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This study explores the generation and use of competitive intelligence (CI) within the buyer–seller exchange process and its influence on salesperson performance. Using the concept of social capital as a theoretical foundation and multilevel data collected at three time points from 686 customer–salesperson dyads, the authors empirically test a conceptual framework that proposes both antecedents and consequences of CI sharing between customer and salesperson. The results of the study demonstrate that CI sharing by customers is a function of salesperson customer orientation, customer-centric extra-role behaviors, and relationship quality. CI sharing translates into increased perceived value, share-of-wallet, and profit margins when the salesperson utilizes the information to position and differentiate his or her product; however this occurs only when the salesperson has strong adaptive selling skills. Surprisingly, CI negatively influences these outcomes among low-adaptive salespeople, indicating that CI can actually work to a firm’s disadvantage if the salesperson is not equipped to respond to it. These findings suggest that CI must be examined differently than general market knowledge and that firms may leverage CI to their tactical advantage at the salesperson–customer interface if managed effectively.

Recent years have seen persistent tension between proponents of big data analytics, using new forms of digital data to make computational and statistical claims about ‘the social’, and many sociologists sceptical about the value of big data, its associated methods and claims to knowledge. We seek to move beyond this, taking inspiration from a mode of argumentation pursued by Piketty, Putnam and Wilkinson and Pickett that we label ‘symphonic social science’. This bears both striking similarities and significant differences to the big data paradigm and – as such – offers the potential to do big data analytics differently. This offers value to those already working with big data – for whom the difficulties of making useful and sustainable claims about the social are increasingly apparent – and to sociologists, offering a mode of practice that might shape big data analytics for the future.

Purpose The purpose of this paper is to scrutinize and classify the literature linking Big Data analytics and management phenomena. Design/methodology/approach An objective bibliometric analysis is conducted, supported by subjective assessments based on the studies focused on the intertwining of Big Data analytics and management fields. Specifically, deeper descriptive statistics and document co-citation analysis are provided. Findings From the document co-citation analysis and its evaluation, four clusters depicting literature linking Big Data analytics and management phenomena are revealed: theoretical development of Big Data analytics; management transition to Big Data analytics; Big Data analytics and firm resources, capabilities and performance; and Big Data analytics for supply chain management. Originality/value To the best of the authors’ knowledge, this is one of the first attempts to comprehend the research streams which, over time, have paved the way to the intersection between Big Data analytics and management fields.

The age of big data is now coming. But the traditional data analytics may not be able to handle such large quantities of data. The question that arises now is, how to develop a high performance platform to efficiently analyze big data and how to design an appropriate mining algorithm to find the useful things from big data. To deeply discuss this issue, this paper begins with a brief introduction to data analytics, followed by the discussions of big data analytics. Some important open issues and further research directions will also be presented for the next step of big data analytics.

The authors develop a conceptual framework for conditions under which news of a major customer data breach at a U.S. retail firm is likely to decrease other U.S. retailers’ shareholder value. Using the massive data breach at Target Corporation as their empirical context, and an event study of 168 publicly listed U.S. retailers as their methodology, the authors find considerable support for their framework. Results indicate that the Target data breach resulted in negative abnormal returns for other U.S. retailers, and that the strength of this contagion effect was moderated by factors related to retailers’ (a) size and product market similarity with Target, (b) governance-related tie-strength with Target, (c) information technology-related ability to prevent a similar breach, (d) marketing ability to respond effectively in the aftermath of a similar breach, and (e) corporate social responsibility. The authors show that although a major retail data breach may result in an intra-industry spillover, managers can use factors related to information technology, marketing, and corporate social responsibility to help insulate their firms from this contagion effect.

As a complement to the transformative marketing framework presented in Kumar, in this commentary, the author proposes a conceptual framework delineating the relationships between a firm's customer information assets, information analysis capabilities, customer knowledge, marketing strategy, and performance in a transformative landscape. Common to both frameworks are the outcomes of marketing strategy effectiveness and efficiency. Data resources, unlocking their potential, and generating insights from them in Kumar's framework overlap with customer information assets, information analysis capabilities, and customer knowledge, respectively, in the proposed framework. The principal focus of the proposed framework is the digital data-rich environment as a transformative force in marketing.

Today's omnichannel business models incorporate physical and digital touchpoints interacting with customers. A hyper-personalization strategy relies on the organization's capability to gather and transform customer data into personalized experiences; therefore, when a hyper-personalization organizational plan is put in place, it serves two main functions: to deliver personalized experiences and increase the number of customers receiving such experiences. For this to happen, four elements are required for a hyper-personalization strategy: data foundation, decisions, design, and distribution. While customer master data management relies on the correct identification of a customer, a real customer insight can only be achieved when three types of customer data are gathered: Identity, Contactability, and Traceability (I, C, T)- fulfilling the first element of a hyper-strategy. This article aims to identify the benefits in the total number of customers that can receive a hyper-personalization strategy when real-time touchpoints are linked to a customer Master Data Management that integrates the three types of customer data.

A seller often needs to determine the amount of product information to provide to consumers. We model costly consumer information search in the presence of limited information. We derive the consumer’s optimal stopping rule for the search process. We find that, in general, there is an intermediate amount of information that maximizes the likelihood of purchase. If too much information is provided, some of it is not as useful for the purchase decision, the average informativeness per search occasion is too low, and consumers end up choosing not to purchase the product. If too little information is provided, consumers may end up not having sufficient information to decide to purchase the product. The optimal amount of information increases with the consumer’s ex ante valuation of the product, because with greater ex ante valuation by the consumer, the firm wants to offer sufficient information for the consumer to be less likely to run out of information to check. One can also show that there is an intermediate amount of information that maximizes the consumer’s expected utility from the search problem (social welfare under some assumptions). Furthermore, this amount may be smaller than that which maximizes the probability of purchase; that is, the market outcome may lead to information overload with respect to the social welfare optimum. This paper can be seen as providing conditions under which too much information may hurt consumer decision making. Numerical analysis shows also that if consumers can choose to some extent which attributes to search through (but not perfectly), or if the firm can structure the information searched by consumers, the amount of information that maximizes the probability of purchase increases, but is close to the amount of information that maximizes the probability of purchase when the consumer cannot costlessly choose which attributes to search through.

With advances in tracking and database technologies, firms are increasingly able to understand their customers and translate this understanding into products and services that appeal to them. Technologies such as collaborative filtering, data mining, and click-stream analysis enable firms to customize their offerings at the individual level. While there has been a lot of hype about web personalization recently, our understanding of its effectiveness is far from conclusive. Drawing on the elaboration likelihood model (ELM) literature, this research takes the view that the interaction between a firm and its customers is one of communicating a persuasive message to the customers driven by business objectives. In particular, we examine three major elements of a web personalization strategy: level of preference matching, recommendation set size, and sorting cue. These elements can be manipulated by a firm in implementing its personalization strategy. This research also investigates a personal disposition, need for cognition, which plays a role in assessing the effectiveness of web personalization. Research hypotheses are tested using 1,000 subjects in three field experiments based on a ring-tone download website. Our findings indicate the saliency of these variables in different stages of the persuasion process. Theoretical and practical implications of the findings are discussed.

The purpose of this paper is to analyze the effect of privacy and perceived security on the level of trust shown by the consumer in the internet. It also aims to reveal and test the close relationship between the trust in a web site and the degree of loyalty to it.