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The research aims to investigate how individuals can be persuaded to make purchases through repeated and personalized messages. Specifically, the study proposes a framework of the potential benefits and risks of the online behavioral and data-driven digital advertising (OBA), which can help researchers and practitioners to better understand shopping behavior in the online retailing setting. In addition, the research focuses on the role of privacy concerns in affecting avoidance or adoption of OBA.

The authors apply a structural equation modeling (SEM) approach with partial least square (PLS) regression method to test the research hypotheses through data coming from a structured questionnaire.

OBA is a controversial type of advertising that activates opposing reactions on consumers' perspective. Specifically, acceptance of the OBA is positively related to relevance, usefulness and credibility of the personalized advertisements, while the intention to avoid personalized ads is strictly related to the privacy concerns. Consequently, OBA acceptance and avoidance affected the click intention on the ad and the behavioral intention that are decisive for the success of data-driven digital advertising.

Prior research came up with complex theoretical frameworks that explain antecedents of OBA focusing only on ethical issues in marketing, on the effectiveness of a single OBA campaign or on how to create a successful advertising campaign. However, no study focuses on the intended or actual behavior of shoppers. Specifically, filling the gap in the existing literature, our research applies an SEM approach to identify both benefits and risks and the antecedents of the actual behavior of individuals in terms of actual purchases promoted by OBA.

With the developments of the digital era and considerable developments in ICT, advertisers are leveraging data-driven forms of online advertising to target consumers individually. The present study integrates privacy concerns with the constructs from the persuasion knowledge model to investigate consumer avoidance of online behavioural advertising (OBA).

The study employed an online survey method for data collection using a sample size of 345. Reliability and validity of the measurement scales were ensured, and hypotheses developed were tested through PLS-SEM using SMART PLS.

The results show that persuasion knowledge is a significant predictor of perceived benefits, perceived risks and privacy concerns. Also, privacy concern was found to significantly mediate persuasion knowledge-avoidance behaviour and perceived risk-avoidance behaviour. On the other hand, the perceived benefit was not found significant in influencing privacy concerns for OBA.

The present study is one of the initial attempts to understand the level of knowledge Indian consumers hold about OBA and how they evaluate and respond to these data-driven forms of advertising. The current study helps advance knowledge of the field and the theories used. Future studies might look at the effect of various demographic and psychographic aspects on consumer avoidance of OBA.

As the country is shifting to digital, it becomes really important to understand the privacy concerns that people perceive in regard with the current advertising practices.

Regulatory initiatives and related technological shifts have been imposing restrictions on data-driven marketing (DDM) practices. This paper aims to find the main restrictions for DDM and the key management theories applied to investigate the consequences of these restrictions.

The authors conducted a unified bibliometric analysis with 104 publications retrieved from both Scopus and Web of Science, followed by a qualitative, in-depth systematic literature review to identify the management theories in literature and inform a research agenda.

The fragmentation of the research outcomes was overcome by the identification of 3 main clusters and 11 management theories that structured 18 questions for future research.

To the best of the authors’ knowledge, this paper sets for the first time a frontier between almost three decades where DDM evolved with no significative restrictions, grounded on innovations and market autoregulation, and an era where data privacy, anti-trust and competition and data sovereignty regulations converge to impose structural changes, requiring scholars and practitioners to rethink the roles of data at the strategic level of the firm.

In the current business environment, more uncertain than ever before, understanding consumer behavior is an integral part of an organization's strategic planning and execution process. It is the key driver for becoming a market leader. Therefore, it is important that all processes in business are customer centric. Marketers need to harness big data by engaging in data driven-marketing (DDM) to help organizations choose the “right” customers, to “keep” and “grow” them and to sustain “growth” and “profitability”. This research examines DDM adoption practices and how companies can aim to enhance shareholder value by bringing about “customer centricity”.

