The role of positive and negative valence factors on the impact of bigness of data on big data analytics usage
Introduction
Many firms have attempted to effectively utilize big data analytics to improve their decision quality (Müller, Junglas, vom Brocke, & Debortoli, 2016). Big data analytics refers to the use of a new generation of analytical tools (e.g., Python, R) to effectively analyze and get insight from data that is high in terms of variety, volume, and velocity (Gantz & Reinsel, 2012). The advent of modern technology, new types of data, and advanced analytical tools provides firms both opportunities and challenges (Lam, Sleep, Hennig-Thurau, Sridhar, & Saboo, 2017; Raguseo, 2018). A recent survey found that the number of firms that intend to invest in using big data analytics during the next two years dropped from 31% to 25% in 2016 (Gartner, 2016). This could be due to the fact that for many firms it is still not clear if the benefits of the use of big data outweigh its challenges and costs (Kache, Kache, Seuring, & Seuring, 2017). Therefore, although in the last few years big data has become a key factor in transforming the way in which firms do their business (Côrte-Real, Oliveira, & Ruivo, 2017; Xu, Frankwick, & Ramirez, 2016), many of them have still not started to utilize such data (Ghasemaghaei, Hassanein, & Turel, 2017). Hence, the main objective of this study is to investigate the role of positive and negative valence factors on the impact of bigness of data on big data analytics usage within firms. This study responds to Clarke’s (2016) call for focusing not only on big data benefits, but also on its risks and challenges.
According to a recent report, only 15% of firms invested in the use of big data analytics successfully deployed their big data analytics projects to production (Gartner, 2016). One of the key challenges that firms face in utilizing big data is security issues involved with big data aggregation and analysis (Bertino & Ferrari, 2018). With the advent of advanced technologies, firms can collect and process huge amounts of sensitive information regarding their employees and customers, as well as trade secrets, intellectual property, and financial information, which could be a valuable target for attackers (Tankard, 2012). Thus, understanding the role of big data security concern as a negative valence factor on the impact of big data on big data analytics usage is critical. Another main challenge facing firms seeking to use big data analytics is the complexity of processing and analyzing big data (Alharthi, Krotov, & Bowman, 2017). Due to the complexity of analyzing big data, many employees may avoid or postpone the use of big data analytics (Ghasemaghaei et al., 2017). Therefore, although advanced technologies enable firms to develop Information Technology (IT) infrastructure to access and obtain big data, because of the data security concern and task complexity issues the use of big data analytics may be reduced considerably. Thus, the first objective of this study is to investigate the role of data security concern and task complexity (as negative valence factors) on the relationship between bigness of data and big data analytics use.
One of the main benefits of obtaining big data from different sources is to improve data diagnosticity. Data diagnosticity refers to obtaining valuable information from data (Grange, Benbasat, & Burton-Jones, 2017). According to LaValle, Lesser, Shockley, Hopkins, and Kruschwitz, (2011), the most successful firms are obtaining big data to transform data into insight and then action. Improving data diagnosticity could be one of the main factors that impact the use of big data analytics within firms (Ghasemaghaei, Ebrahimi, & Hassanein, 2016). Another main benefit in obtaining big data is the increase in data accessibility. The increase in volume, variety, and velocity enhances the availability of data for firms to make more informed and faster decisions (Ghasemaghaei, Ebrahimi, & Hassanein, 2018). Therefore, the second objective of this study is to investigate the role of data diagnosticity and data accessibility (as positive valence factors) on the relationship between bigness of data and big data analytics use.
The existing literature mainly considers anecdotal evidence regarding the benefits and challenges of obtaining big data (Alharthi et al., 2017; Yang, Huang, Li, Liu, & Hu, 2017); empirical evidence about the impact of bigness of data on big data analytics use and the role of positive and negative valence factors on this association is lacking (Abbasi, Sarker, & Chiang, 2016; Clarke, 2016). Our study draws on the valence theory perspective to address the following research questions: (1) Do data security concern and task complexity mediate the impact of bigness of data on the use of big data analytics? And (2) Do data diagnosticity and data accessibility mediate the impact of bigness of data on the use of big data analytics? Both of these questions examine novel aspects not previously studied in the Information Systems (IS) literature. Towards the above objectives and leveraging the above theory, we propose and empirically validate a research model using survey data collected from 140 IT managers and data analysts. The findings of this research provide a theory-based understanding of the impact of data security concern, task complexity, data accessibility, and data diagnosticity on the impact of bigness of data on the use of big data analytics within firms, while also providing guidance for managers to successfully utilize big data analytics within their firms.
Section snippets
Theoretical background
This section provides a review of the literature regarding big data analytics usage and valence theory perspective.
Research model and hypotheses
The research model in Fig. 1 integrates the valence theory discussed above to explain the mediating role of positive and negative valence factors on the relationship between bigness of data and big data analytics usage. Table 1 shows the definitions of the variables in the model.
Bigness of data refers to high-velocity, high-volume, and high-variety data assets (Lycett, 2013). Velocity refers to the speed of accessing, streaming, and aggregating data (Bhosale & Gadekar, 2014). Many firms have
Sampling
In this study, the survey method has been used to understand the causal relationships between the variables in the research model. This method has been used because it can identify valuable information in a sample and explain relationships between variables in the model (Gable, 1994; Wamba et al., 2017). To test the proposed research model, a survey of IT managers and data analysts has been used, who were recruited through a national market research firm. Data analysts and IT managers were
Measurement model
To measure the constructs in the research model, the study adapted previously validated instruments (see items and sources in Appendix A). Scales were slightly adapted to reflect the context of this study. Three potentially relevant control variables were also included in the survey. First, firm size, which was operationalized with number of employees, was considered a control variable because larger firms often have access to more resources (Chen et al., 2014). Firm size was coded as a dummy
Control variable effects
We evaluated whether the impact of control variables (i.e., firm industry type, firm revenue, and firm size) on dependent variable (i.e., big data analytics use) was significant. Results indicated that firm industry type, firm revenue, and firm size did not significantly influence big data analytics use (β = -0.070; p > 0.05, β = -0.039; p > 0.05, and β = -0.014; p > 0.05, respectively).
Impact of interaction between valence factors and bigness of data on big data analytics use
We used Interaction software package (See www.danielsoper.com) to investigate the impact of interaction
Theoretical contributions
Research indicates that the main challenge many firms face is that it is still not clear if the benefits of the use of big data outweigh its challenges and costs. Along this line, reports show that the number of firms that intend to invest in using big data analytics within the next two years has decreased. Therefore, there is a need to understand the most critical factors (both barriers and benefits) that impact the use of big data analytics within firms; this is what this study sought to do.
Conclusion
This study addressed a significant gap in the literature regarding the reason that most firms could not successfully adopt big data analytics within their firms. We used valence theory perspective to explain the role of positive and negative valence factors on the impact of bigness of data on big data analytics use. The results suggest that bigness of data increases data security concern, task complexity, data accessibility, and data diagnosticity. Our findings also reveal that while task
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