Determinants of Self-Service Analytics Adoption Intention

Abstract

The increasing popularity of self-service analytics (SSA) is empowering business users to analyze data and generate actionable insights autonomously. While there are many benefits to SSA tools, there is a scarcity of research on the factors influencing their adoption in business organizations. This article presents an extended technology acceptance model (TAM) that incorporates the task-technology fit (TTF), compatibility, and user empowerment as critical antecedents of users' intention to adopt SSA tools for reporting and analytics tasks. To test the proposed model, data were collected through a questionnaire survey of 211 business users working in different industries in Jordan. The collected data were analysed using structural equation modeling (SEM). The results of this study demonstrate that the task-technology fit, compatibility, and user empowerment are significant predictors of users' perceptions of usefulness and ease of use of SSA tools. Both of perceived usefulness and perceived ease of use have a positive effect on users' intention to adopt SSA tools. Collectively, all these factors account for 51.6 percent of the variance in the behavioral intention. The findings of this study provide several key implications for research and practice, and thus should contribute to the design and adoption of more user-accepted SSA tools and applications.

An Empirical Examination of the Relationship Between Data Storytelling Competency and Business Performance

“…Some of commercial self-service business analytics tools, such as Tableau 1 , QlikView 2 , and Microsoft Power BI 3 can be used for creating sharable data-driven stories via data visualizations and interactive dashboards and reports. These platforms lay the foundations for constructing narrative elements and exploratory visuals of data storytelling (Daradkeh, 2019b(Daradkeh, , 2019c. However, business analytics platforms with dedicated features for developing data stories are not yet as advanced and still used less frequently than the aforementioned alternatives (Tischler et al, 2017).…”

“…Other studies (White, Burger, & Yearworth, 2016) suggest that organizations that are adept at extracting and communicating analytical insights to technical and non-technical audiences through telling compelling stories can extend the reach and scope of business analytics applications to address a wider range of business problems and needs. Such expansion must support the needs of the analytics audience for personalized and collaborative decision-making environment (Daradkeh, 2019b). It also enables organizations to build an analytics-driven culture and improve analytics literacy among employees, leading to a better productivity and performance (Boldosova, 2019;Dahlstrom, 2014).…”

“…In today's business environment, organizations accumulate massive amounts of data, and their ability to make informed decisions and drive business performance depends in a part on their acumen and competency in analyzing these data and converting them into actionable insights (Daradkeh, 2019a(Daradkeh, , 2019b. To this end, various business analytics solutions are increasingly being leveraged by organizations to extract meaningful and relevant insights from the data they accumulate and support decision-making at both strategic and operational levels (Delen & Zolbanin, 2018).…”

“…As business analytics usually deals with complex data and uses sophisticated algorithms and statistical models to generate insights (Davenport & Harris, 2007), there are often times issues with interpreting and digesting the analytical insights presented and contextualizing the analytics to the overall decision-making process (S. Chen et al, 2020). Low adoption rates of business analytics, as shown in various studies and reports (e.g., Daradkeh, 2019b;Ghasemaghaei, Ebrahimi, & Hassanein, 2018;Richardson, Sallam, Schlegel, Kronz, & Sun, 2020;Rouhani, Ashrafi, Ravasan, & Afshari, 2018;Sallam & Howson, 2017), and the benefits organizations expect from the pervasive usage of their information assets prove that there is a huge gap between obtaining analytical insights and presenting them to the relevant audience in a simple, compelling and impactful way (S. Chen et al, 2020;Vidgen, Shaw, & Grant, 2017). As a result, business analytics capabilities may not get used effectively, and decision-makers who usually lack analytical skills and expertise may fall back on their intuition or experience for decision-making (Herschel & Clements, 2017).…”

On the relevance of self-service business intelligence to university management

“…Academic research also supports this over-positive representation. Contributions on selfservice BI are mainly technical, concentrating on the specific benefits or features (Elbashir et al, 2008;Dilla, 2013;Kowalczyk, 2015;Schlesinger and Rahman, 2016;Daradkeh and Moh'd Al-Dwairi, 2018;Daradkeh, 2019). This lack of research and narrow vision is highlighted by Rikhardsson and Yigitbasioglu (2018), who, in discussing future open issues, pointed to the need for more critical studies where technical issues linked to information systems are coupled to an organisational perspective.…”