Special Issue in Journal of Business Research
Leveraging User Behavior and Data Science Technologies for Management
Guest Editors: L. Blasco-Arcas, A. Reyes, M. L. Alcañiz Raya, and M. Kastanakis
31 December 2020 31 March 2021
Call for Paper
Increased access to large amounts of freely available or proprietary data has created massive commercial opportunities nowadays, effectively becoming a key strategic capability for companies able to leverage them. Specifically, a proliferation of various kinds of digital data provides companies an ever-increasing source of consumer data that can be used to inform and improve managerial decision-making (George et al., 2014; Erevelles et al., 2016). Following this, a continuous development of more agile and powerful analytical tools facilitates companies to better exploit such datasets and derive previously unattainable insights from a wide variety of data sources (Sivarajah et al., 2017; Van Auken, 2015).
Looking at users’ behaviour – digital data creation is exponentially increasing. On the one hand, consumption habits and daily activities of millions of consumers revolve around technology, mobile devices, apps, digital platforms and social networks. Despite security concerns and privacy issues, it has become a common and acceptable behavior for people to share information and create content – generally known as User Generated Contentor abbreviated UGC (Chevalier and Mayzlin, 2006; Godes and Mayzlin, 2004; Van Dijck, 2009 – related to their individual experiences, feelings, opinions and, eventually, behaviour. On the other, the availability of a wide number of tracking technologies (cookies, sensors, beacons etc.), allows the collection of a significant amount of behavioral data of these users – known as User Generated Behavior or abbreviated UGB (Netzer et al., 2014) – in both digital and physical environments (McAfee et al., 2012; Vanhala et al., 2020).
UGC constitutes data that has been intentionally created by consumers themselves, typically in digital media. Web 2.0 development has, over the years, facilitated this type of data creation in different forms such as, for example, the publication of comments in social media (Villi and Matikainen, 2016; Saura et al., 2019), product reviews in websites (Zhu and Zhang, 2010), or participation in online brand communities (Schau et al., 2009; Zaglia, 2013). This voluntary and intentional data creation from consumers allows companies to not only gain better insights into them but, in addition, to foster and benefit from other participative behaviors such as the co-creation of products and services in digital media (Blasco-Arcas et al., 2014, 2016; Ramaswamy and Gouillart, 2010).
However, consumers not only create data with their own will but they also generate several other kinds of data unintentionally, as their activities – in both digital and physical environments – leave behavioral traces that companies are able to track, collect and analyze with current technologies. UGB data collection in the real world has been growing because sensors, beacons, mobile applications and other tracking technologies can monitor, store and analyze the presence and activity of these consumers in physical spaces; while digital environments are also becoming more sophisticated and immersive thanks to technologies such as Augmented Reality (AR) or Virtual Reality (VR) (Alcañiz et al., 2018; Flavian et al., 2019; Martinez-Navarro et al, 2019).
From an organizational and managerial perspective – both UGC and UGB have increased companies’ access to a wide number of, previously not available, unstructured raw data sources. These, in turn, allow the most forward-acting organizations to stay ahead of others by designing and exploiting new strategies that create sustainable competitive advantages. To benefit from that trend, companies are adopting novel and sophisticated ways of obtaining, processing and analyzing large amounts of data to improve their understanding of and better exploit consumer behavior (Balducci and Marinova, 2018; Reyes-Menendez et al. 2019; Vanhala et al., 2020). Emerging computer science methodologies and analytical techniques such as data-mining and text analysis (Berger et al., 2020; Humphreys and Wang, 2018; Villaroel Ordenes et al., 2017, 2019), machine learning (Hartmann et al., 2019; Vermeer et al., 2019) and neuro-marketing (Ariely and Berns, 2010; Cascio et al. 2015; Plassmann et al. 2012) – to name a few – increasingly gain importance in the managerial toolkit compared to traditional, and occasionally obsolete, market research techniques.
Because such types of unstructured data are now a sizable source of novel insights for management science, the question of how to collect, process and analyze this gained traction and become a central pursuit in various business and marketing disciplines and their respective sub-fields (Balducci and Marinova, 2018, Alcañiz et al., 2018). For example, UGC analysis has benefited from the development and application of computer science methods, mainly revolving around text analysis (for an extensive review on this topic, see Humphreys and Wang, 2018). However, there is a pressing need for further methodological development based on other behavioral, cognitive and emotional approaches (Balducci and Marinova, 2018) such as, indicatively, sentiment analysis, text mining, semantic analysis, and Natural Language Processing (NLP). These can aid managers to comprehend at a deeper level not only the (evident surface level of) text but, importantly, the context and the intention (Marín-Morales et al., 2018) of consumers’ interactions and, therefore, gain a better understanding on how new technologies can work synergistically to improve both managerial sense-making and consumer experience and protection: one example would be the – frequently hyped – blockchain technology that could help to protect the copyright of the content created.
