Big data, oriented-organizational culture, and business performance: A socio-technical approach

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This paper experimentally examines the impact of oriented-organizational culture that could support big data analytics (BDA) in higher education institutions (HEIs) in Saudi Arabia. Specifically, this study analyzed the effect of oriented-organizational culture (OC) on big data tasks (BDTs) toward improving decision-making (DM) and organization performance (OP). The study hinged on the theory of socio-technical systems to investigate BDA elements in higher education decision-making in Saudi Arabia. The analysis was conducted using a quantitative survey research design where data were collected from 270 IT staff working in Saudi Arabian HEIs using Qualtrics. PLS-SEM was applied to validate the research data and explore the relationship between the proposed hypotheses. The findings show that oriented-organizational culture positively affected big data tasks, i.e., storing, analyzing, and visualizing. Similarly, oriented-organizational culture positively affects improving decision-making by top management in Saudi Arabian universities. OC also positively influences the performance of Saudi Arabian universities. Improving decision-making by top management has a positive impact on enhancing the overall university’s performance. However, big data tasks, i.e., storing, analyzing, and visualizing, negatively affect improving decision-making by top management in Saudi Arabian HEIs. One of the study limitations is the small sample size; future studies should include private and public universities to alter the expected outcomes. Additional technological elements, such as IT infrastructure at Saudi Arabia’s private and public HEIs, are recommended to be considered in future studies to establish the competence of respective IT infrastructure.

Acknowledgment
The authors wish to thank the Problems and Perspectives in Management Journal editors for their valuable time and assistance in improving the manuscript.

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    • Figure 1. Research model showing the relationship between social subsytem, technical subsytem and improved decision making
    • Figure 2. Measurement model highlighting factor loadings
    • Figure 3. Structural model based PLS-SEM analysis
    • Table 1. Demographic information of respondents
    • Table 2. Measurement model assessment showing data validity
    • Table 3. Hypotheses testing
    • Table 4. Structural model assessment of eminence quality
    • Conceptualization
      Maher Aseeri, Kyeong Kang
    • Data curation
      Maher Aseeri, Kyeong Kang
    • Formal Analysis
      Maher Aseeri, Kyeong Kang
    • Funding acquisition
      Maher Aseeri, Kyeong Kang
    • Investigation
      Maher Aseeri
    • Resources
      Maher Aseeri
    • Software
      Maher Aseeri, Kyeong Kang
    • Validation
      Maher Aseeri, Kyeong Kang
    • Visualization
      Maher Aseeri, Kyeong Kang
    • Writing – original draft
      Maher Aseeri, Kyeong Kang
    • Writing – review & editing
      Maher Aseeri, Kyeong Kang
    • Methodology
      Kyeong Kang
    • Project administration
      Kyeong Kang
    • Supervision
      Kyeong Kang