Purpose
In the complex environments of online personalization, multiple factors have been considered to explain consumers’ online behaviour, but largely without considering the role of specific ...configurations of variables and how they may affect consumer behaviour. This study aims to show how trust towards online vendors, privacy, emotions and experience combine to predict consumers’ purchase intentions.
Design/methodology/approach
Building on complexity theory, a conceptual model followed by research propositions is presented. The propositions are empirically validated through configurational analysis, using fuzzy-set qualitative comparative analysis (fsQCA) on 182 customers with experience in personalized online shopping. Predictive validity analysis is also performed.
Findings
Five solutions of trust, privacy, emotions and experience increase intention to purchase, and six solutions inhibit it. The findings verify the importance of trust and happiness in successful personalized online shopping. Their absence inhibits purchase intentions. Also, high experience may help to overcome low trust or negative emotions, whereas low experience requires the combination of high trust and happiness. None of the examined factors are indispensable to explain purchase intentions.
Research limitations/implications
The study uses fsQCA, differentiating from traditional studies in the area that use variance-based methods and identifies multiple solutions explaining the same outcome. The proposed approach contributes to theory development in the field.
Practical implications
The multiple solutions lead to new ways on how companies may approach their customers, as each one covers a specific part of the sample, adding to the fact that in personalized marketing there is not one single optimal solution explaining customer purchase intentions.
Originality/value
This study contributes by extending existing knowledge on how trust, privacy, emotions and experience combine to increase or mitigate intention to purchase towards the development of new emotion-centric theories and the design and provision of personalized services and presenting a step-by-step methodological approach for how to apply fsQCA in e-commerce studies.
•We provide a step-by-step guide on employing fsQCA based on an already published study.•Performing contrarian case analysis and testing for predictive validity is highly recommended.•FsQCA can be ...used together with variance-based methods (e.g., SEM).•Existing studies can be extended and complemented through fsQCA.
The increasing interest in fuzzy-set Qualitative Comparative Analysis (fsQCA) in Information Systems and marketing raises the need for a tutorial paper that discusses the basic concepts and principles of the method, provide answers to typical questions that editors, reviewers, and authors would have when dealing with a new tool of analysis, and practically guide researchers on how to employ fsQCA. This article helps the reader to gain richer information from their data and understand the importance of avoiding shallow information‐from‐data reporting. To this end, it proposes a different research paradigm that includes asymmetric, configurational‐focused case‐outcome theory construction and somewhat precise outcome testing. This article offers a detailed step-by-step guide on how to employ fsQCA by using as an example an already published study. We analyze the same dataset and present all the details in each step of the analysis to guide the reader onto how to employ fsQCA. The article discusses differences between fsQCA and variance-based approaches and compares fsQCA with those from structured equation modelling. Finally, the article offers a summary of thresholds and guidelines for practice, along with a discussion on how existing papers that employ variance-based methods are extendable and complemented through fsQCA.
A central question for information systems (IS) researchers and practitioners is if, and how, big data can help attain a competitive advantage. To address this question, this study draws on the ...resource-based view, dynamic capabilities view, and on recent literature on big data analytics, and examines the indirect relationship between a firm’s big data analytics capability (BDAC) and competitive performance. The study extends existing research by proposing that BDACs enable firms to generate insight that can help strengthen their dynamic capabilities, which, in turn, positively impact marketing and technological capabilities. To test our proposed research model, we used survey data from 202 chief information officers and IT managers working in Norwegian firms. By means of partial least squares structural equation modeling, results show that a strong BDAC can help firms build a competitive advantage. This effect is not direct but fully mediated by dynamic capabilities, which exerts a positive and significant effect on two types of operational capabilities: marketing and technological capabilities. The findings suggest that IS researchers should look beyond direct effects of big data investments and shift their attention on how a BDAC can be leveraged to enable and support organizational capabilities.
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.
Metaverses refer to immersive virtual worlds in which people, places, and things of the physical world are represented by their digital representations. The wide adoption of metaverses is expected to ...widely disrupt the way we interact in the virtual world by elevating our online interactive experiences and bringing a plethora of implications for businesses. Following a structured literature review of related research published in the last decade, we shed light on our current understanding of metaverses and reflect on the potentially transformative value of metaverses for businesses in the near future. We draw on an established research framework to organize the insights of existing literature across different levels of analysis and activities’ purpose. Through this analysis, we reveal eight propositions on the changes brought by the use of metaverses and identify a number of open questions which could serve as future research avenues.
•This study uses complexity theory and configurational analysis to explain satisfaction in SNSs.•Motivations and emotions combine to predict high satisfaction.•Convenience followed by entertainment ...and information motivations have key role for satisfied users.•High satisfaction can be achieved with the existence of both positive and negative emotions.
Social Networking Sites (SNSs) play an important role in our daily lives and the number of their users increases regularly. To understand how users can be satisfied in the complex digital environment of SNSs, this study examines how motivations and emotions combine with each other to explain high satisfaction. Users’ motivations comprise four attributes, entertainment, information, social-psychological, and convenience. Emotions are divided into their two main categories, that is positive and negative emotions. We draw on complexity and configuration theories, present a conceptual model along with propositions and perform a fuzzy-set qualitative comparative analysis (fsQCA). Through an empirically study with 582 SNSs users, we present eight combinations (configurations) of motivations and emotions that lead to high satisfaction, which highlight the role of high convenience, followed by entertainment and information motivations in being satisfied with SNSs. High satisfaction can be achieved both when positive and negative emotions are high and low, depending on how they combine users’ motivations. None of the factors are indispensable to explain high satisfaction on their own, instead they are insufficient but necessary parts of the causal combinations that explain high satisfaction. This study contributes in SNSs literature by extending current knowledge on how motivations and emotions combine to increase satisfaction, and by identifying specific patterns of users for whom these factors are important and influence greatly their satisfaction.
