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Entrepreneurs and their ventures are often portrayed as unambiguously positive forces in society. Specifically, high technology and equity-funded startups are heralded for their innovative products and services that are believed to alter the economic, social, and even political fabric of life in advantageous ways. This paper draws on established theory on the causes of misconduct in and by organizations to elaborate the factors that can give rise to misconduct in entrepreneurial ventures, illustrating our arguments with case material on both widely known and less well-known instances of entrepreneurial misconduct. In venturing into the dark side of entrepreneurship, we hope to contribute to theory on entrepreneurship and organizational misconduct, augment entrepreneurship pedagogy, and offer ideas and examples that can enhance entrepreneurs’ awareness of their susceptibility to wrongdoing.

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 provides an overview of the strategic implications of chatbot use and implementation, including potential applications in marketing, and factors affecting customer acceptance. After presenting a brief history and a classification of conversational artificial intelligence (AI) and chatbots, the authors provide an in-depth review at the crossroads between marketing, business, and human–computer interaction, to outline the main factors that drive users' perceptions and acceptance of chatbots. In particular, the authors describe technology-related factors and chatbot design characteristics, such as anthropomorphism, gender, identity, and emotional design; context-related factors, such as the product type, task orientation, and consumption contexts; and users-related factors such as sociodemographic and psychographic characteristics. Next, the authors detail the strategic importance of chatbots in the field of marketing and their impact on consumers' perceived service quality, satisfaction, trust, and loyalty. After discussing the ethical implications related to chatbots implementation, the authors conclude with an exploration of future opportunities and potential strategies related to new generative AI technologies, such as ChatGPT. Throughout the chapter, the authors offer theoretical insights and practical implications for incorporating conversational AI into marketing strategies.

This chapter explored how people and technology are managed in the workplace. It examines how data and data analytics in AI, human resource information system (HRIS), learning content management systems, learning management systems, and talent management software have become major components of human resource and workforce development. Middle-skill, low-skill, and disadvantaged employees are being asked to use their knowledge, skills, and abilities (KSAs) to evaluate and understand technology systems, technology resources, and equipment in the workplace. HRD and HRM professionals must understand the competencies and resources needed to achieve harmony and balance between people and technology use in the workplace.

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.

Tourism activity is a global industry and, as such, it is subject to global risks. International travel has developed exponentially over the last few decades. At the same time, diseases have increased their geographical spread influenced by ecologic, genetic and human factors. Currently, the increasing virus, epidemic and pandemic outbreaks represent some of the most negative consequences of globalization, causing deaths and significant economic losses due to the negative impacts they have on the tourism industry, one of the sectors that have been the most affected by health crises.

This work presents insights on the epidemics, pandemics and virus outbreaks that have occurred throughout the twenty-first century and how those occurrences have affected the tourism industry and the global economy. A brief literature review on health risks in tourism is presented, followed by a clinical perspective to help people understand the differences between endemics, outbreaks, epidemics and pandemics. Then, the study offers a presentation of the most significant pandemics in recent human history and a deep analysis of the COVID-19 disease. Finally, the effects that the different pandemics, epidemics and outbreaks that occurred in the present century had on tourism are explained, and the challenges tourism has to face are presented and discussed.

The current study is an extension of the PF model research program that began after the Great Depression in the early 1930s. The purpose of the study is to introduce political dimensions to the PF defense-growth model and to assess the impact of political and conflict variables on EG. The study theorizes that excluding political factors from the PF defense-growth model hampers any realistic explanation of the problems of EG; that the influences of economic and military variables and their externalities effects vary across different political contexts; that political factors are at least as important as economic factors in determining the outcome of EG; that intrastate and interstate conflicts have differential effects on EG (both types of conflicts have negative effects on EG; however, intrastate conflicts have more damaging effects on growth than do interstate conflicts); and that the impact of conflicts on EG differs across regions.