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The concept and practice of e-services has become essential in business transactions. Yet there are still many organizations that have not developed e-services optimally. This is especially relevant in the context of Indonesian Airline companies. Therefore, many airline customers in Indonesia are still in doubt about it, or even do not use it. To fill this gap, this study attempts to develop a model for e-services adoption and empirically examines the factors influencing the airlines customers in Indonesia in using e-services offered by the Indonesian airline companies. Taking six Indonesian airline companies as a case example, the study investigated the antecedents of e-services usage of Indonesian airlines. This study further examined the impacts of motivation on customers in using e-services in the Indonesian context. Another important aim of this study was to investigate how ages, experiences and geographical areas moderate effects of e-services usage.

The study adopts a positivist research paradigm with a two-phase sequential mixed method design involving qualitative and quantitative approaches. An initial research model was first developed based on an extensive literature review, by combining acceptance and use of information technology theories, expectancy theory and the inter-organizational system motivation models. A qualitative field study via semi-structured interviews was then conducted to explore the present state among 15 respondents. The results of the interviews were analysed using content analysis yielding the final model of e-services usage. Eighteen antecedent factors hypotheses and three moderating factors hypotheses and 52-item questionnaire were developed. A focus group discussion of five respondents and a pilot study of 59 respondents resulted in final version of the questionnaire.

In the second phase, the main survey was conducted nationally to collect the research data among Indonesian airline customers who had already used Indonesian airline e-services. A total of 819 valid questionnaires were obtained. The data was then analysed using a partial least square (PLS) based structural equation modelling (SEM) technique to produce the contributions of links in the e-services model (22% of all the variances in e-services usage, 37.8% in intention to use, 46.6% in motivation, 39.2% in outcome expectancy, and 37.7% in effort expectancy). Meanwhile, path coefficients and t-values demonstrated various different influences of antecedent factors towards e-services usage. Additionally, a multi-group analysis based on PLS is employed with mixed results. In the final findings, 14 hypotheses were supported and 7 hypotheses were not supported.

The major findings of this study have confirmed that motivation has the strongest contribution in e-services usage. In addition, motivation affects e-services usage both directly and indirectly through intention-to-use. This study provides contributions to the existing knowledge of e-services models, and practical applications of IT usage. Most importantly, an understanding of antecedents of e-services adoption will provide guidelines for stakeholders in developing better e-services and strategies in order to promote and encourage more customers to use e-services. Finally, the accomplishment of this study can be expanded through possible adaptations in other industries and other geographical contexts.

This chapter presents a summary of the literature on the influence of the Internet and other digital innovations on markets, consumers, and firms. The review leads to a list of topics in need of research in the general areas of big data, online and mobile advertising, consumer search, online privacy, online reviews, social networks, platforms for online transactions, and the impact of the Internet on retail markets, including multi-channel and omni-channel retailing. We discuss the big data approaches that have been applied to problems of targeting and positioning and suggest areas for further development of these approaches. We also discuss the emerging area of mobile advertising, which can further enhance targeting. On the consumer side, the evidence indicates that the Internet has greatly lowered the costs of search and access to retailers. Much of the consumer data are transmitted to sellers, and much of the online advertising is transmitted to consumers, through platforms, such as Google. We conclude that better models of competition among these platform firms are needed and that they need to be examined for anti-trust violations. While online retailing has grown rapidly, it still has a relatively small share of retail sales. Since sellers can combine the advantages of online and offline channels, it has been common for sellers to branch into multi-channel retailing. Given the increased availability of detailed consumer data, omni-channel selling, which emphasizes strategies for the various touchpoints that lead to a transaction, is an area for further development.

Allocating budget optimally to marketing channels is an increasingly difficult venture. This difficulty is compounded by an increase in the number of marketing channels, a rise in siloed data between marketing technologies, and a decrease in individually identifiable data due to legislated privacy policies. The authors explore the rich attribution modeling literature and discuss the different model types and approaches previously used by practitioners and researchers. They also investigate the changing landscape of marketing attribution, discuss the advantages and disadvantages of different data handling approaches (i.e., aggregate vs. individualistic data), and present a research agenda for future attribution research.

