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Disturbing increase in the use of virgin resources to produce new products has threatened the environment. Many countries have reacted to this situation through regulations which aim to eliminate negative impact of products on the environment shaping the concept of environmentally conscious manufacturing and product recovery (ECMPRO). The first crucial and the most time-consuming step of product recovery is disassembly. The best productivity rate is achieved via a disassembly line in an automated disassembly process. In this chapter, we consider a sequence-dependent disassembly line balancing problem (SDDLBP) with multiple objectives that is concerned with the assignment of disassembly tasks to a set of ordered disassembly workstations while satisfying the disassembly precedence constraints and optimizing the effectiveness of several measures considering sequence-dependent time increments among disassembly tasks. Due to the high complexity of the SDDLBP, there is currently no known way to optimally solve even moderately sized instances of the problem. Therefore, an efficient methodology based on the simulated annealing (SA) is proposed to solve the SDDLBP. Case scenarios are considered and comparisons with ant colony optimization (ACO), particle swarm optimization (PSO), river formation dynamics (RFD), and tabu search (TS) approaches are provided to demonstrate the superior functionality of the proposed algorithm.

The increasing importance and proliferation of text data provide a unique opportunity and novel lens to study human communication across a myriad of business and marketing applications. For example, consumers compare and review products online, individuals interact with their voice assistants to search, shop, and express their needs, investors seek to extract signals from firms' press releases to improve their investment decisions, and firms analyze sales call transcripts to increase customer satisfaction and conversions. However, extracting meaningful information from unstructured text data is a nontrivial task. In this chapter, we review established natural language processing (NLP) methods for traditional tasks (e.g., LDA for topic modeling and lexicons for sentiment analysis and writing style extraction) and provide an outlook into the future of NLP in marketing, covering recent embedding-based approaches, pretrained language models, and transfer learning for novel tasks such as automated text generation and multi-modal representation learning. These emerging approaches allow the field to improve its ability to perform certain tasks that we have been using for more than a decade (e.g., text classification). But more importantly, they unlock entirely new types of tasks that bring about novel research opportunities (e.g., text summarization, and generative question answering). We conclude with a roadmap and research agenda for promising NLP applications in marketing and provide supplementary code examples to help interested scholars to explore opportunities related to NLP in marketing.

This chapter provides a comprehensive investigation of the literature on sustainability reporting in Islamic banks using a content analysis of 200 English language articles published between 2000 and 2023. The data were collected from the Web of Science (WOS) database and analyzed using Bibliometrix in R software. This chapter addresses the trends of the most influential authors, institutions, countries, and research hotspots. This chapter fills one of the gaps in sustainability reporting literature, setting up a statistical description of the principal features of sustainable Islamic banking research and carrying out an analysis of its knowledge structure via bibliometric analysis. This chapter found that the number of articles on sustainability reporting in Islamic banks has increased over time; however, it is concentrated on a few core sources and authors, mostly related to Islamic finance, accounting, and ethics, as well as sustainability and social responsibility. The analysis of the co-authorship network shows a limited degree of collaboration between researchers from different clusters. The most productive and collaborative countries are Malaysia, the United Kingdom, and Pakistan, reflecting their well-developed and regulated Islamic banking industries that follow sustainability guidelines and frameworks. Moreover, the co-word analysis identifies 11 clusters and 43 keywords that represent the main topics and themes in the field, such as corporate governance, performance, disclosure, and risk. This chapter suggests some directions for future research and action on sustainability reporting in Islamic banks. This chapter contributes to the literature on sustainability in Islamic banking and the UN 2030 Agenda for Sustainable Development.

The growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility – if only the model outputs are interpretable enough to earn the trust of consumers and buy-in from companies. To build a foundation for understanding the importance of model interpretation in image analytics, the first section of this article reviews the existing work along three dimensions: the data type (image data vs. video data), model structure (feature-level vs. pixel-level), and primary application (to increase company profits vs. to maximize consumer utility). The second section discusses how the “black box” of pixel-level models leads to legal and ethical problems, but interpretability can be improved with eXplainable Artificial Intelligence (XAI) methods. We classify and review XAI methods based on transparency, the scope of interpretability (global vs. local), and model specificity (model-specific vs. model-agnostic); in marketing research, transparent, local, and model-agnostic methods are most common. The third section proposes three promising future research directions related to model interpretability: the economic value of augmented reality in 3D product tracking and visualization, field experiments to compare human judgments with the outputs of machine vision systems, and XAI methods to test strategies for mitigating algorithmic bias.

