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Business analytics (BA) has been a breakthrough technological development in recent years. Although scholars have suggested several solutions in using these technologies to facilitate decision-making, there are as of yet limited studies on how analysts, in practice, improve decision makers' understanding of business environments. This study uses sensemaking theory and proposes a model of how data analysts generate analytical outcomes to improve decision makers' understanding of the business environment.

This study employs an interpretive field study with thematic analysis. The authors conducted 32 interviews with data analysts and consultants in Australia and New Zealand. The authors then applied thematic analysis to the collected data.

The thematic analysis discovered four main sensegiving activities, including data integration, trustworthiness analysis, appropriateness analysis and alternative selection. The proposed model demonstrates how these activities support the properties of sensemaking and result in improved decision-making.

This study provides strong empirical evidence for the theory development and practice of sensemaking. It brings together two distinct fields – sensemaking and business analytics – and demonstrates how the approaches advocated by these two fields could improve analytics applications. The findings also propose theoretical implications for information system development (ISD).

This study demonstrates how data analysts could use analytical tools and social mechanisms to improve decision makers' understanding of the business environment.

This study is the first known empirical study to conceptualize the theory of sensemaking in the context of BA and propose a model for analytical sensegiving in organizations.

Business analytics (BA) is a new frontier of technology development and has enormous potential for value creation. Information systems research shows ample evidence of its positive business impacts and organizational performance. However, there is limited understanding of how decision-makers or users of BA outcomes actually engage with data analysts in the process of data-driven insight generation and how they improve their understanding of business environments using BA outcomes. To aid this engagement and understanding, this study investigates the interaction between decision-makers and data analysts when they attempt to uncover data capacities and business needs and acquire business insights from BA tools.

This study employs an interpretive field study with thematic analysis. The authors conducted interviews with 31 participants who all relied on BA in their daily decisions. The study participants were engaged in different BA roles, including data analysts and decision-makers. They validated the applicability and usefulness of our findings through a focus group with eight practitioners, including decision-makers and data analysts from the same companies.

This study proposes a process model of data-driven sensemaking and sensegiving based on Weick’s sensemaking framework. The findings exhibit that decision-makers are engaged in sensemaking by identifying areas of focus, determining BA scope, evaluating generated insights and turning BA into action. The findings also show that data analysts engage in sensemaking by consolidating data, data understanding, preparing preliminary outcomes and generating actionable reports. This study shows how sensemaking processes and sensegiving activities work together over time through immediate enactment, selection and decision cycles.

This study is a first attempt to understand interactions in the context of BA using the perspective of sensemaking and sensegiving.

This paper seeks to propose and empirically validate a conceptual model on the antecedents of review helpfulness comprising three constructs, namely, valence dissimilarity, lexical dissimilarity and review order.

A panel dataset of customer reviews was collected from Amazon. Using deep learning and text processing techniques, 650,995 reviews on 13,612 products from 570,870 reviewers were analyzed. Using negative binomial regression, four hypotheses were tested.

The results indicate that new reviews with high valence dissimilarity and lexical dissimilarity compared to existing reviews are less helpful. However, over the sequence of reviews, the negative effect of review dissimilarity on review helpfulness can be moderated. This moderation differs for valence and lexical dissimilarity.

This study explains review dissimilarity in the context of online review helpfulness. It draws on the elaboration likelihood model and explains how the impacts of peripheral and central cues are moderated over the sequence of reviews.

The findings of this study provide benefits to online retailers planning to implement online reviews to improve user experience.

This paper highlights the importance of review dissimilarity in identifying user perception of online review helpfulness and understanding the dynamics of this perception over the sequence of reviews, which can lead to improved marketing strategies.

Through a data-driven theory development approach, this study builds on affordance theory and demonstrates how online mobile app reviews can be analyzed to understand the drivers of informal mobile learning success.

Textual big data provide a wealth of information regarding user–app relationships and various facets of user engagement. Adopting structural topic modeling and sentiment analysis, the authors extract latent topics from reviews of two educational apps: Duolingo and Photomath.

The findings suggest that the quality of the relationship between users and mobile learning apps is significantly reliant on how underlying affordances have been actualized. While affordances can leverage satisfaction, they may also be a source of frustration in case of any failure in their design or integration. The analysis reveals eight emergent affordances: practicality, affordability, information reliability, instruction integrity, hedonic experience, user-friendliness, interactive input and iterative upgrading.

Since affordances of a technology entail both enablement and constraint and are best studied as a bundle of connected elements influencing each other reciprocally, the authors discuss how to address potential challenges from technical aspects to the added value of using mobile learning apps.

The results demonstrate that qualitative information in online reviews about mobile learning app experiences is of significant value. The approach demonstrates how advanced analytics can provide business value by addressing the evolving nature of customer needs and expectations. It proves the value of online reviews in discovering underlying technology affordances and their potential boundaries and challenges.