An online survey conducted in 2016 received 180 responses from junior, middle and senior executives. Of the total responses, 26% were from senior management, 39% from middle management and the remaining 35% from junior management. Industries represented in the survey included retail, BFSI, healthcare and government, automobile, telecommunication, transport and logistics and IT. Other industries represented were aviation, marketing research and consulting, hospitality, advertising and media and human resource.

Success of DDM depends upon how well an organization embraces the practice. The first and foremost indicator of an organization's commitment is the extent of resources invested for DDM. Respondents were divided into four categories; Laggards, Dabblers, Contenders and Leaders based on their “current level of investments” and “willingness to enhance investments” soon.

With storming digital age and the development of analytics, the process of decision-making has gained significant importance. Judgment and intuition too are critical to the process. Choosing an appropriate action cannot be done strictly on a rational basis.

The results of the study offer interesting implications for managing the growing sea of data. An iterative and incremental approach is the need of the hour, even if it has to start with baby steps, to invest in and reap the fruits of DDM. The intention to use any system is always dependent on two primary belief factors: perceived usefulness and perceived ease of use; however, attitudes and social factors are equally important.

There is a dearth of knowledge with regards to who is and is not adopting DDM, and how best big data can be harnessed for enhancing effectiveness and efficiency of marketing budget. It is, therefore, imperative to build a knowledge base on DDM practices, challenges and opportunities. Better use of data can help companies enhance shareholder value by bringing about “customer centricity”.

Despite many years of academic research into organisational integration and effectiveness, organisations still struggle to successfully implement strategy and achieve competitive advantage. However, the rapid evolution of marketing technologies such as big data, marketing analytics, artificial intelligence and personalised consumer interactions offer potential for an integrated marketing communication technological capability that aligns and integrates an organisation. Programmatic advertising is one such integrated marketing communication (IMC) technology capability, applying and learning from customer information and behaviours to align and integrate organisational activity. The literature on programmatic is embryonic and a conceptual framework that links its potential to organisational effectiveness is timely. This paper aims to develop a framework showing the potential for programmatic advertising as an IMC technology capability to enhance organisational integration and performance.

An exploratory methodology gained insight from 15 depth interviews with senior marketing executives from both organisations and external advertising agencies.

Four elements of a programmatic integrated organisation were identified and aligned with seven marketing activity levers to deliver firm performance measures.

This research contributes to theory, affirming IMC as a capability and positioning programmatic as a means of organisational integration.

The model also offers guidance for practitioners looking to integrate programmatic into their organisation.

To the best of the authors’ knowledge, this is the first paper to look at programmatic from an IMC perspective and as a means of organisational integration. It is also the first to apply Moorman and Day’s (2016) model to explore organisational integration and programmatic, developing a new model, specifically contextualised for programmatic advertising.

Big data and analytics make digital communications more effective, but little is known about how institutional pressures shape data-driven communications. These pressures determine and constrain how, what, when and to whom practitioners should communicate. This empirical study explores how institutional forces influence the use of data in guiding digital communications. The paper identifies factors that impact communications and shape practitioner views on particular tools in their day-to-day work.

This study uses a qualitative exploratory approach with in-depth interviews of 15 Australian communication practitioners through the lens of neo-institutional theory. Thematic analysis was applied to identify three main themes.

Communications professionals disclosed how they were influenced by coercive institutional forces such as ambiguous data privacy regulations, normative forces that shaped ethical concerns, professionalism and various challenges, and mimetic forces that determined shared methods and implementation of digital communications technologies such as analytics. Furthermore, the authors reveal how analytics – tools typically associated with uncertainty and mimetic influences – exert coercive pressures that could lead to misguided decision-making.

This study’s findings highlight the need for practitioners to learn more about the inner workings of analytics tools and for managers to determine if the perceived benefits of these solutions outweigh any undesirable effects.

The study contributes to extant research on digitalization in strategic communication by providing new insights into practitioner views and challenges with digital communications technologies.

