Data-driven decision making in graduate students’ research topic selection

Abstract

Purpose With the advent of the intelligent environment, as novice researchers, graduate students face digital challenges in their research topic selection (RTS). The purpose of this paper is to explore their cognitive processes during data-driven decision making (DDDM) in RTS, thus developing technical and instructional strategies to facilitate their research tasks. Design/methodology/approach This study developes a theoretical model that considers data-driven RTS as a second-order factor comprising both rational and experiential modes. Additionally, data literacy and visual data presentation were proposed as an antecedent and a consequence of data-driven RTS, respectively. The proposed model was examined by employing structural equation modeling based on a sample of 931 graduate students. Findings The results indicate that data-driven RTS is a second-order factor that positively affects the level of support of visual data presentation and that data literacy has a positive impact on DDDM in RTS. Furthermore, data literacy indirectly affects the level of support of visual data presentation. Practical implications These findings provide support for developers of knowledge discovery systems, data scientists, universities and libraries on the optimization of data visualization and data literacy instruction that conform to students’ cognitive styles to inform RTS. Originality/value This paper reveals the cognitive mechanisms underlying the effects of data literacy and data-driven RTS under rational and experiential modes on the level of support of the tabular or graphical presentations. It provides insights into the match between the visualization formats and cognitive modes.

Data literacy and the cognitive challenges of a data-rich business environment: an analysis of perceived data overload, technostress and their relationship to individual performance

“…Therefore, DL can be negatively associated with PDO, since it involves the ability to synthesize large amounts of data and to understand what questions should be asked to that data to meet a specific business need (Koltay, 2017;. Besides, the results of the study by Li et al (2019) indicate that the individual's DL significantly affects his cognition in terms of information/data processing. So, we hypothesize that: H2.…”

“…We developed a questionnaire for data collection composed of 19 indicators: 5 related to DL (adapted from Li et al, 2019), 6 to PDO (adapted from Karr-Wisniewski and Lu, 2010;Wu et al, 2019;Wang and Li, 2019), 4 to TS (adapted from Ayyagari et al, 2011) and 4 to IP (adapted from Karr-Wisniewski and Lu, 2010;Chen and Karahanna, 2018) which were presented to the respondents on a seven-point Likert-type agreement scale, ranging from 1 5 strongly disagree to 7 5 strongly agree. Such items are listed in Table 2 in the topic referring to the presentation of results.…”

“…Thus, the focus of the business field on DL in terms of Information Systems is little explored empirically, especially considering the relationships between the variables proposed here. That is, the literature still needs more evidence about the effects of DL on the individual's cognitive aspects (Li et al, 2019).…”

Cognitive mechanisms underlying interaction and contribution in online health communities: the perspectives of doctors and patients

“…The RM is labelled analytical, deliberative, verbal, and logical, while the EM is labelled intuitive, automatic, natural, nonverbal, narrative, and affective (Epstein, 1994). Researchers have applied CEST to explain human cognition in various situations (Li et al , 2019). These two systems operate in parallel and interact with each other, and human behaviour is the product of the joint operation of the two modes (Epstein, 1994).…”

How do institutional dimensions of open government data affect innovation? Evidence from research institutes in China

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