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This study aims to investigate the factors that influence South African customers to potentially switch from one bank to another. Instead of using established models and survey techniques, the research measured social media sentiment to measure threats to switch.

The research involved a 12-month analysis of social media sentiment, specifically customer threats to switch banks (churn). These threats were then analysed for co-occurring themes to provide data on the reasons customers were making these threats. The study used over 1.7 million social media posts and focused on all five major South African retail banks (essentially the entire sector).

This study concluded that seven factors are most significant in understanding the underlying causes of churn. These are turnaround time, accusations of unethical behaviour, billing or payments, telephonic interactions, branches or stores, fraud or scams and unresponsiveness.

This study is unique in its measurement of unsolicited social media sentiment as opposed to most churn-related research that uses survey- or customer-data-based methods. In addition, this study observed the sentiment of customers from all major retail banks across 12 months. To date, no studies on retail bank churn theory have provided such an extensive perspective. The findings contribute to Susan Keaveney’s churn theory and provide a new measurement of switching threat through social media sentiment analysis.

The insurance industry has not been able to effectively retain its customers and struggles to establish and maintain long-lasting relationships with them. The purpose of this paper is thus to identify the main factors that explain the cancellation of motor insurance policies by individual customers, considering the influence of intermediaries on their decisions.

The data used in this research is based on a sample of 3,500 insurance policies that lapsed during the period of analysis between January and July 2017, against another sample of 3,500 policies that did not lapse, from a major insurance company in Portugal. Binary logistic regression was used for data analysis, using IBM SPSS software.

Aggressive tactics by insurance companies for customer acquisition may induce the cancellation of insurance policies. More valuable customers, the policies with higher premiums and recent claims, as well as the ancillary intermediaries and agents, are determinants of insurance cancellation. Conversely, the payment of policies by direct debit and without instalments reduces the probability of cancellations.

The main limitation of this study is the restriction on data access. Insurance companies are significantly resistant to sharing their customer data – including with academic researchers – even in an anonymised form.

The paper highlights internal and external practices of insurance companies that should be reformulated to significantly improve their performance regarding product cancellation, related to customer information management, mistrust behaviours related to stakeholders and new value propositions that deepen the relationships with intermediaries.

This research developed a framework with which to identify the factors that are mainly associated with motor insurance cancellation and to predict its likelihood.

保險業一向未能有效地留住客戶，故業內不斷努力建立並維繫與客戶長久的關係。因此、本文擬確定個人客戶終止其汽車保單的因素；研究過程中、會考慮中介人對客戶作有關決定的影響 。

本研究所使用的數據、為基於一個涵蓋3500份期滿終止保單的樣本、並以之與另一涵蓋3500份沒有終止保單的樣本相比，樣本均來自葡萄牙一家大型保險公司。期滿終止保單之樣本、乃於2017年1月至同年7月這個研究進行期間内期滿終止的。研究使用了二元羅吉斯迴歸來分析數據，並應用了IBM SPSS 數據分析軟件。

保險公司為贏得客戶所採用的積極進取策略可能導致客戶終止其保單。保單終止的決定因素包括更多有價值的客戶、保險費較高的保單、最近的索賠和輔助的中介及代理。反之，以自動扣帳方式及以非分期付款形式繳付保單則會減低終止保單的機會。

本研究發展了一個框架、以確定與客戶終止其保單有關聯的主要因素，並預計客戶終止保單的可能性。本文亦強調了保險公司須重新制定其內部與外部的做法，以能大幅改善其與產品取消有關的績效，而這些產品取消涉及客戶信息管理、與持份者有關的不信任行為、以及能深化與中介人之關係的新價值主張。

This study aims to investigate customers’ churning out of Internet Protocol Television (IPTV) service, one of the most prevalent forms of IT convergence.

Based on the review of current literature, a research model is introduced to depict the effects of select independent variables on customer churning behavior. First of all, the two groups are compared in terms of predictor variables, including switching barriers, voice of customer (VOC), membership period and degree of contents usage. Then, a curvilinear regression was applied to understand the association relationship between the level of IPTV contents usage and variables of switching barriers, VOC and membership period. Third, a logit regression was performed to predict customer churning through the variables of switching barriers, VOC, membership period and level of IPTV contents usage.

