” and “Multi-head Attention-based Cross-modality Interaction Module”, are proposed to mine and capture intra- and cross-modality interactions for further reducing data redundancy and producing more powerful multimodal representations.

Extensive experiments on two benchmark datasets (i.e. RAVDESS and CMU-MOSEI) demonstrate the effectiveness and rationality of KE-AFN. Specifically, (1) KE-AFN is superior to state-of-the-art baselines for audiovisual emotion recognition. (2) Exploring the supplementary and complementary information of different modalities can provide more emotional clues for better emotion recognition. (3) The proposed key-frame extraction strategy can enhance the performance by more than 2.79 per cent on accuracy. (4) Both exploring intra- and cross-modality interactions and employing attention-based audiovisual fusion can lead to better prediction performance.

The proposed KE-AFN can support the development of engaging and empathetic human–computer interaction environment.

Process mining has emerged in the last decade as one of the most promising tools to discover and understand the actual execution of processes. This paper addresses the application of process mining techniques to analyze the performance of automatic guided vehicles (AGVs) in one of the Body in White circuits of the factory that Stellantis has in Vigo, Spain.

Standard process mining discovery and conformance algorithms are applied to analyze the different AGV execution paths, their lead times, main sources and identify any unexpected potential situations, such as unexpected paths or loops.

Results show that this method provides very useful insights which are not evident for logistics technicians. Even with such automated devices, where the room for decreased efficiency can be apparently small, process mining shows there are cases where unexpected situations occur, leading to an increase in circuit times and different variants for the same route, which pave the road for an actual improvement in performance and efficiency.

This paper provides evidence of the usefulness of applying process mining in manufacturing processes. Practical applications of process mining have traditionally been focused on processes related to services and management, such as order to cash and purchase to pay in enterprise resource planning software. Despite its potential for use in industrial manufacturing, such contributions are scarce in the current state of the art and, as far as we are aware of, do not fully justify its application.

The authors argue that privacy is integral to the well-being of consumers and an essential component in not only corporate social responsibility (CSR) but what they term uniquely as social media responsibility (SMR). A conceptual framework is proposed that delineates the privacy issues companies should pay attention to in artificial intelligence (AI)-fueled social media environments.

The authors review literature on privacy issues in social media and AI in the academic and practitioner literatures. Based on the review, arguments focus on the need for an SMR framework, proposing responsible use of consumer data that is attentive to consumers' privacy concerns.

Implications from the framework are a path forward for social media companies to treat consumer data more fairly in this new environment. The framework has implications for companies to reduce potential harms to consumers and consider addressing their power and responsibility. With social media and AI transforming consumer behavior so profoundly, there are a variety of short- and long-term social implications.

Since AI tools are becoming integral to social media company activities, this research addresses the changing responsibilities social media companies have in securing consumers' data and enabling consumers the agency to protect their privacy effectively. The authors propose an SMR framework based on CSR research and AI tools employed by social media companies.

Unstructured data such as images have defied usage in property valuation for a long time. Instead, structured data in tabular format are commonly employed to estimate property prices. This study attempts to quantify the shape of land lots and uses the resultant output as an input variable for subsequent land valuation models.

Imagery data containing land lot shapes are fed into a convolutional neural network, and the shape of land lots is classified into two categories, regular and irregular-shaped. Then, the intermediate output (regularity score) is utilized in four downstream models to estimate land prices: random forest, gradient boosting, support vector machine and regression models.

Quantification of the land lot shapes and their exploitation in valuation led to an improvement in the predictive accuracy for all subsequent models.

The study findings are expected to promote the adoption of elusive price determinants such as the shape of a land lot, appearance of a house and the landscape of a neighborhood in property appraisal practices.

The purpose of this study is to estimate the impact of conflict witnessed in Pakistan on the enrolment rates of boys and girls. Pakistan has the world’s second-highest number of out-of-school children, with an estimated 22.8 million children aged 5–16 years not attending school.