Despite the considerable effects of institutional pressures, this study is the first to explore the impacts of data-driven communications at the level of individual practitioners. The paper advances neo-institutional theory in public relations (PR), strategic communication and corporate communications at the micro level.

This study aims to contribute a comprehensive historical analysis of account planning in scholarly literature, tracing its origins in agency adoption through transformation amid today’s rapidly changing advertising environment.

The paper applies a historical literature review of advertising account planning literature spanning over a half-century. The analysis focuses on dual dimensions of chronological evolution and thematic content analysis to trace the development of key constructs and their changes over time.

The analysis sheds light on the origins of account planning as a discipline and factors influencing its practice among agencies. Three distinct chronological stages of development are identified, and four dominant construct themes evident across time. Analysis indicates that traditional account planning was well formed functionally in the 1990s and peaked in prominence around the turn of the new millennium. Of late, however, advances in account planning theory appear limited and the future fragmented.

This study provides a unique historical perspective tracing the chronological development and key constructs of account planning. Implications for the future of the planning discipline are uncertain as agencies innovate with new functional roles seeking to unlock consumer insights and creative opportunities in the digital advertising environment.

Digital advertising enables retailers to rely on large volumes of data on consumers and even leverage artificial intelligence (AI) to target consumers online with personalised and context-aware advertisements. One recent example of such advertisements is programmatic advertising (PA), which is facilitated by automatic bidding systems. Given that retailers are expected to increase their use of PA in the future, further insights on the pros and cons of PA are required. This paper aims to enhance the understanding of the implications of PA use for retailers.

A theoretical overview is conducted that compares PA to traditional advertising, with an empirical investigation into consumer attitudes towards PA (an online survey of 189 consumers using an experimental design) and a research agenda.

Consumer attitudes towards PA are positively related to attitudes towards the retailer. Further, perceived ad relevance is positively related to attitudes towards PA, which is moderated by (1) consumer perceptions of risks related to sharing their data with retailers online and (2) consumer perceptions of AI's positive potential. Surprisingly, the disclosed use of AI for PA does not significantly influence consumer attitudes towards PA.

This paper contributes to the literature on technology-enabled services by empirically demonstrating that ad relevance drives consumer attitudes towards PA. This paper further examines two contingencies: risk beliefs related to data (i.e. the source of PA) and perceptions of AI (i.e. the somewhat nebulous technology associated with PA) as beneficial. A research agenda illuminates central topics to guide future research on PA in retailing.

The purpose of this research is to investigate how to introduce a financing scheme to tackle the manufacturer's capital constraint problem, discuss the effects of data-driven marketing (DDM) quality, cross-channel-return (CCR) rate and financing interest rate on the members' pricing and delivery-lead-time decisions and optimal performances, and analyzes `how to achieve the coordination within a dual-channel supply chain (DSC) by contract coordination.

This work establishes a DSC model with DDM, and the offline retailer can provide internal financing to the capital-constrained online manufacturer. The demand under the price is determined based on DDM quality, customer channel preference and delivery lead time. Then, combined with the Stackelberg game, the optimal pricing and delivery-lead-time decisions are discussed under the inconsistent and consistent pricing strategies with decentralized and centralized systems. Furthermore, it designs a manufacturer-revenue sharing contract to coordinate the members under the two pricing strategies.

(1) The increase of DDM quality will reduce the delivery-lead-time under the inconsistent or consistent pricing strategy and will push the selling prices; (2) The growth of the CCR rate will raise selling prices and extend the delivery-lead-time under the decentralized decision; (3) Under price competition, the offline selling price is higher than the online selling price when customers prefer the offline channel and vice versa; (4) The retailer and the manufacturer can achieve a win-win situation through a manufacturer-revenue sharing contract.

This paper contributes to the studies related to DSC by investigating pricing and delivery-lead-time decisions based on DDM, CCR, internal financing and supply chain contract and proposes some managerial implications.