Transport shapes the health of urban populations. It can support healthy behaviours such as participation in regular physical activity and access to community connection. Transport systems can also have major negative impacts on health. For example, through air pollution from fossil fuel-based modes of travel, the risk of injury and death from transport related collisions, and in the way sedentary modes of travelling can contribute to less physically active lifestyles.

This chapter considers the long-term impact of the pandemic on a series of well-researched transport-related health outcomes. It first describes the established connections between transport and health. It then considers the future implications of three potential pandemic-induced shifts: the increased uptake of working from home (WFH); decreased usage of public transport and increased interest in walking and cycling in the local neighbourhood. The impacts of these shifts on the transport-health nexus are then discussed, revealing both positive and negative outcomes. The authors conclude by providing policy recommendations to mitigate possible negative outcomes and strengthen the positive consequences into the future.

In this paper I investigate the nature of the differential in poverty by ethnicity in rural China using data from the Chinese Household Income Project in 2002. For that, I compare observed poverty with that in a counterfactual distribution in which ethnic minorities are given a set of relevant village and household characteristics of the Han majority. In particular, I investigate the importance of the location of minorities in explaining their higher poverty levels. The ethnic poverty differential does not change after equalizing the distribution of the population by geographical region (unless we use a higher poverty line). However, it is reduced after equalizing other locational characteristics of minorities (such as them living in less developed and mountainous areas), their larger number of children, their low education, and their fewer skilled non-agriculture workers. Finally, the ethnic per capita (log) income differential is shown to be higher for higher percentiles, with an increasing role of the geographical region as the main driver of these higher differentials.

This chapter aims to (1) examine the effect of full-time employees’ STARA awareness on innovative work behavioural intentions in US casual dining restaurants; (2) investigate the mediating roles of employees’ challenge–hindrance appraisals of STARA awareness on the relationship between their STARA awareness and innovative work behavioural intentions; (3) compare the group differences between management employees and non-management employees; and (4) provide recommendations for the casual dining restaurants.

This chapter employed an online survey to collect data from 609 full-time employees in US casual dining restaurants, including 306 management employees and 303 non-management employees. Partial least squares–structural equation modelling was applied for data analysis. The results reveal that the high levels of employees’ STARA awareness raise innovative work behavioural intentions through the mediations of challenge appraisal of STARA awareness.

The proposed conceptual framework and empirical findings in this chapter enrich the literature of cognitive appraisal theory, transactional model and stress, two-dimensional stressor framework, and person-environment fit theory. Employees’ challenge appraisal of STARA awareness makes the job insecurity stressor to drive innovative work behavioural intentions. As STARA adoption deepens in casual dining restaurants, managers need to be aware of full-time employees’ stress and psychological responses towards STARA adoption. Restaurants are suggested to provide employees with adequate resources and support to help employees’ professional competency growth. The capable employees will appraise the job insecurity stressor induced by STARA adoption as an opportunity and be motivated to perform innovatively in the workplace. The casual dining restaurants may enjoy a competitive advantage in the market through value-added innovative activities.

Several parameters set economic growth, and one of them is innovation. Thus, it is vital to realize the proper motives for a firm to innovate. The key actors in innovation systems – such as firms, research institutions, academia, and standards developing communities – affect knowledge ­creation, contribute to its diffusion and use, and set the global innovation capacity. Additionally, the market-driven and the worldwide open paradigm may aid to ensure stable and reliable integration, interactive abilities, and active collaborations to drive international innovation and its ­concepts forward. The World Trade Organization (WTO) is an international organization that assembles principles to produce such standards as mentioned above, so as to ensure a prosperous future globally. Global cooperation, openness, interaction, and the generation of motives for innovation are some of the principles that WTO embraces, and all of these are to benefit humanity from free market and trade growth, leading to triumph through innovation. Overall, this chapter examines the significant role of WTO in trade policy uncertainty and how innovation is ensured and stimulated by WTO regarding the specific case of the Chinese market.