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This paper aims to propose that the literature on customer engagement has emphasized the benefits of customer engagement to the firm and, to a large extent, ignored the customers’ perspective. By drawing upon co-creation and other literature, this paper attempts to alleviate this gap by proposing a strategic framework that aligns both the customer and firm perspectives in successfully creating engagement that generates value for both the customer and the bottom line.

A strategic framework is proposed that includes the necessary firm resources, data, process, timeline and goals for engagement, and captures customers’ motives, situational factors and preferred engagement styles.

The authors argue that sustainability of data-driven customer engagement requires a dynamic and iterative value generation process involving customers recognizing the value of engagement behaviours and firm’s ability to capture and passing value back to customers.

This paper proposes a dynamic strategic value-creation framework that comprehensively captures both the customer and firm perspectives to data-driven customer engagement.

Provides a review of the main features of the software available in the market that deal with retention management. Reviews a range of recently published works discussing retention management features and establishes a list of criteria. Reviews the most important and widely available software tools for retention management against these features and makes a comparison. Provides information about each how each criterion is satisfied by the reviewed tools and gives an overview on how the industry approaches retention management. Reveals that the list of the reviewed software is not exhaustive; also, the evaluation could have included more information on the tools and examples of how they can actually be used. Maintains, however, that this is a very useful source of information and impartial advice for everyone interested in retention management. Especially useful is the discussion on the future issues identified. Fulfils an identified information need for a discussion on how existing systems deal with retention management and offers help to an individual who wants to carry out research in this area.

Although the financial markets are regulated by robust systems and rules that control their efficiency and try to protect investors from various manipulation schemes, markets still suffer from frequent attempts to mislead or misinform investors in order to generate illegal profits. The impetus to effectively and systematically address such schemes presents many challenges to academia, industry and relevant authorities. This paper aims to discuss these issues.

The paper describes a case study on fraud detection using data mining techniques that help analysts to identify possible instances of touting based on spam e‐mails. Different data mining techniques such as decision trees, neural networks and linear regression are shown to offer great potential for this emerging domain. The application of these techniques is demonstrated using data from the Pink Sheets market.

Results strongly suggest the cumulative effect of “stock touting” spam e‐mails is key to understanding the patterns of manipulations associated with touting e‐mail campaigns, and that data mining techniques can be used to facilitate fraud investigations of spam e‐mails.

The approach proposed and the paper's findings could be used retroactively to help the relevant authorities and organisations identify abnormal behaviours in the stock market. It could also be used proactively to warn analysts and stockbrokers of possible cases of market abuse.

This research studies the relationships between the cumulative volume of spam touts and a number of financial indicators using different supervised classification techniques. The paper aims to contribute to a better understanding of the market manipulation problem and provide part of a unified framework for the design and analysis of market manipulation systems.

This paper aims to outline the purpose, planning, development and delivery of the “1st Academic-Practitioner Research with Impact workshop: Customer Experience Management (CEM) and Big Data” held at Alliance Manchester Business School on 18th and 19th January 2016, at which four subsequent papers were initially developed.

The paper sets out a summary of the importance and significance of the four papers developed at the workshop and how the co-creative dialogue between managerial practitioners, presenting key problems and issues that they face, and carefully selected teams of academics was facilitated.

To develop richer and more impactful understanding of current problems challenging customer-focused managers, there is a need for more dialogue and engagement between academics and practitioners.

The paper serves as a guideline for developing future workshops that aim at strengthening the links between academia and the business world.

This paper highlights the value of academic–practitioner workshops for focusing academic research on areas of importance for practitioners to generate impact. The innovative format of the workshop and the resulting impactful papers should serve as a call and motivation for future academic–practitioner workshop development.

Bankruptcy is a financial failure of a business or an organization. Different kinds of bankruptcy prediction techniques are proposed to predict it. But, they are restricted as techniques in predicting the bankruptcy and not addressing the associated activities like acquiring the suitable data and delivering the results to the user after processing it. This situation demands to look for a comprehensive solution for predicting bankruptcy with intelligence. The paper aims to discuss these issues.

To model Business Intelligence (BI) solution for BP the concept of reference model is used. A Reference Model for Business Intelligence to Predict Bankruptcy (RMBIPB) is designed by applying unit operations as hierarchical structure with abstract components. The layers of RMBIPB are constructed from the hierarchical structure of the model and the components, which are part of the reference model. In this model, each layer is designed based on the functional requirements of the Business Intelligence System (BIS).

This reference model exhibits the non functional software qualities intended for the appropriate unit operations. It has flexible design in which techniques are selected with minimal effort to conduct the bankruptcy prediction. The same reference model for another domain can be implemented with different kinds of techniques for bankruptcy prediction.

This model is designed using unit operations and the software qualities exhibited by RMBIPB are limited by unit operations. The data set which is applied in RMBIPB is limited to Indian banks.

A comprehensive bankruptcy prediction model using BI with customized reporting.

The current service landscape is increasingly dynamic, and consumers’ engagement in market-related behavior is constantly changing. Developments in technology further influence this continuous dynamism. Therefore, it is important to understand the factors that may cause different engagement valence, especially as only some consumers actively engage in online platforms. The purpose of this paper is to characterize factors that positively and negatively influence consumer engagement and suggest theoretical and managerial implications for the different factors that determine consumer engagement.

This paper conceptualizes factors that influence consumer engagement based on their characteristics (behavioral, emotional, and cognitive) and the type of influence (positive or negative). The study uses customer-dominant logic of service, which focuses on emancipated customers and idiosyncratic customer logic, rather than on provider-orchestrated customer experiences of brands, firms, or offerings. An abductive research approach is used to qualitatively explore consumer engagement in the context of online interest communities.

The study identifies the behavioral, emotional, and cognitive factors that positively and negatively determine consumer engagement in the context of online interest communities.

Through the focus on customer logic, the study provides a detailed and nuanced view of factors that influence consumer engagement. Future research is needed to explore how this framework can be applied to other online communities and different service contexts.

The paper provides insights into the presence of an interest area in consumers’ lives. The study indicates how firms may be involved in consumers’ lives and how firms may create successful customer relationships based on consumer engagement.

This study enhances previous research in four ways: by characterizing factors that determine engagement, paying particular attention to its negatively valenced factors and examining the interplay of the factors that positively and negatively influence engagement, by describing consumers’ connection to the interest area instead of positioning the brand as the link between the consumers and the provider, and by discussing the theoretical and practical challenges associated with understanding and managing consumer engagement.

The proliferation of customer-related data provides companies with numerous service opportunities to create customer value. The purpose of this study is to develop a framework to use this data to provide services.

This study conducted four action research projects on the use of customer-related data for service design with industry and government. Based on these projects, a practical framework was designed, applied, and validated, and was further refined by analyzing relevant service cases and incorporating the service and operations management literature.

The proposed customer process management (CPM) framework suggests steps a service provider can take when providing information to its customers to improve their processes and create more value-in-use by using data related to their processes. The applicability of this framework is illustrated using real examples from the action research projects and relevant literature.

“Using data to advance service” is a critical and timely research topic in the service literature. This study develops an original, specific framework for a company’s use of customer-related data to advance its services and create customer value. Moreover, the four projects with industry and government are early CPM case studies with real data.