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Considering the cognitive challenges associated with a data-rich business environment, this research aims to investigate the relationship between data literacy (DL), perceived data overload (PDO), and technostress (TS), besides the effect of these constructs on professional's individual performance (IP).

Through survey research, the authors collected data from 321 professionals who work in data-rich and highly technological business environments. To test the hypotheses proposed, the authors developed the partial least squares structural equation modeling (PLS-SEM) procedures.

The results showed that DL is positively associated with IP and negatively with PDO. PDO is positively associated with TS and negatively with IP. The authors found no significant negative association between TS and IP.

With this research, the authors seek to contribute to the gap in the literature concerning two cognitive challenges associated with data-rich business environments: PDO and TS, analyzing from the point of view of the individual, and highlighting the importance of DL in this context.

The results can assist managers in effectively being concerned with the DL level of their workforce. This is important considering not only the professionals' IP but also the cognitive challenges such as PDO and TS.

The innovation of this study lies in the empirical analysis of DL in the business context and its relationship with two cognitive challenges inherent in data-rich environments: PDO, and TS. Besides, the authors highlight the importance of understanding such phenomena in terms of IP.

Few well-documented studies have explained the importance of researching firms' marketing analytics capability (FMAC). In spite of its significance, there is scant attention to conceptualising and empirically investigating FMAC and its consequences in a data-driven business context. Thus, this study aims to develop and test a conceptual model that relates FMAC and its repercussions in the data-rich business environment.

This study analysed the data from 250 managers amongst large and medium-sized manufacturing and service-intensive firms. Furthermore, this research performed an empirical study by using operationalised questionnaire survey method to verify the hypotheses and reach its theoretical and managerial implications. Structural equation modelling with maximum-likelihood estimation method was applied to verify the validity of the proposed research model.

Multivariate analysis results show that FMAC significantly influences firms' competitive marketing performance (FCMP) with the presence of holistic marketing decision-making (HMDM) as a mediator. Moreover, adoption of artificial intelligence (AAI) enhances the relationship of FMAC-HMDM and FMAC-FCMP linkages.

This study analyses how FMAC can enhance FCMP and contributes to resource-based views and technological capability theories. From a managerial perspective, guidelines are provided for marketers to adopt advance technologies, such as AI, to optimise FMAC and HMDM to achieve competitive marketing performance.

Believing that “how to be competitive in marketing performance under data-rich-environment”, this research is the first to use the data of a firm manager to facilitate the understanding of FMAC, which provides a new direction for improving marketing performance. In addition, HMDM and AAI are also proposed for firms to optimise FCMP.

Over the last 50 years, increased attention for personalization paved the way for one-to-one marketing efforts, but firms struggle to deliver on this promise. The purpose of this manuscript is to provide a complete picture on personalization, develop a future research agenda and put forth concrete advice on how to move the field forward from a theoretical, methodological, contextual, and practical viewpoint.

This research follows a systematic literature review process, providing an in-depth analysis of 135 articles (covering 184 studies) to distill the (1) key building blocks and components of personalization and (2) theoretical, contextual, and methodological aspects of the studies.

This manuscript uncovers six personalization components that can be linked to two personalization building blocks: (1) learning: manner, transparency, and timing and (2) tailoring: touchpoints, level, and dynamics. For each of these components, the authors propose future research avenues to stimulate personalization research that accounts for challenges in today's data-rich environments (e.g. data privacy, dealing with new data types). A theoretical, contextual, and methodological (i.e. industry, country and personalization object) review of the selected studies leads to a set of concrete recommendations for future work: account for heterogeneity, embed theoretical perspectives, infuse methodological innovation, adopt appropriate evaluation metrics, and deal with legal/ethical challenges in data-rich environments. Finally, several managerial implications are put forth to support practitioners in their personalization efforts.

This research provides an integration of personalization research beyond existing and outdated review papers. Doing so, it accounts for the impact of new technologies and Artificial Intelligence and aims to advance the next generation of knowledge development on personalization.

Over the last few years, the European Central Bank (ECB) has adopted unconventional monetary policies. These measures aim to boost economic growth and increase inflation through the bond market. The purpose of this paper is to study the impact of the ECB’s quantitative easing (QE) on the investor’s behavior in the stock market.