Purpose
– Satisfaction and experience are essential ingredients for successful customer retention. This study aims to verify the moderating effect of experience on two types of relationships: the ...relationship of certain antecedents with satisfaction, and the relationship of satisfaction with intention to repurchase.
Design/methodology/approach
– This paper applies structural equation modelling (SEM) and multi-group analysis to examine the moderating role of experience in a conceptual model estimating the intention to repurchase. Responses from 393 people were used to examine the differences between high- and low-experienced users of online shopping.
Findings
– The research shows that experience has moderating effects on the relationships between performance expectancy and satisfaction and satisfaction and intention to repurchase. This study empirically demonstrates that prior customer experience strengthens the relationship between performance expectancy and satisfaction, while it weakens the relationship of satisfaction with intention to repurchase.
Practical implications
– Practitioners should differentiate the way they treat their customers based on their level of experience. Specifically, the empirical research demonstrates that the expected performance of the online shopping experience (performance expectancy) affects satisfaction only on high-experienced customers. Instead, the effort needed to use online shopping (effort expectancy) and the user's belief in own abilities to use online shopping (self-efficacy) influence satisfaction only on low-experienced customers. The effect of trust and satisfaction is significant on online shopping behaviour on both high- and low-experienced customers.
Originality/value
– This paper investigates how different levels of experience affect customers' satisfaction and online shopping behaviour. It is proved that experience moderates the effect of performance expectancy on satisfaction and the effect of satisfaction on intention to repurchase. It also demonstrates that certain effects (effort expectancy and performance expectancy) are valid for only one of the two examined groups, while only one effect (trust) is valid for both (high- and low-experienced).
When users engage with learning technologies, produce a vast amount of multimodal data, such data can help us increase the prediction accuracy of users learning performance and examine unscripted ...tasks during learner–computer interaction.
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•We propose using multimodal data to capture learning experience.•Multimodal data from 251 game sessions and 17 users were collected.•Click-stream models achieve 39% error rate in predicting learning.•Fusing multimodal data drops error rate up to 6%.•Identify the physiological features that best predict skill development.
Most work in the design of learning technology uses click-streams as their primary data source for modelling & predicting learning behaviour. In this paper we set out to quantify what, if any, advantages do physiological sensing techniques provide for the design of learning technologies. We conducted a lab study with 251 game sessions and 17 users focusing on skill development (i.e., user's ability to master complex tasks). We collected click-stream data, as well as eye-tracking, electroencephalography (EEG), video, and wristband data during the experiment. Our analysis shows that traditional click-stream models achieve 39% error rate in predicting learning performance (and 18% when we perform feature selection), while for fused multimodal the error drops up to 6%. Our work highlights the limitations of standalone click-stream models, and quantifies the expected benefits of using a variety of multimodal data coming from physiological sensing. Our findings help shape the future of learning technology research by pointing out the substantial benefits of physiological sensing.
This study uses complexity theory to explain and better understand the causal patterns of factors stimulating online shopping behavior in personalized e-commerce environments. To this end, it ...identifies cognitive and affective perceptions as essential factors in online shopping behavior and proposes a conceptual model along with research propositions. To test its propositions, it employs fuzzy-set qualitative comparative analysis (fsQCA) on a sample of 582 experienced online shoppers. Findings indicate nine configurations of cognitive and affective perceptions that explain high intention to purchase. This study, contributes to the literature 1) by offering new insights into the relation among the predictors of online shopping behavior and 2) advancing the theoretical ground of how customers' cognitive and affective perceptions combine to better explain high purchase intentions. The findings support the need for online shopping environments to be more interactive in order to target customers' cognitive and affective perceptions, and increase their intention to purchase.
Mobile technologies and their applications have the potential to benefit various learning contexts. Users’ perceptions of mobile learning (m-learning) technologies are of great importance and precede ...the successful integration of these technologies in education. M-learning adoption has been investigated in the literature with reference to various factors and learning analytics, but largely without considering the role of different configurations (i.e., specific combinations of variables), and how these configurations might affect the adoption of various user groups. For instance, users with different backgrounds, experiences, learning styles, and so on might not be represented by the one-model-fits-all produced from the common regression approaches. In this study, we briefly review factors that have been proven important in the context of mobile learning adoption, and build on complexity theory and configuration theory in order to explore the causal patterns of factors that stimulate the use of mobile learning. To test its propositions, the study employs fuzzy-set qualitative comparative analysis (fsQCA) on a data sample from 180 experienced m-learning users. Findings indicate eight configurations of cognitive and affective characteristics, and social and individual factors, that explain m-learning adoption. This research study contributes to the literature by (1) offering new insights on how predictors of m-learning adoption interrelate; (2) extending existing knowledge on how cognitive and affective characteristics, and social and individual factors, combine to lead to high m-learning adoption; and (3) presenting a step-by-step methodological approach for how to apply fsQCA in the area of learning systems and learning analytics.
•How technology acceptance research can capture complex multidimensional phenomena.•How predictors of m-learning adoption interrelate to form complex configurations.•Eight different solutions can explain high m-learning adoption.•A methodological approach on how to apply fsQCA in learning systems and analytics.
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