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This study aims to discover the legal borderline between licit online marketing and illicit privacy-intrusive and manipulative marketing, considering in particular consumers’ expectations of privacy.

A doctrinal legal research methodology is applied throughout with reference to the relevant legislative frameworks. In particular, this study analyzes the European Union (EU) data protection law [General Data Protection Regulation (GDPR)] framework (as it is one of the most advanced privacy laws in the world, with strong extra-territorial impact in other countries and consequent risks of high fines), as compared to privacy scholarship on the field and extract a compliance framework for marketers.

The GDPR is a solid compliance framework that can help to distinguish licit marketing from illicit one. It brings clarity through four legal tests: fairness test, lawfulness test, significant effect test and the high-risk test. The performance of these tests can be beneficial to consumers and marketers in particular considering that meeting consumers’ expectation of privacy can enhance their trust. A solution for marketers to respect and leverage consumers’ privacy expectations is twofold: enhancing critical transparency and avoiding the exploitation of individual vulnerabilities.

This study is limited to the European legal framework scenario and to theoretical analysis. Further research is necessary to investigate other legal frameworks and to prove this model in practice, measuring not only the consumers’ expectation of privacy in different contexts but also the practical managerial implications of the four GDPR tests for marketers.

This study originally contextualizes the most recent privacy scholarship on online manipulation within the EU legal framework, proposing an easy and accessible four-step test and twofold solution for marketers. Such a test might be beneficial both for marketers and for consumers’ expectations of privacy.

Purpose: This chapter sets out to lay out and analyse the effectiveness of the General Data Protection Regulation (GDPR), a recently established European Union (EU) regulation, in the local insurance industry.

Methodology: This was done through a systematic literature review to determine what has already been done and then a survey as a primary research tool to gather information. The survey was aimed at clients and employees of insurance entities.

Findings: The general results are that effectiveness can be segmented into different factors and vary regarding the respondents’ confidence. Other findings include that the GDPR has increased costs, and its expectations are unclear. These findings suggest that although the GDPR was influential in the insurance market, some issues about this regulation still exist.

Conclusions: GDPR fulfils its purposes; however, the implementation process of this regulation can be facilitated if better guidelines are issued for entities to follow to understand its expectations better and follow the law and fulfil its purposes most efficiently.

Practical implications: These conclusions imply that the GDPR can be improved in the future. Overall, as a regulation, it is suitable for the different member states of the EU, including small states like Malta.

Many companies have developed corporate database systems and/or management information systems, some have established intelligence units. However, the systems often fail to scan the external environment to which the companies are exposed, and fail to yield meaningful intelligence, thus overwhelming managers with internal data. Argues that to address the information needs of top marketing managers, the environmental factors that have immediate effects on the company′s operation should be identified. Computer‐based information systems could play an important role in collecting environmental information from various sources; however, in interpreting data and disseminating intelligence to marketing managers, a human‐computer mixed system is essentially required. The system would be used in conjunction with a corporate database system to present intelligence to marketing managers, so as to keep them instantly informed, reinforce their knowledge learning, and support their decisions. Highlights the critical factors for developing and utilizing such systems. The approach adopted is to examine managers′ working style and decision processing, to develop a framework of environmental scanning, human‐computer‐based data filtering, interpreting, and intelligence reporting system with natural interface.

The financial services industries is facing a number of important legal developments. The most publicised of these is the coming into force on 1st March, 2000 of the new Data Protection Act 1998. This is going to have a profound impact on the way in which businesses process personal data. It will, for the first time ever, apply to personal data stored in certain manual files and it will also regulate trans‐border data flows and restrict the processing of a special category of sensitive personal data. Information is the lifeblood of any business. There is no doubt that this Act is going to regulate the flow of that information and all its uses. Other developments in the pipeline include the proposed European Union Directive on the distance selling of financial services to consumers and the various legislative provisions which are being put in place to deal with the huge surge in on‐line transactions.

The librarian and researcher have to be able to uncover specific articles in their areas of interest. This Bibliography is designed to help. Volume IV, like Volume III, contains features to help the reader to retrieve relevant literature from MCB University Press' considerable output. Each entry within has been indexed according to author(s) and the Fifth Edition of the SCIMP/SCAMP Thesaurus. The latter thus provides a full subject index to facilitate rapid retrieval. Each article or book is assigned its own unique number and this is used in both the subject and author index. This Volume indexes 29 journals indicating the depth, coverage and expansion of MCB's portfolio.

