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Impact of Social Media on the Firm’s Knowledge Exploration and Knowledge Exploitation: The Role of Business Analytics Talent
Social media is one of the most disruptive technologies in executing a firm’s digital business transformation strategies. Does the firm’s ability to use social media affect its proficiency in exploring and exploiting knowledge? What should be the role of business analytics talent in this equation? We study theoretically and empirically these cutting-edge research questions. Our proposed research model argues that social media capability enables the development of knowledge exploration and knowledge exploitation, and business analytics talent exerts a positive reinforcing role in the impact of social media on knowledge exploration. We empirically tested the proposed research model with a secondary dataset from a sample of US firms using PLS path modeling. After running a robustness test by estimating eight alternatives/competing models, the empirical analysis revealed that social media capability is positively related to knowledge exploration and knowledge exploitation, but with a stronger effect on knowledge exploration. Moreover, business analytics talent plays a positive moderator role in the relationship between social media capability and knowledge exploration. This study contributes to the IS research by (1) introducing, developing, and operationalizing the concepts of social media capability and business analytics talent; and (2) theoretically arguing and empirically showing the pivotal role of social media capability in exploring new knowledge and the complementary role of business analytics talent. Our study also provides several critical lessons learned for top executives and proposes promising future IS research avenues.
Journal Article
Decision making performance of business analytics capabilities: the role of big data literacy and analytics competency
PurposeThis study investigates the relationships between data governance (DG), business analytics capabilities (BAC), and decision-making performance (DMP), with a focus on the mediating effects of big data literacy (BDL) and data analytics competency (DAC).Design/methodology/approachThe study was conducted with 178 experienced managers in public service organizations, using a quantitative approach. Structural equation modeling (SEM) and mediation tests were employed to analyze the data.FindingsThe findings reveal that DG and BDL are critical antecedents for developing analytical capabilities. Big data literacy mediates the relationship between DG and BAC, while BAC mediates the relationship between DG and DMP. Furthermore, DAC mediates the relationship between BA capabilities and DMP, explaining most of the effect of BAC on DMP.Practical implicationsThese results highlight the importance of DG in fostering BDL and analytical skills for improved decision-making in organizations.Originality/valueBy prioritizing DG practices that promote BDL and analytical capabilities, organizations can leverage business analytics to enhance decision-making.
Journal Article
How market orientation contributes to innovation and market performance: the roles of business analytics and flexible IT infrastructure
Purpose
Market orientation (MO) (intelligence generation, intelligence dissemination and responsiveness) is known as one of the key concepts in marketing literature. Although prior research has widely focused on the meaning and application of MO, few attempts have been made to explore how market-oriented firms lead to innovation and market performance and what factors actually moderate this relationship. To fill this gap, the present study aims to explore the relationship between MO, innovation and market performance. This study also attempts to examine the intervening role of IT infrastructure, business analytics (BA) capabilities and market turbulence in the proposed model.
Design/methodology/approach
In this study, a questionnaire-based survey was undertaken to test the proposed hypotheses. To verify the proposed theoretical model, partial least squares (PLS)/structured equation modeling (SEM) was performed with 114 valid survey data.
Findings
Despite prior studies which postulated innovation performance as the final outcome of MO (Han et al., 1998; Song et al., 2015), this study focused on innovation performance as a mediating outcome which finally leads to market performance. The statistical results approve the putative relationship which means managers would be able to realize the paramount role of innovation as an integral part of achieving higher market performance. In addition, no support was found for the relationship between intelligence generation and responsiveness. This finding shows that not all obtained information can help managers in the decision-making process.
Originality/value
This study aims to enrich literature by developing a conceptual model to test the link between MO, innovation and market performance. The value of this study is to investigate the roles of flexible IT infrastructure, BA capabilities and market turbulence as the potential moderators in the proposed model. The results advance the understanding of the influence of BA capabilities on the link between intelligence dissemination and responsiveness. Findings also show innovation performance as remarkable and deemed valuable capability, leading to higher performance in marketing-related activities, particularly in highly turbulent markets.
Journal Article
Unlocking agility: Trapped in the antagonism between co-innovation in digital platforms, business analytics capability and external pressure for AI adoption?
PurposeDigital technology is increasingly important in enhancing organizational agility (OA). Institutional theory and resource-based view were harmonized to analyze firms' adoption of digital technologies. However, previous studies on OA have revealed that external pressures imply the imposition of barriers or technological standards that ultimately restrict OA. This study employs this double theoretical lens to investigate the mediation role of business analytics capability (BAC) in the relationship between co-innovation (CO), i.e. open innovation in digital platforms, and OA, as well as the negative moderating effect of external pressure for artificial intelligence adoption (EPAIA) on this mediation.Design/methodology/approachStructural equation modeling was used to test the moderated mediation with survey data from 229 firms.FindingsThe main result indicates that 72% of OA variance is explained by the effect of CO that is transmitted by the mediator (BAC). However, contrary to the authors' expectations, EPAIA only has a positive moderating effect along the path between BAC and OA.Originality/valueThis work contradicts the prevalent notion of the negative consequences of external pressures for artificial intelligence adoption. Specifically, this study's findings refute the notion that institutional pressures are the source of technical problems that disrupt CO and BAC integration and reduce OA. In contrast, the unexpectedly positive effect of EPAIA may indicate that this type of external pressure can be viewed as a significant sign and an opportunity for the company to adopt the industry's most advanced and effective digital transformation practices.
Journal Article
Collect, combine, and transform data using Power Query in Excel and Power BI
\"Using Power Query, you can import, reshape, and cleanse any data from a simple interface, so you can mine that data for all of its hidden insights. Power Query is embedded in Excel, Power BI, and other Microsoft products, and leading Power Query expert Gil Raviv will help you make the most of it. Discover how to eliminate time-consuming manual data preparation, solve common problems, avoid pitfalls, and more. Then, walk through several complete analytics challenges, and integrate all your skills in a realistic chapter-length final project. By the time you're finished, you'll be ready to wrangle any data-and transform it into actionable knowledge.\"-- Provided by publisher.
Book
A decision-analytic framework for interpretable recommendation systems with multiple input data sources: a case study for a European e-tailer
Recommendation systems help companies construct online personalization strategies for customers who are often overwhelmed by the abundance of product choices available. To extend existing operations research literature on recommendation systems, this article proposes a decision analytic framework for interpretable recommendation systems with multiple input data sources for e-commerce settings. The impact of multiple data sources on recommendation performance is investigated and two hybridization data fusion strategies, i.e., a posteriori weighting and input data source combination using factorization machines are benchmarked. Furthermore, a new importance score mechanism is introduced to provide insight into the input data sources’ and underlying variables’ impact on recommendation performance. The framework is empirically validated on 164,338 customers and 51,367 products across eight real-life data sets with four input data sources (product, customer, raw behavioral, and aggregated behavioral data) obtained from a large European e-commerce company. With this new decision analytic framework, e-commerce companies are able to open their recommendation system’s black box, to identify the most predictive input data sources and the best hybridization strategy for their business context.
Journal Article