First, the authors theoretically identify the transmission channels of the QE shocks to the stock market. Then, the authors empirically assess the financial market’s responses to QE shocks in a data-rich environment using a factor augmented VAR (FAVAR).

The results show that the ECB’s unconventional monetary policy positively affects the stock market. A QE shock leads to an increase in stock prices and a drop in the realized volatility and the implied risk premium. The authors also suggest that the ECB’s QE is transmitted to the stock market through five main channels: the liquidity, the expectation, the portfolio reallocation, the interest rates and the risk premium channels.

The findings help to better understand the behavior of stock market assets in a data-rich economic context and guide investors and policymakers in the presence of unconventional monetary tools. For instance, decision-makers and investors should consider the short-term effect of the QE interventions and the changing behavior of the financial actors over time. In addition, high stock market returns can increase risk appetite. This can lead investors to underestimate the market risk. Decision-makers and market participants should take into consideration the impact of the large injection of money through the QE, which may raise the risk of a speculative bubble in the financial market.

To the best of the authors’ knowledge, this is the first study that incorporates a theoretical and empirical analysis to explore QE transmission to the stock market in the European context. Unlike previous studies, the authors use the shadow rate proposed by Wu and Xia (2017) to quantify the effect of the ECB’s QE in a data-rich environment. The authors also include two key risk indicators – the stock market risk premium and the realized volatility – to capture investors’ behavior in the stock market following QE shocks.

The marketing information system (MkIS) in the data-rich business environment receives all the attention these days, but as essential and perhaps even more essential is the marketing information system management capability (MkISMC). Although many service firms apprehend the return from MkIS, others clearly struggle. It seems that MkIS management capability dynamics and their direct/indirect holistic influence on service firm's competitive performance (SFCP) are unsolved in the current data-driven service economy. This study aims to conceptualize a model and test the antecedents on service firms' competitive performance.

This study utilizes a survey of a sizeable sample of service firms’ managers at the firm level. A total of 250 useable responses were obtained and analyzed through structural equation modeling.

Results reveal that variables under their respective direct influences are positively and significantly related. Interestingly, MkISMC has a relatively large magnitude of positive and direct effects on service firms' competitive performance. The other variables, such as the use of marketing analytics (UMAN), service innovation and management (SINM), partially mediate the effect of MkISMC on the competitive performance of service firms.

The findings inform practitioners that MkISMC, UMAN and SINM play a vital role in attaining service firms' competitive performance in the data-rich environment. Overall, it deepens the understanding of the mediation effect of UMAN and SINM of service firms on competitive performance.

The study advances theoretical understanding of resource-based view (RBV), market orientation and dynamic capability that formulate the relationship of MkISMC, UMAN and SINM in attaining SFCP in the ever-changing data-driven business economy.

The purpose of this paper is to demonstrate the value of enriched customer data for analytical customer relationship management (CRM) in the insurance sector. In this study, online quotes from an insurer’s website are evaluated in terms of serving as a trigger event to predict churn, retention, and cross-selling.

For this purpose, the records of online quotes from a Swiss insurer are linked to records of existing customers from 2012 to 2015. Based on the data from automobile and home insurance policyholders, random forest prediction models for classification are fitted.

Enhancing traditional customer data with such additional information substantially boosts the accuracy for predicting future purchases. The models identify customers who have a high probability of adjusting their insurance coverage.

The findings of the study imply that enriching traditional customer data with online quotes yields a valuable approach to predicting purchase behavior. Moreover, the quote data provide supplementary features that contribute to improving prediction performance.

This study highlights the importance of selecting the relevant data sources to target the right customers at the right time and to thus benefit from analytical CRM practices.

This paper is one of the first to investigate the potential value of data-rich environments for insurers and their customers. It provides insights on how to identify relevant customers for ensuing marketing activities efficiently and thus avoiding irrelevant offers. Hence, the study creates value for insurers as well as customers.

Healthcare facility managers work in complex and dynamic environments where critical decisions are constantly made. Providing them with enhanced decision support systems would result in a positive impact on the productivity and success of the projects they undertake, as well as the sustainability of critical healthcare infrastructure. The purpose of this paper is to propose a conceptual ambient intelligent environment for enhancing the decision-making process of the facility managers. This low-cost data-rich environment would use building information modeling (BIM) and mobile augmented reality (MAR) as technological bases for the natural human-computer interfaces and aerial drones as technological tools.

