Patrick Mikalef

Big data analytics capabilities: a systematic literature review and research agenda

With big data growing rapidly in importance over the past few years, academics and practitioners have been considering the means through which they can incorporate the shifts these technologies bring into their competitive strategies. To date, emphasis has been on the technical aspects of big data, with limited attention paid to the organizational changes they entail and how they should be leveraged strategically. As with any novel technology, it is important to understand the mechanisms and processes through which big data can add business value to companies, and to have a clear picture of the different elements and their interdependencies. To this end, the present paper aims to provide a systematic literature review that can help to explain the mechanisms through which big data analytics (BDA) lead to competitive performance gains. The research framework is grounded on past empirical work on IT business value research, and builds on the resource-based view and dynamic capabilities view of the firm. By identifying the main areas of focus for BDA and explaining the mechanisms through which they should be leveraged, this paper attempts to add to literature on how big data should be examined as a source of competitive advantage. To this end, we identify gaps in the extant literature and propose six future research themes.

Consumer Intentions on Social Media: A fsQCA Analysis of Motivations

With social media gaining rapidly in popularity, a large number of companies have initiated attempts to capitalize on the large user base present on such platforms. Yet, it still remains unclear how affordances that social media facilitate can influence consumer intentions to purchase and engage in word-of-mouth. This paper builds on the distinction between utilitarian and hedonic features, and empirically examines how these aspects present on social media platforms affect consumer behavior. Using survey data from 165 social media users we perform fuzzy set Qualitative Comparative Analyses (fsQCA) to extract patterns of factors that impact both purchase, and word-of-mouth intentions. The outcomes of the analyses demonstrate that realizing high purchase and word-of-mouth intentions can be achieved through multiple ways which also depend on gender and spending history. Practical and theoretical implications are discussed, particularly concerning how these findings can guide the design of successful social media outlets for commerce.

Absolute price as a determinant of perceived service quality in hotels: a qualitative analysis of online customer reviews

User generated content and especially customer-generated reviews are becoming a prominent information source for travellers making hotel booking decisions. Building upon the dimensions of the SERVQUAL model, the aim of this study is to identify why hotel customers have disparate opinions regarding perceived service quality during their stays. In order to do so, we distinguish between high and low absolute priced hotels. We adopt this categorisation based on prior studies, which identify price as a strong determinant of hotel customers’ perceptions and decisions. By applying qualitative data analysis methods on customer reviews from one of the world’s leading online hotel reservation agencies (Booking.com), we show that absolute price has an impact on how customers perceive various dimensions of service quality. In particular, the number of positive reviews is significantly different for low and high priced hotels when examining the dimensions of tangibility and empathy. Additionally, our research reveals that the number of negative reviews also differentiates significantly for low and high priced hotels, not only for the dimensions of tangibility and empathy but also with regard to responsiveness and reliability. Based on these findings we highlight implications for researchers and practitioners and suggest future directions for research.

Investigating students’ use and adoption of with-video assignments : lessons learnt for video-based open educational resources

The use of video-based open educational resources is widespread, and includes multiple approaches to implementation. In this paper, the term “with-video assignments” is introduced to portray video learning resources enhanced with assignments. The goal of this study is to examine the factors that influence students’ intention to adopt with-video assignments. Extending the technology acceptance model by incorporating students’ emotions, we applied partial least squares structural equation modeling based on a sample of 73 students who systematically experienced with-video assignments in their studies. In addition, students’ activity was analyzed using aggregated time series visualizations based on video analytics. Learning analytics indicate that students make varying use of with-video assignments, depending on when they access them. Students are more likely to watch a greater proportion of the video when they use with-video assignments during the semester, as opposed to during the exams. Further, the findings highlight the important role of students’ emotions in adopting with-video assignments. In addition, perceived usefulness of with-video assignments increases their positive emotions and intention to adopt this medium, while perceived ease of use increases only their intentions. Together, these constructs explain 68% of the variance in students’ intention to adopt with-video assignments.

Designing social commerce platforms based on consumers’ intentions

ABSTRACT Social commerce has been gaining momentum over the last few years as a novel form of e-commerce, creating substantial changes for both businesses and consumers. However, little is known about how consumer behaviour is influenced by characteristics on social commerce platforms. The purpose of this research is to elucidate how user intentions to purchase and to spread word-of-mouth (WOM) are influenced by characteristics present on social commerce platforms. More specifically, we adopt a uses-and-gratifications perspective and examine the influence of socialising, personal recommendation agents, product selection, and information availability. Partial least squares structural equation modelling analysis is performed on a sample of 165 social commerce users. Outcomes of the analysis indicate that socialising and personal recommendation agents positively influence purchase and WOM intentions, while product selection is found to only enhance purchase intentions. Interestingly, our findings reveal that information availability has no significant effect on purchase and WOM intentions. Finally, we find that when purchase intentions are triggered, they will tend increase consumers’ intentions to WOM.