Further looking at the analysis of UGB, one can see several emerging, advanced techniques that allow researchers to obtain and analyze non-intentionally generated consumer data in its various forms (e.g. geospatial positioning, video-tracking, voice, facial and gestural cues, etc.). Some of them can be used experimentally in controlled environments via Neuromarketing techniques such as Eye Tracking, fMRI or Electroencephalogram (EEG) to explore unconscious cognitive processing and consumers’ behavioral responses (Ariely and Berns, 2010; Cascio et al. 2015). Importantly, the application of data science to marketing and management decisions has also bolstered the development and use of predictive models, to support decision-making and a better understanding of future consumer behavior (Erevelles et al., 2020; Junqué de Fortuny et al. 2013; Martens et al. 2016).
While user generated data – UGC and UGB – constitute great opportunities for organizations to gather superior insights in increasingly competitive markets, their competent use and application is far from clear and entails several challenges:
– A deeper understanding of people’s interactions in the context of UGC and UGB is needed. For example, one challenge is to understand how other consumers’ behavioral traces may influence cognitive processing and responses those interacting with them; it has been long documented (Kastanakis and Balabanis, 2011) that people are sensitive to context and effects such as either social proof or going against the crowd. Moreover, there are numerous ethical and privacy concerns that need attention in order to make use of proper data-driven practices that do not alienate, intrude into people’s lives, deceive or otherwise harm consumers, having a negative impact on business outcomes;
– It remains a challenge how to effectively integrate different data sources (both UGC and UGB). For example, UGC analysis has focused on analyzing and integrating text data insights, but less emphasis has been placed in analyzing other data forms including images, emojis, and verbal and nonverbal facial cues (Balducci and Marinova, 2018). UGB largely remains an unexplored area and a better understanding is needed on how organizations can aid decision-making by effectively using implicit metrics and integrating behavioral data in different processes;
– From a scholarly perspective, there is a lack of conceptual frameworks that could provide a multidisciplinary and integrated overview of this emerging area. User behavior analysis requires a multidisciplinary approach, that begs to integrate perspectives from diverse areas such as computer science, linguistics, psychology, sociology, economics and management. Developing integrative conceptual frameworks requires scholars’ urgent attention to gain an overarching comprehensive view of theoretical and practical developments in this key area.
Consequently, the objective of this Special Issue “Leveraging User Behavior and Data Science Technologies for Management” is to invite novel research to fill these gaps in current business knowledge, while offering an integrative view of how data-driven decisions and user generated data impact current and future business practices. We welcome theoretical, empirical, conceptual and experimental studies that offer fresh and critical insights on the topic. We are open to a wide range of research methods as long as they are consistent with the aims and scope of the special issue.
We welcome submissions that focus on – but are not limited – to:
– Identify best practices in leveraging and applying data science to inform marketing and management decision-making;
– Understand how can companies improve the use of user data (both UGC and UGB) to gain knowledge of peoples’ behavior and needs;
– Analyze differences in the analysis of data generated from explicit (UGC) and implicit (UGB) user participation, e.g. how implicit, unintentional vs. explicit, voluntary data may impact differently in consumer decision-making; how these types of data may impact into or inform managerial decisions;
– Identify the technological, ethical or other challenges that impact implementation of data science technologies that unveil consumer digital behavior;
– Understand how traditional text-mining techniques are evolving from traditional sentiment to explore modal properties such as trust, commitment, etc. to gain a better understanding on users’ perceptions and behavior;
– Identify and improve on approaches that capture and analyze more insightfully unstructured data such as video, voice, images…both individually or in combination with text in order to improve managers’ decision-making;
– Explore Consumer privacy rights and ethical issues (e.g. who owns and who controls the data, how much exposure is acceptable vs. intrusive or threatening etc.);
– Address how current and emerging digital data analytical techniques can improve consumer insights, thus leading to more effective decision-making;
– Identify and explore the impact of implicit metrics (versus traditional explicit metrics) to gain understanding on user behavior through these emerging data science techniques
– Analyze how multiple sources of user data can be efficiently incorporated (for example, integrating digital data with offline, contextual data) to overcome risks associated to decision-making based on aggregate-level big data.
Generally, we invite contributions related – but not limited – to any of the topics outlined above and which clearly relate to UGC and UGB and data science for management using research approaches such as data mining, social network analysis, knowledge discovery, sentiment analysis, big data, machine learning approaches, Virtual Reality (VR), Augmented Reality (AR), Support Vector Machines (SVM), Digital Analytics, Big Data Analytics, Neuromarketing, etc.
The submission deadline is 31 Dec 2020. All papers will be screened by the guest editors (and desk rejected if not deemed suitable) before being sent to at least two reviewers. Manuscripts must apply the general author guidelines such as style and paper length of the Journal of Business Research to be found here. All papers must go through the journal’s online submission portal. Authors should select “SI: Behavior and Data Science” option when submitting their manuscript. Final Acceptance: October 2021.
We welcome informal enquiries relating to the Special Issue, proposed topics, and potential fit with the Special Issue objectives. Please direct any questions on the Special Issue to the Guest Editors:
● Dr. Lorena Blasco-Arcas: [email protected]
● Dr. Ana Reyes: [email protected]
● Prof. Mariano Luis Alcañiz Raya: [email protected]
● Prof. Minas Kastanakis: [email protected]
More info here.