By merging data on violence with the data on enrolment rates, this paper finds that exposure to violence is correlated with a decline in overall district-level enrolment rates in the short run at primary-level schools and middle-level schools.

However, for boys, violence is also negatively correlated with enrolment rates at middle-level schools in the medium run. One possible mechanism tested in this paper is the potential substitution of boys into the labour market during a period of conflict.

To the best of the authors’ knowledge, this paper adds to the existing literature in several ways. Firstly, the effect of conflict on the labour market by impacting schooling for boys and girls is examined for the first time in Pakistan. Secondly, the district-level data set on enrolment rates used for this study is novel and has not been used before for this type of analysis. Thirdly, while this study strengthens the evidence that the short run effects of conflict are stronger than the long-run effects, it also confirms the negative effects of conflict do not fade away immediately. Fourthly, this study emphasizes that each conflict is unique in terms of its heterogeneous effects across different cohorts, such as gender, as these effects are dependent on the mechanism through which conflict impacts each individual.

This paper presents a review of the state of the art on the application of blockchain and smart contracts to the peer-review process of scientific papers. The paper seeks to analyse how the main characteristics of the existing blockchain solutions in this field to detect opportunities for the improvement of future applications.

A systematic review of the literature on the subject was carried out in three databases recognized by the research community (IEEE Xplore, Scopus and Web of Science) and the Frontiers in Blockchain journal. A total of 1,967 articles were initially found, and after the exclusion process, the 26 remaining articles were classified according to the following dimensions: System Type, Open Access, Review Type, Reviewer Incentive, Token Economy, Blockchain Access, Blockchain Identification, Blockchain Used, Paper Storage, Anonymity and Maturity of the solution.

Results show that the solutions are normally concerned on offering incentives to the reviewers' work (often monetary). Other common general preferences among the solutions are the adoption of open reviews, the use of Ethereum, the implementation of publishing ecosystems and the use of InterPlanetary File System to the storage of the papers.

There are currently no studies covering the main aspects of blockchain solutions in the field of scientific peer review. The present study provides an overall review of the topic, summarizing important information on the current research and helping new adopters to develop solutions grounded on the existing literature.

The development of online brand communities employed by marketers to maintain consumer relationships and brand building is increasing. This study aims to explore how value co-creation practices can cultivate consumers' brand loyalty.

Using partial least squares modeling, the hypotheses testing involves the utilization of and data collection from 599 Chinese consumers who actively engage in brand communities in China.

Value co-creation practices in brand communities cultivate consumers' affective commitment and psychological brand ownership, which in turn can further contribute to consumers' brand loyalty.

By offering a more comprehensive insight into how affective commitment and psychological brand ownership act as intermediaries between value co-creation practices and consumers' brand loyalty, this research enhances the existing knowledge on value co-creation and brand management.

Introduction: Skill development is crucial in developing economies by enhancing productivity and creating employment opportunities. At the macro level, it also leads to industrial development and economic growth.

Purpose: The research is to identify the types of skills required for increasing the probability of employability of labour. It also aims to define the challenges and opportunities in skill development to drive change.

Need of the Study: Studying opportunities and challenges for skill development in developing economies is essential for achieving sustainable economic growth, reducing poverty, increasing employment opportunities, and promoting global competitiveness.

Research Methodology: Some skills are recognised through research that has been published to determine the skill set needed to increase labour productivity. To draw lessons, some skill development initiatives by various companies are also identified and presented in case studies. Additionally, several government programs are available to assess the possibilities and prospects for skill development in the Indian market.

Practical Implications: The research will be valuable in micro and macro decision making. At the micro level, research is advantageous for a business person to initiate the skill development of its employees by using government schemes. Nations other than India can understand the policy framework for skill development.

Findings: The term ‘skilling’ has become fashionable. Due to the need for skill-based earnings data, only some studies examine the return on skill (ROS) of the labour market. Skill development plays a significant role in bringing change at the micro and macro levels. Hence it is necessary to exploit all opportunities for skill development.