An integrative adoption model of video-based learning

Purpose – Video-based learning (VBL) is gaining increased attention as an educational means in settings such as the flipped classroom and massive open online courses. The value of VBL has been recognized in a range of contexts due to the ability to extend opportunities for life-long education for all socio-economic levels, removing geographical boundaries while at the same time alleviating time constraints. Yet, despite the advantages featured by VBL and some promising early outcomes regarding its effectiveness, little is known about what influences individuals to adopt VBL systems and technologies. The paper aims to discuss this issue. Design/methodology/approach – Building on behavioral and adoption-acceptance theories as well as on past empirical studies on e-learning, a conceptual model of VBL adoption is proposed. By analyzing survey data from 260 VBL learners, the conceptual model is put to test by means of structural equation modeling. Findings – Outcomes indicate that performance expectancy (PE) a...

Big Data Enabled Organizational Transformation: The Effect of Inertia in Adoption and Diffusion.

Big data and analytics have been credited with being a revolution that will radically transform the way firms operate and conduct business. Nevertheless, the process of adopting and diffusing big data analytics, as well as actions taken in response to generated insight, necessitate organizational transformation. Nevertheless, as with any form of organizational transformation, there are multiple inhibiting factors that threaten successful change. The purpose of this study is to examine the inertial forces that can hamper the value of big data analytics throughout this process. We draw on a multiple case study approach of 27 firms to examine this question. Our findings suggest that inertia is present in different forms, including economic, political, socio-cognitive, negative psychology, and socio-technical. The ways in which firms attempt to mitigate these forces of inertia is elaborated on, and best practices are presented. We conclude the paper by discussing the implications that these findings have for both research and practice.

Social Media and Analytics for Competitive Performance: A Conceptual Research Framework

Social media websites have managed in a very short period of time to attract and maintain a massive user. Recognizing their potential, the vast majority of companies are deploying strategies in order to harness their potential in various ways, and ultimately, to establish their competitive position. Nonetheless, being relevantly novel, it still remains unclear as to how it is possible to make the most out of social media, especially in competitive and highly dynamic environments. As with any new technology, it is important to understand the mechanisms and processes through which social media can be of business value for companies in order to incorporate them into their competitive strategies. To this end, the present paper aims to provide a theoretical discussion leading up to a conceptual research framework that can help explain the mechanisms through which social media and analytics lead to competitive performance gains. The conceptual research framework builds on the resource-based view (RBV) and dynamic capabilities view (DCV) of the firm, and provides a synthesis of the two theoretical perspectives.

Big Data Analytics as an Enabler of Process Innovation Capabilities: A Configurational Approach

A central question for information systems (IS) researchers and practitioners is if, and how, big data can help attain a competitive advantage. Anecdotal claims suggest that big data can enhance a firm’s incremental and radical process innovation capabilities; yet, there is a lack of theoretically grounded empirical research to support such assertions. To address this question, this study builds on the Resource-Based View and examines the fit between big data analytics resources and organizational contextual factors in driving a firm’s process innovation capabilities. Survey data from 202 chief information officers and IT managers working in Norwegian firms is analyzed by means of fuzzy set qualitative comparative analysis (fsQCA). Results demonstrate that under different patterns of contextual factors the significance of big data analytics resources varies, with specific combinations leading to high levels of incremental and radical process innovation capabilities. These findings suggest that IS researchers and practitioners should look beyond direct effects, and rather, identify key combinations of factors that lead to enhanced process innovation capabilities.

Motivations and emotions in social media: Explaining users' satisfaction with FsQCA

This study aims to explain how motivations and emotions combine to influence users’ satisfaction with social media. Motivations are decomposed into four attributes, entertainment, information, social-psychological, and convenience, while emotions are divided into their two main categories, that is positive and negative emotions. In order to examine the interplay of these factors and their combined effect on satisfaction, a conceptual model is developed, and validated on a data sample of 582 social media users, through fuzzy-set qualitative comparative analysis (fsQCA). The findings indicate eight configurations that lead to high satisfaction, which show the importance of high convenience, followed by entertainment and information in being satisfied with social media, while emotions and social-psychological factors are less important. This study contributes in social media literature by identifying specific patterns of users for whom these factors are important and influence greatly their satisfaction.