New information and communication technologies have transformed the ways businesses communicate and maintain relationships with their customers. In this chapter, we review the main topics in academic research pertaining to digital marketing and relationship perspectives. The discussion involves email and website marketing, search engine marketing, user-generated content, co-creation marketing, social media marketing, prosumption marketing in the sharing economy, immersive marketing and AI-based marketing. On this basis, the chapter proposes some future research directions including influencer marketing, live streaming, social service enhancement and the dark side of social media marketing, consumer-to-consumer interactions on two-sided platforms, psychological mechanisms related to the use of mixed realities, and the experience of AI-enabled service automation and relevant ethical issues. Furthermore, emerging technologies such as the Internet of things (IoT) and blockchain are expected to be the next breakthrough in marketing, which warrant further research to examine their impact on the customer-firm relationship. It is suggested the researchers could focus on the adoption and application of blockchain technology, participative marketing via IoT, as well as the privacy and data protection issues related to these technologies. This chapter provides a comprehensive picture of the connection between digital marketing and relationship marketing in academic research.

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.

This study investigates the competencies required for quality management professionals to meet the needs of industry 4.0. The authors use a case study strategy at an electronics manufacturer in southern Malaysia, to adapt their role to be relevant in the industry 4.0 environment. In doing so, this study answers the following four questions: (1) How are the changing technological trends expected to impact the future role of quality in industry 4.0? (2) What are the competencies gap between current and future roles of quality professionals? (3) What are the views and practices related to quality roles? (4) How can the gaps identified be closed to meet the quality challenges of industry 4.0?

The research methods consist of a comprehensive review of literature on the technological trends towards industry 4.0 and the impact on the role of quality and competence that may be required in the future, as well as internal document review on the current roles of quality professionals in an electronics manufacturer in southern Malaysia, to identify the competence gap. Empirical data was collected based on surveys conducted on 64 quality professionals with a response rate of 96.88%. Interviews were conducted on three decision-makers from critical areas in the electronics manufacturer for viewpoints from three different perspectives: finance, operations and talent development.

Quality professionals will require technical competencies to interpret large amounts of data from processes to make strategic decisions, the use of new AR tools and be aware of data security risks. Methodological competencies will be required to use data to identify the source of problems, to access reliable sources of learning and the ability to use new tools for solving complex problems efficiently. Social competencies will be required in communications across multi-sites, suppliers and customers in new collaborative virtual platforms, with the ability to retain tacit and explicit knowledge, in a decentralized environment that will require leadership ability to make decisions. Personal competencies required will be the ability to work in a flexible workplace and time and more frequent work-related changes.

The limitation of the study is based on what the authors currently know of the future, which may not be much for the quality professionals in the electronics manufacturer, who have not been exposed much to the technology yet. The potential for the future landscape to change dramatically with rapid technology changes may also result in a different set of skills for future quality professionals. The quality professionals who were involved in this study were the quality executives, engineers and managers, irrespective of their gender, age, length of service and experience in the field of quality. Therefore, these variables were not taken into consideration for this research.

This research helped to identify the role of quality in industry 4.0 and key competencies that the quality professionals in the electronics manufacturer will require to adapt to their role in industry 4.0. However, based on the questionnaire and the interview comments of key personnel, it can be concluded that quality professionals lack awareness of their new roles in industry 4.0. This could be due to the fact that the new technology is not implemented by quality professionals but by the innovation team based in Singapore headquarters, as was also advised by the operations head.

The benefit of industry 4.0 technology is clearly shown by Philips's new Dutch factory with robotized technology that was able to produce the same output with one-tenth of the workers of its China factory (Rifkin, 2014, chapter 8). Rojko (2017, p. 80) also shared a similar view that industry 4.0 is expected to reduce production costs by 10–30%, logistics costs by 10–30% and quality management costs by 10–20%. The importance of this research can be seen from the findings of “The Future of Jobs” (2018, p. 22), which suggests that the window of opportunity for organizations to leverage the new technology to re-skill is within the period of 2018–2022, in order to enable employees to reach full potential in the high value-added tasks. The electronics manufacturer may need to keep to this timeline to maintain its competitive advantage.

The purpose of this paper was to determine the competence gap of current quality professionals in the electronics manufacturer with the competencies required in industry 4.0. This led to the third objective, to identify the views of stakeholders based on the propositions derived from the gaps identified, to triangulate the findings, to conclude the competency gaps of the current quality professionals in the electronics manufacturer. Finally, the objective of this paper was to make a recommendation on how to prepare the quality professionals in the electronics manufacturer for their role in industry 4.0. The research identified the technical, methodological, social and personal competencies gap of the quality professionals in the electronics manufacturer by looking at the changes expected in industry 4.0 from four aspects, factory (people and process), business, product and customers.

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.