This article aims firstly to show how the introduction of new computer hardware can bring considerably more power and control to data processing for marketing researchers. Secondly, it discusses the use of basic statistical techniques as an aid to effective marketing decision making. Thirdly, it comments on the effective use of multidimensional and mutivariate techniques. It concludes that, given that the major expense in marketing research is often data collection, it is essential that data analysis be effectively used to unveil the true marketing implications of the findings.

The main purpose of this paper is to review the related literature and propose a new decision‐support‐system (DSS) framework for marketing in the business‐to‐business (B2B) arena based on customer‐relationship management (CRM) and knowledge‐driven marketing to help related‐field graduate students and marketing managers.

Reviews a range of the most important works published between 1966 and 2004 in order to demonstrate both practical and theoretical aspects. The main method of this research is analytical and conceptual and the approach to this subject was to investigate the gap between marketing DSSs and analytical CRM.

Provides information about a customized marketing DSS in a B2B context, indicates related literature and frameworks and, finally, tests the ideas with a case study.

Outcomes and applications are identified for developing new activities in improving marketing decision making and marketing planning based on customer orientation and customer satisfaction.

Despite such interdependencies, the research in the fields of DSSs and CRM solutions has not adequately considered the integration of such systems. The novel contribution of this paper lies in integrating marketing DSSs and CRM with regard to knowledge‐driven marketing in B2B marketing, in both theoretical and practical aspects.

The paper aims to analyze the potential of artificial intelligence (AI) in enhancing marketing capabilities for Indian fast-moving consumer goods (FMCG) firms, with appropriate emphasis on dynamic capabilities (DC). By integrating AI into their operations, companies can enhance diverse marketing practices, leading to improved efficiency and effectiveness. In addition, the study offers valuable insights and guidance for effectively implementing AI in marketing strategies.

In-depth interviews of 26 seasoned professionals from various positions in the Indian FMCG sector were conducted for the study. A systematic approach of coding that includes open, axial and selective coding was made to recognize the themes that represent AI-enabled marketing practices. This approach ensured that the themes were precisely identified and comprehensively analyzed. Interviews followed by a rigorous coding process provide valuable insights into the practical implementation of AI-enabled marketing practices for the Indian FMCG industry.

The study’s results underscore the importance of AI in enhancing marketing practices for FMCG firms, particularly in four critical areas: personalization, consumer engagement, marketing automation and strategic goals. By adopting AI in these areas, Indian FMCG firms can significantly enhance their marketing capabilities, increasing efficiency, effectiveness and gaining a competitive advantage.

The study uses qualitative data analysis to explain how DC through AI technologies can enhance digital marketing practices for Indian FMCG organizations. Furthermore, the study provides valuable insights into how AI technologies can improve marketing capabilities and emphasizes the importance of DC. The study also proposes a framework for AI-enabled Marketing 4.0 that can guide FMCG firms seeking to leverage AI technologies to enhance their marketing practices.

This study outlines the potential for enhancing marketing practices by adapting AI in the Marketing 4.0 environment for Indian FMCG companies, highlighting the importance of DC in achieving this goal.

The purpose of this paper is to analyze the effects of data-driven capabilities on supply chain integration (SCI) and competitive performance of firms in the food and beverages (F & B) industry in Pakistan.

The authors adopt the structural equation modeling approach to test the proposed hypotheses using AMOS 23. Survey data were collected from 240 firms in the F & B industry in Pakistan.

The results revealed that SCI (i.e. internal integration (II) and external integration (EI)) significantly mediates the effect of data-driven capabilities (i.e. flexible information technology resources and data assimilation) on a firm’s competitive performance. In addition to the direct effects, II also has an indirect effect on competitive performance through EI.

The study has several implications for managers in the context of big data application in food supply chain management (FSCM) in a developing country context. The study posits that firms can achieve excellence in performance by governing data-driven supply chain operations. The study also has implications for distributors and importers in the F & B industry. The cloud-based sharing of data can improve the operational performance of channel members while reducing their overall cost of operations. In practice, food franchises largely get the advantage of shared resources of their suppliers in managing orders, payments, inventory and after-sales services.

The study is novel and deepens the understanding about the use of big data in FSCM keeping in view the industry trends and stakeholder’s priorities in a developing country context.

Few developments in recent years have had as great an impact on the practice of physical distribution and materials management as the introduction of computers. Many physical distribution activities (e.g., order‐processing, storage and retrieval systems and so forth) have undergone profound changes because of automation. But a distribution system which focuses on only one particular logistical activity is too restrictive to be very useful. Developing a computer‐based model requires a co‐ordinated team effort which calls upon personnel in accounting, data processing, marketing, operations research, production, traffic and other functional specialities. Hence, we will examine the role of computer‐based logistics models in marketing strategies and present some directions for future logistics modelling efforts.