Through the empirical analysis, this study analyzed the factors affecting customer churning behavior of IPTV service providers based on switching barriers, VOC and contents usage.

Although several studies on IPTV have been undertaken globally, they have largely depended on self-reporting surveys to examine dynamics between antecedent variables and IPTV performance in terms of customer satisfaction, usage intension and customer retention. This empirical study is performed to understand influential factors of IPTV service defection through the weblog analysis of 3,906 service users, who represented both service defectors and non-defectors during a specific month.

This paper aims to make it convenient for those who have only just begun their research into Community Question Answering (CQA) expert recommendation, and for those who are already concerned with this issue, to ease the extension of our understanding with future research.

In this paper, keywords such as “CQA”, “Social Question Answering”, “expert recommendation”, “question routing” and “expert finding” are used to search major digital libraries. The final sample includes a list of 83 relevant articles authored in academia as well as industry that have been published from January 1, 2008 to March 1, 2019.

This study proposes a comprehensive framework to categorize extant studies into three broad areas of CQA expert recommendation research: understanding profile modeling, recommendation approaches and recommendation system impacts.

This paper focuses on discussing and sorting out the key research issues from these three research genres. Finally, it was found that conflicting and contradictory research results and research gaps in the existing research, and then put forward the urgent research topics.

This paper aims to contribute to the understanding of the mechanisms that evolve during reputational scandals and lead to changes in industry regulation. It explores the processes by which a demand for external industry regulation evolves, also addressing the consequences of firms’ competitive behaviors which lead to substantial misbehavior and the destruction of reputational capital. The authors are interested in whether and how regulatory activities – in the case analyzed here, changes in insurance regulation regarding sales commissions for insurance brokers – are used as a costly, external behavioral control mechanism (third-loop learning) to terminate a reputational scandal that cannot be stopped by internal controls at a firm level (first-loop and second-loop learning) anymore.

The paper explores a real-life case in the German insurance industry that peaked in 2012 and has been well documented by broad media coverage, complemented by interviews with leading industry representatives. Using causal process tracing as a methodology, the authors study the factors in the case that led to an industry scandal. The authors further analyze why the insurance firms involved were not able to limit the scandal’s impact by internally controlling their behaviors, but had to call for external regulation, thus imposing costly restrictions on sales and contract processes. To identify the mechanisms underlying this result, theories from the fields of economics (game theory) and sociology (vicious cycle of bureaucracies), as well as organizational learning theory, are used.

The authors find that individual rationality does not suffice to prevent insurance firms from scandalous business practices, e.g. via implementing appropriate internal behavioral control measures within their organizations. If, as a result, misbehavior leads to reputational scandals, and the destruction of reputational capital spills over to the whole industry, a vicious cycle is set in motion which can be terminated by regulation as an externally enforced control mechanism.

This study is limited to the analysis of a single case study, combining published materials, e.g. broad media coverage, with interviews from representatives of the insurance industry. Nevertheless, the underlying mechanisms that have been identified can be used in other case studies as well.

The paper shows that if firms want to avoid increasing regulation, they must implement strong reputational risk management (RRM) to counteract short-term profit pressure and to avoid restrictive regulation imposed on the industry as a whole. Furthermore, it sheds light on the relevance of spillover effects for RRM, as not only employee behavior within an organization might lead to the destruction of reputational capital but also that from other firms, e.g. from elsewhere within an industry.

The paper contributes by emphasizing a direct causal link between corporate scandals, loss of reputation and regulatory change within the insurance industry. Furthermore, the paper contributes by combining economic theories with organizational theories to understand real-life phenomena.