This paper presents a scenario for the integration of augmented reality (AR) and building information modeling (BIM) to build an ambient intelligent (AmI) environment for facility managers where mobile, natural, user interfaces would provide the users with required data to facilitate their critical decision-making process. The technological requirements for having such an intelligent environment are also discussed.

The proposed BIM-MAR-based approach is capable of enhancing maintenance related practices for facility managers who are mobile to integrate with their facilities' intelligent environment. This approach is also capable of providing a collaborative environment in which different stakeholders, across geographically distributed areas, could work together to solve facility management tasks.

In this paper ambient intelligence will be considered for the first time in the area of healthcare facility management practices to provide facility managers with an intelligent BIM-based environment to access facility information and consequently enhance their decision-making process.

In the era of Big Data, larger volumes of data arrive in various forms at an increasing pace but of questionable quality and value. The abundant information (that emanates from these 5Vs – volume, variety, velocity, veracity, and value) taxes the bounded capacity of managers. This chapter introduces a taxonomy of approaches available for strategic decision making in an information-rich environment, several of which showcase that automation can help to augment (not supplant) managerial decision making. This taxonomy is then applied to an innovation context. Mapping a stylized version of the phases of the innovation process (i.e., front-end innovation, new product development, commercialization) onto the four decision-making approaches yields an organizing framework for understanding strategic decision making in the realm of innovation. The chapter concludes by identifying promising areas for future research.

This conceptual chapter: (1) explicates the foundational terminology regarding strategic decision making in a marketing context; (2) provides a primer on the era of Big Data and making strategic decisions in an information-rich environment; (3) introduces a taxonomy, which features approaches to decision making in an information-rich environment; and (4) applies the taxonomy in an innovation context to yield an organizing framework.

This chapter focuses on the nascent field that is emerging at the intersection of innovation, marketing strategy, and information-rich environments, and breaks new ground by exploring automation available to aid managerial decision making in this realm.

The main practical implication is to elucidate that managers can apply different approaches to decision making in today’s information-rich environment. Tables 2–4 provide to managers 12 examples of the types of decision making in an innovation context.

This chapter introduces a new taxonomy to classify four approaches for making strategic decisions in an information-rich environment, and extends that framework to the innovation realm. This framework aims to prompt researchers to explore important topics that exist at the intersection of innovation, marketing strategy, and managerial decision making in an information-rich environment.

The purpose of this paper is to explore the potential of the substitution, augmentation, modification and redefinition (SAMR) model as a research method via a project that aimed to investigate pre-service teachers' negotiation of data-rich environments.

The project that underpins this paper saw the author working with a group of seven pre-service teachers through an online learning community on WhatsApp to explore their understanding of increasingly complex computational topics associated with the “Implications and Impacts” component of the digital technologies curriculum.

The knowledge and experiences of the pre-service teachers suggest considerable unexplored potential through the alignment of the SAMR model in online learning communities.

The paper draws attention to a range of opportunities associated with prompting discussion about increasingly complex topics in online learning communities. The adoption of the SAMR model offers a framework in education and the social sciences. It stresses the affordances enabled due to the personalized, ubiquitous and situated nature of the method. Unique contributions include the interpretive and critical approaches discussed in terms of research methods. The paper may be of value to researchers who are interested in a scaffolded means to engage with participants through an online learning community.

Knowledge management (KM) has emerged in recent times as a phenomenon with wide‐ranging implications for organizational innovation and competitiveness. Supporters argue that as organizations understand the value of KM, they have the opportunity to establish long‐term internal strengths, which will lead to external competitive advantage. Further, we find the current literature advocates that KM can be implemented in every organizational discipline. KM is approached from several different perspectives, and a number of these are used to structure our paper and identify emerging factors in: strategy, human resources management (HRM), information technology (IT), total quality management (TQM), and marketing. This paper presents a summary of key responses to a recent survey of FTSE 100 companies conducted by the authors, which shows that KM is an extremely popular management topic, yet relatively few organizations have serious implementation programs in place. Also presented are findings from longitudinal studies of six case organizations, which have been approaching and deploying KM over the last three years. The academic arguments for organizations to be proactive in KM are strong and compelling. Our research identifies the critical factors that respondents feel are vital for successful KM implementation, and these provide a basis for a further stage of the study which considers how best to develop appropriate performance measurements.