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Purpose – With the rapid proliferation of digital technology for creating and disseminating content in different forms – textual, music, video – both firms and consumers have a number of alternative technology and formats available for creating and consuming content. While this has led to more value for consumers, the firms have had mixed results. Some firms have seen their value erode through the adoption of newer formats as compared to the older ones (e.g., streamed music format vs. CD format), and other firms have been generally reluctant to embrace newer technology and formats for the similar reasons.Design/methodology/approach – In this chapter, we review the research issues in designing and pricing such digital content and formats and the various strategies that firms can adopt in ensuring that both firms and consumers benefit through the use of newer formats.Findings – We review and discuss the extant research in this domain and identify research issues for future research.Value/originality – As more traditional products morph into digital services, it is critical that these issues are addressed so that the content creation industry can survive in the short term and prosper in the long term.

The Covid-19 pandemic and the related closures and lockdowns have changed how consumers shop for products and how they consume them. In this paper, the authors focus on how customers' journeys from the awareness stage down to purchase and loyalty stages have been impacted by the pandemic across different product categories and markets and how they affect the same post-pandemic. The authors propose directions for future research based on our analysis.

Analyzing the components of customer utility, the authors provide the basis for the rapid shift towards online and digital touchpoints and the nature of emerging interactions between firms and consumers. The authors highlight those areas where changes could be permanent.

The authors show why some of the changes due to the Covid-19 pandemic could be permanent and irrevocable and what this implies for firms' strategies to acquire, retain, and grow their business with their customers.

The authors highlight why omnichannel strategies are the way for firms to thrive in the post-pandemic marketplace, and outline areas for future research that will allow researchers to examine how customer journeys will evolve post-pandemic.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

The purpose of this paper is to propose a holistic approach for supplier evaluation and purchasing order allocation among the ranked suppliers who meet acceptable levels of economic, environmental and social measures.

A mixed research method of case study and analytical approach is adopted in this research. A fuzzy analytical hierarchical process (FAHP) is applied for ranking of suppliers. Supplier ranks are validated using judgements from multiple decision makers. Purchasing order allocation among the ranked suppliers is determined using cost minimization subject to multiple criteria of economic, environmental and social conditions. A cement manufacturing case example demonstrates and validates the proposed approach.

The research shows that both economic and environmental considerations are significant when suppliers are evaluated for sustainable procurement within the best practice of supply management process. Ranking of suppliers, based on experts’ opinions, indicates varying degrees of importance for each criterion. Adoption of sustainable procurement criteria for evaluating supplier in a cement manufacturing organization is explained by three organizational theories including resource-based, institutional and dynamic capabilities theories. Preferred suppliers from FAHP method are confirmed by judgements from multiple decision-makers. The analysis reveals that purchasing order allocation is different when suppliers are evaluated based on their relative importance and overall ranking.

Currently, individual performance measures and decision-makers are selected from a limited set. The purchasing allocation among ranked suppliers, subjected to cost minimization, incorporates environmental objective of acceptable carbon dioxide emission and social perspective of health and safety of workers, and provides a new approach for dual supplier evaluation and purchasing allocation problem in cement industry. Adopting the proposed supplier evaluation and order allocation approach in practice needs to be guided by the operational principles and an overall methodology which is appropriate for the specific industry with sustainability objectives.

This research enables decision-makers to incorporate sustainability analysis in the supplier evaluation as the basis for best practice with an industry-friendly holistic approach. Using organizational theories, the research re-enforces the importance of not only the energy consumption and environmental management systems of environmental dimension as driving forces/factors from Institutional theory perspective, but also pollution controls and prevention as purchasing capabilities from resource-based theory perspective. The proposed approach is expected to motivate decision-makers to consider sustainable perspectives in supplier evaluation and order allocation processes in a global supply chain and can become a benchmarking tool.

Suppliers’ information on health and safety of their truck drivers are used in order allocation, thus emphasizing the importance of social dimension and encouraging better conditions and benchmarking for delivery drivers.

This paper extends the contribution to the literature by providing guidelines for managers to set strategies, benchmarks and policies within broader sustainable supply chain practices and demonstrates the applicability of the approach using a cement-manufacturing scenario in an emerging economy.