Sharing knowledge with business partners is a challenging issue as firms need to share their valuable know-how assets with individuals or other companies out of their organizational boundaries. As supply chain management (SCM) deals with various stakeholders, firms face difficulties with privacy and ownership when they share their know-how with suppliers or business partners. This study introduces blockchain technology as a mediator in improving knowledge sharing (KS) practices in supply chains.

The data have been collected from surveys with 116 experts working in blockchain start-ups and organizations, and the authors used structural equation modeling for its analysis.

The results show that two features of blockchain technology, namely transparency and security, have the highest impacts on mediating knowledge sharing impacts on supply chain performance. The authors’ findings also highlight that among the performance metrics of SCM, speed is highly improved when blockchain technology is used for knowledge sharing. Their study provides guidance for managers on how to improve SCM performance through KS, which is empowered by a blockchain system.

The authors’ findings help organizations to improve supply chain actions, improve innovation, enhance competitive advantage and increase the speed of relationships in the supply chain. The research also contributes literature by analyzing the key factors showing how knowledge sharing structure may be improved by blockchain technology which would be helpful for both academics and practitioners.

This paper aims at exploring the role of structural capital (SC) dimensions – customer, process and innovational – in the development of e‐business models (eBM). The Iranian carpet industry is tested regarding five types of eBMs: Direct to customer, Full Service Provider, Virtual Community, Shared Infrastructure and Value Net Integrator.

First, measures for SC dimensions and required core competencies for eBMs are extracted from the literature. Then, the correlation level between SC dimensions and different eBMs are hypothesized. Finally, after using a questionnaire in 30 Iranian carpet companies, the hypotheses are tested.

This study indicates that three dimensions of SC influence different eBMs in their own way. While one instant dimension is strongly effective for one eBM, it does not significantly affect the other one.

The role of human capital – the second part of intellectual capital – on the development of eBM as well as the dependency of some other eBMs such as intermediaries on intellectual capital should be investigated in further research.

Using the help of this study, firstly, companies will concentrate on the most effective dimensions of SC in developing a special eBM. Secondly, they will exclude those eBMs which are not applicable regarding their knowledge capabilities.

This study brings together two disciplines that have not been considered together before: the development of eBMs and the management of intellectual capital.

This paper aims to empirically explore the effects of intellectual property (IP) on intellectual capital (IC) and firm performance in Iran.

A questionnaire‐oriented survey from senior and top managers in the Iranian computer and electronic industry was utilized for regression analysis.

The findings indicate that IP significantly influences other dimensions of IC, which consists of human capital (HC), relational capital (RC) and structural capital (SC). The study also provides empirical evidence that gaining firm performance is positively related to these three elements of IC.

First, more advanced statistical techniques with a larger number of respondents could be used to evaluate the regression equations. Second, the companies chosen for the study are from two specific and fairly similar industries in Iran. Thus, the results may not be applicable to other industries in different countries.

With a broad view on IP that considers its creation, protection and utilization too, IP has a central role in knowledge‐based organizations to enhance competitive advantage.

This study builds on and extends the research made by Bollen et al., to link IP and IC to company performance. The paper focuses on the effects of IP on other parts of IC.

This study aimed to examine the impact of ICT and digital knowledge on students’ thoughts and beliefs. Using Information and Communication Technology (ICT) in learning and teaching processes can improve the interpretation of knowledge, not only in the learning process but also for thoughts and beliefs. Beliefs and thoughts as propositional content are understood to be a subjective manner of knowing and becoming a focal point of education process. In addition, ICT plays a vital role in enhancing the efficiency of the teaching process which can change the thoughts of learners. So, in this paper, the usage of ICT in education was considered as a key factor for improving students’ thoughts and beliefs. In addition, a conceptual model was proposed to evaluate this impact.

Data were collected from 384 students from secondary schools in Iran. For assessing the elements of the model, a complete questionnaire was designed. For statistical analysis of questionnaires, SPSS 22 and SMART-PLS 3.2 software package was used.

The obtained results showed the high strength of the proposed model. The outcomes indicated that digital technology acceptance positively affects students’ thoughts and beliefs. In addition, the findings showed that the role of digital knowledge, digital training facilities and digital education content on students’ thoughts and beliefs was significant.

The authors deal with one experiment and so the results cannot be generalized. The trail should be repeated with many groups and in diverse contexts.

Despite the importance of the investigating the impact of ICT and digital knowledge on the students’ thoughts and beliefs, the relationship among these factors was not examined well in previous research. Thus, the investigation of the impact of ICT and digital knowledge on the students’ thoughts and beliefs is the main originality of this research. For this goal, a new conceptual model is proposed, which has 11 sub-indicators within four variables: digital technology acceptance, digital knowledge, digital training facilities and digital education content.