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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.

The extant literature provides much-needed support to understand marketing accountability and how marketing actions are related to financial performance (FP). However, we have limited understanding of the relationships between marketing actions and firms' social performance (SP) and environmental performance (EP). Understanding these links is critical to enhancing sustainable FP, SP, and EP. Moreover, the literature provides limited understanding of the measures by which SP and EP may be operationalized, or the data necessary to reach a conclusion. This study bridges these gaps by extensively reviewing the extant literature to offer a set of measures and data sources to operationalize SP and EP, and empirically show their relationships with marketing actions. We find that greenhouse gas (GHG) emission, environmental disclosure score, waste reduction, energy consumption, and recycling are prominent measures of EP, and that social disclosure score, philanthropy or community spending, and diversity of gender and race are prominent measures of SP. The KLD, ASSET4, and Bloomberg are prominent sources of data that can be used to operationalize SP, to which CDP may be added for EP. We also show that marketing actions positively affect EP and SP. This study contributes to the extant literature on SP and EP by identifying measures and data sources and linking marketing actions to both performance types. It contributes to policy development by identifying the importance of EP and SP and how marketing actions can help achieve such performance.

This chapter provides a comprehensive review of research and developments relating to the use of Web 2.0 technologies in education. As opposed to early educational uses of the Internet involving publication of static information on web pages, Web 2.0 tools offer a host of opportunities for educators to provide more interactive, collaborative, and creative online learning experiences for students. The chapter starts by defining Web 2.0 tools in terms of their ability to facilitate online creation, editing, and sharing of web content. A typology of Web 2.0 technologies is presented to illustrate the wide variety of tools at teachers’ disposal. Educational uses of Web 2.0 technologies such as wikis, blogs, and microblogging are explored, in order to showcase the variety of designs that can be utilized. Based on a review of the research literature the educational benefits of using Web 2.0 technologies are outlined, including their ability to facilitate communication, collaborative knowledge building, student-centered activity, and vicarious learning. Similarly, issues surrounding the use of Web 2.0 tools are distilled from the literature and discussed, such as the possibility of technical problems, collaboration difficulties, and plagiarism. Two case studies involving the use Web 2.0 tools to support personalized learning and small group collaboration are detailed to exemplify design possibilities in greater detail. Finally, design recommendations for learning and teaching using Web 2.0 are presented, again based on findings from the research literature.

We analyze the impact of monetary policy instruments on interbank lending rates and retail bank lending in China using an extended version of the model developed by Porter and Xu (2009). Unlike the central banks of advanced economies, the People’s Bank of China (PBoC) uses changes in the required reserve ratios and open market operations to influence liquidity in money markets and adjusts the regulated deposit and lending rates and loan targets to intervene in the retail deposit and lending market. These interventions prevent the interbank lending rate from signaling monetary policy stance and transmitting the effect of policy to the growth of bank loans. Since the global financial crisis, the PBoC’s monetary policy has gone through a full cycle. The combining effects of using different policy instruments simultaneously within a short period of time were quite effective in bringing the credit and money growth in line with its desired level. Most recently steps have been taken to speed up the interest rate liberalization. Effective July 2013, the PBoC removed the floors of the benchmark lending rates.

Small businesses are dominant in most economies and their owners likely experience high levels of distress. However, we have not fully explored how these common businesses meaningfully differ with respect to the stress process. Understanding the meaningful variations or subgroups (i.e., heterogeneity) in the small business population will advance occupational health psychology, both in research and practice (e.g., Schonfeld, 2017; Stephan, 2018). To systematize these efforts, the author identifies five commonly appearing “heterogeneity factors” from the literature as modifiers of stressors or the stress process among small business owners. These five heterogeneity factors include: owner centrality, individual differences, gender differences, business/ownership type, and time. After synthesizing the research corresponding to each of these five factors, the author offers specific suggestions for identifying and incorporating relevant heterogeneity factors in future investigations of small business owners’ stress. The author closes by discussing implications for advancing occupational health theories.

This research aimed to determine the mediating effect of entrepreneurial self-efficacy on the relationship between entrepreneurial passion and leadership styles. Two hundred hardware and footwear entrepreneurs from the Las Malvinas Commercial Emporium, Lima, Peru were surveyed. The partial least squares structural equation modelling (PLS-SEM) multivariate statistical technique was applied for data analysis. It was found that there is a complementary mediating effect of entrepreneurial self-efficacy in the relationship between entrepreneurial passion and leadership styles. On the other hand, it was concluded that entrepreneurial passion has a positive influence on entrepreneurial self-efficacy. Likewise, there is a positive influence of entrepreneurial self-efficacy on leadership styles, and there is a positive influence of entrepreneurial passion for leadership styles. This research contributed theoretically to the academic literature and provided empirical evidence of the relationship of the proposed variables; it proved a new predictive and explanatory structural model that can now be used in future research worldwide and generated a model of the studied variables that are useful for both academia and the business world.

Although most of the traditional economic theories used to explain investor behaviour assume full investor rationality, experience proves otherwise. In this study, we examine a number of important determinants of risk appetite and tolerance, including gender, education and knowledge of financial services and loss aversion. We do this by designing a questionnaire to measure the level of risk appetite and tolerance, together with various demographic characteristics and loss aversion. The survey is administered among a random sample of 1,648 respondents from Turkey. We find that on average men exhibit a higher level of risk appetite and tolerance than women, thus suggesting a clear gender divide in terms of investment behaviour. Furthermore, knowledge of financial services is associated with higher levels of both risk appetite and tolerance, with younger respondents also exhibiting similar patterns of behaviour. By contrast, loss-averse respondents are less open and tolerant of risk, which indicates that behavioural biases such as loss aversion can have a meaningful impact on investment decisions, at odds with traditional models based on rational choice theory. The findings have a number of important implications for investment behaviour and education, including the importance of gender balance within investment committees in order to ensure a more even distribution of risk behaviour, as well as the need to incorporate considerations related to loss aversion in all aspects of policymaking and regulation.