Due to stern management policies and increased community attentiveness, sustainable supply chain management (SSCM) performs a vast component in endeavor operation and production management. Sustainable circular supplier selection (SCSS) and evaluation presented the environmental and social concerns in the fields of circular economy and sustainable supplier selection. Choosing the optimal SCSS is vital for organizations to persuade SSCM, as specified in various researches. Based on the subjectivity of human behavior, the selection of ideal SCSS often involves uncertain information, and the Pythagorean fuzzy sets (PFSs) have a huge capability to tackle strong vagueness, uncertainty and inaccuracy in the multi-criteria decision-making (MCDM) procedure. Here, a framework is developed to assess and establish suitable suppliers in the SSCM and the circular economy.

This paper introduced an extended framework using the evaluation based on distance from average solution (EDAS) with PFSs and implemented it to solve the SCSS in the manufacturing sector. Firstly, the PFSs to handle the uncertain information of decision experts (DEs) is employed. Secondly, a novel divergence measure and parametric score function for calculating the criteria weights are proposed. Thirdly, an extended decision-making approach, known as PF-EDAS, is introduced.

The outcomes and comparative discussion show that the developed method is efficient and capable of facilitating the DEs to choose desirable SCSS. Therefore, the proposed framework can be used by organizations to assess and establish suitable suppliers in the SCSS process in the circular economy.

Selecting the optimal sustainable circular supplier (SCS) in the manufacturing sector is important for organizations to persuade SSCM, as specified in various research. However, corresponding to the subjectivity of human behavior, the selection of the best SCS often involves uncertain information, and the PFSs have a huge capability to tackle strong vagueness, uncertainty and inaccuracy in the MCDM procedure. Hence, manufacturing companies' administrators can implement the developed method to assess and establish suitable suppliers in the SCSS process in the circular economy.

This paper aims to extend a widely used stochastic model of purchase loyalty to include covariates such as demographics, psychographics and geodemographics. Potentially, this allows covariates to explain variations in brand performance measures (BPMs) such as penetration/reach, average purchase frequency, sole buying, share of category requirements, repeat purchase and so forth. The result is to integrate consumer-based segmentation into previously unsegmented stochastic models of brand performance.

This paper describes a model for predicting BPMs. Covariates are then introduced into the model, with discussion of model specification, model estimation, overall model assessment, and the derivation of generalised theoretical BPMs. The outcome is a practical procedure for behavioural loyalty segmentation.

The implications for strategy and management in applying covariates to the BPMs are considerable. Where there are concentrations of consumers with high repeated purchase/consumption, then many aspects of the marketing mix will be affected. An investigation of the role of covariates in understanding BPMs in the laundry detergent market is presented as an example, and ways for market analysts to display results are demonstrated.

Despite the fact that BPMs are the best operationalisation of behavioural loyalty, until now there has not been a model to evaluate the impact of consumer characteristics as covariates on these BPMs. This paper's original contribution includes a model that fits covariates to the BPMs. New statistical and graphical methods are described. Computer software for fitting the model and generating the output is available from the authors.

Organizations are increasingly required to take up extended responsibilities for social and environmental outcomes, including in global value chains. To address these challenges, the organization must call upon stakeholders to engage, contribute, and innovate, and in turn, this requires the organization to have a stronger social basis for its relationships. An integrative model of global value chain management based on social cooperation shifts the focus from corporate reputation to value chain reputation, from a firm-centric view of corporate reputation to a multistakeholder conception of value chain reputation. This approach conceptualizes reputation as a dynamic and potentially vulnerable organizational feature which cannot always be managed by public relations but requires a more stable notion grounded in something more permanent in the organization’s character, history, and the quality of its relationships with stakeholders. We consider the prospects for attending to organizational integrity as a stabilizing force for its public reputation. Integrity may be adopted as a hypernorm for motivating stakeholders who share a concern for the organization’s reputation. Co-creating reputation depends upon a social bond of cooperation developed by stakeholders caring about the organization and in turn, the organization caring about its stakeholders. This socialized understanding of reputation-building is grounded in an ethic of care and manifested through joint purposes, boundary-crossing processes, collaboration practices, and a division of labor into which value chain members are integrated and brought into relation with one another. We propose a model of global value chain management that discusses organizational capabilities required for such an approach.

The expansion of marketing data is encouraging the growing use of deep learning (DL) in marketing. I summarize the intuition behind deep learning and explain the mechanisms of six popular algorithms: three discriminative (convolutional neural network (CNN), recurrent neural network (RNN), and Transformer), two generative (variational autoencoder (VAE) and generative adversarial networks (GAN)), and one RL (DQN). I discuss what marketing problems DL is useful for and what fueled its growth in recent years. I emphasize the power and flexibility of DL for modeling unstructured data when formal theories and knowledge are absent. I also describe future research directions.