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With the growth of organizations and businesses, customer acquisition and retention processes have become more complex in the long run. That is why customer lifetime value (CLV) has become crucial to sales managers. Predicting the CLV is a strategic weapon and competitive advantage in increasing profitability and identifying customers with more splendid profitability and is one of the essential key performance indicators (KPI) used in customer segmentation. Thus, this paper proposes a stacked ensemble learning method, a combination of multiple machine learning methods, for CLV prediction.

In order to utilize customers’ behavioral features for predicting the value of each customer’s CLV, the data of a textile sales company was used as a case study. The proposed stacked ensemble learning method is compared with several popular predictive methods named deep neural networks, bagging support vector regression, light gradient boosting machine, random forest and extreme gradient boosting.

Empirical results indicate that the regression performance of the stacked ensemble learning method outperformed other methods in terms of normalized rooted mean squared error, normalized mean absolute error and coefficient of determination, at 0.248, 0.364 and 0.848, respectively. In addition, the prediction capability of the proposed method improved significantly after optimizing its hyperparameters.

This paper proposes a stacked ensemble learning method as a new method for accurate CLV prediction. The results and comparisons support the robustness and efficiency of the proposed method for CLV prediction.

The purpose of this paper is to explore the prospect of using neurophenomenology to understand, design and test phygital consumer experiences. It aims to clarify interpretivist approaches to consumer neuroscience, wherein theoretical models of individual phenomenology can be combined with modern neuroimaging techniques to detect and interpret the first-person accounts of phygital experiences.

The argument is conceptual in nature, building its position through synthesizing insights from phenomenology, phygital marketing, theoretical neuroscience and other related fields.

Ultimately, the paper presents the argument that interpretivist neuroscience in general, and neurophenomenology specifically, provides a valuable new perspective on phygital marketing experiences. In particular, we argue that the approach to studying first-personal experiences within the phygital domain can be significantly refined by adopting this perspective.

One of the primary goals of this paper is to stimulate a novel approach to interpretivist phygital research, and in doing so, provide a foundation by which the impact of phygital interventions can be empirically tested through neuroscience, and through which future research into this topic can be developed. As such, the success of such an approach is yet untested.

Phygital marketing is distinguished by its focus on the quality of subjective first-personal consumer experiences, but few papers to date have explored how neuroscience can be used as a tool for exploring these inner landscapes. This paper addresses this lacuna by providing a novel perspective on “interpretivist neuroscience” and proposes ways that current neuroscientific models can be used as a practical methodology for addressing these questions.

The purpose of this paper is to explore leadership succession in families in business. Although there is a vast amount of research on leadership succession, no attempt has been made to understand this phenomenon by using an intergenerational learning approach. By applying the Double ABC–X model, the authors discuss how resilience is developed through intergenerational learning during family leadership succession in business.

Based on a single case, the authors define pre- and post-event parameters of the business family under study and use the Double ABC–X Model as an analytical framework. Individual and pair interviews, as well as a family firm workshop, were undertaken following an action research approach using multiple interventions. The qualitative data were collected by reflective journals, field notes and observation protocols. Finally, the authors analyze the data according to a circular deconstruction strategy.

The authors find specific pre-event stressor parameters related to mutual mistrust, independent decision making and non-strategic transmission of power, knowledge and responsibility from predecessor to successor. The intervention based on the intergenerational approach during the post-crisis phase focuses on problem solving and coping within the new situation of co-habitation among the two generations. The intergenerational learning approach based on pile-up of demands, adaptive resources and perception is the source of family adaptation. Additionally, the power of the narrative to reflect past events and project the future seems to the point where the family starts developing resilience.

The way family businesses deal with critical and stressful events during leadership succession may lead to intergenerational learning, which is a source of resilient families. The authors apply the Double ABC–X model to understand family leadership succession in business and further develop it to explain how families develop resilience.