With Industry 4.0 and the extensive rise of smart technologies, we are seeing remarkable transformations in work practices and workplaces. Scholars report the phenomenal progress of smart technologies. At the same time, we can hear the rhetoric emphasising their potential threats. This study focusses on how and where intelligent machines are leveraged in the workplace, how humans co-working with intelligent machines are affected and what they believe can be done to mitigate the risks of the increased use of intelligent machines.

We conducted in-depth interviews with 15 respondents working in various leadership capacities associated with intelligent machines and technologies. Using NVivo, we coded and churned out the themes from the qualitative data collected.

This study shows how intelligent machines are leveraged across different industries, ranging from chatbots, intelligent sensors, cognitive systems and computer vision to the replica of the entire human being. They are used end-to-end in the value chain, increasing productivity, complementing human workers’ skillsets and augmenting decisions made by human workers. Human workers experience a blend of positive and negative emotions whilst co-working with intelligent machines, which influences their job satisfaction level. Organisations adopt several anticipatory strategies, like transforming into a learning organisation, identifying futuristic technologies and upskilling their human workers, regularly conducting social learning events and designing accelerated career paths to embrace intelligent technologies.

This study seeks to understand the emotional and practical implications of the use of intelligent machines by humans and how both entities can integrate and complement each other. These insights can help organisations and employees understand what future workplaces and practices will look like and how to remain relevant in this transformation.

The purpose of the study was to ascertain the influence of innovation conviction, innovation mindset and innovation creed on the performance of women entrepreneurs in South African small and medium enterprises and their capacity for innovation. The study also examined how proactive personality and entrepreneurial education moderate the relationship between innovative capability and women entrepreneurs' performance.

The study used a quantitative research design and administered a questionnaire to collect data from participants. Since there was no sampling frame available, purposive sampling, a non-probability sampling technique, was used to select suitable respondents who were identified as entrepreneurial women. Data were collected from 304 women entrepreneurs in the Gauteng province of South Africa. The data were analyzed using smart partial least squares.

The findings demonstrated that innovation conviction, innovation mindset and innovation creed have a positive impact on innovation capability. It was also discovered that innovation capability, proactive personality and entrepreneurial education all positively and significantly impact women entrepreneurs' performance. Furthermore, the results showed that entrepreneurial education and proactive personality had a positive and significant moderating effect on the nexus between innovation capability and the performance of women entrepreneurs.

This study will add to the body of knowledge on women's small business management and entrepreneurship in Africa, two topics that are typically ignored by academics in developing nations.

Artificial intelligence (AI) has started to receive attention in the field of digital entrepreneurship. However, few studies propose AI-based models aimed at assisting entrepreneurs in their day-to-day operations. In addition, extant models from the product design literature, while technically promising, fail to propose methods suitable for opportunity development with high level of uncertainty. This study develops and tests a predictive model that provides entrepreneurs with a digital infrastructure for automated testing. Such an approach aims at harnessing AI-based predictive technologies while keeping the ability to respond to the unexpected.

Based on effectuation theory, this study identifies an AI-based, predictive phase in the “build-measure-learn” loop of Lean startup. The predictive component, based on recommendation algorithm techniques, is integrated into a framework that considers both prediction (causal) and controlled (effectual) logics of action. The performance of the so-called active learning build-measure-predict-learn algorithm is evaluated on a data set collected from a case study.

The results show that the algorithm can predict the desirability level of newly implemented product design decisions (PDDs) in the context of a digital product. The main advantages, in addition to the prediction performance, are the ability to detect cases where predictions are likely to be less precise and an easy-to-assess indicator for product design desirability. The model is found to deal with uncertainty in a threefold way: epistemological expansion through accelerated data gathering, ontological reduction of uncertainty by revealing prior “unknown unknowns” and methodological scaffolding, as the framework accommodates both predictive (causal) and controlled (effectual) practices.

Research about using AI in entrepreneurship is still in a nascent stage. This paper can serve as a starting point for new research on predictive techniques and AI-based infrastructures aiming to support digital entrepreneurs in their day-to-day operations. This work can also encourage theoretical developments, building on effectuation and causation, to better understand Lean startup practices, especially when supported by digital infrastructures accelerating the entrepreneurial process.