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All Journal Data : Journal of Information Systems and Management Novatio : Journal of Management Technology and Innovation
Onasoga Olukayode
Universiti Utara Malaysia
Computer Science & IT Decision Sciences, Operations Research & Management

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Dashboard Information Quality, Recommendation Explainability, and Contextual Relevance as Predictors of Replenishment Decision Confidence among Alfamart Employees in Singkawang Onasoga Olukayode; P. Natarajan; C. Tri Widiastuti; Setyoningsih Wibowo
Data : Journal of Information Systems and Management Vol. 4 No. 2 (2026): April 2026
Publisher : PT Penerbit Ilmiah Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/data.v4i2.2002

Abstract

Business intelligence dashboards are widely deployed in retail store networks to support inventory management. Yet the availability of dashboard-generated replenishment recommendations does not automatically ensure that employees feel confident acting on them. This cross-sectional survey examined whether three dashboard-related characteristics perceived information quality, recommendation explainability, and contextual relevance are associated with replenishment decision confidence among frontline employees. Data were collected from 120 eligible staff across 30 Alfamart outlets in Singkawang, West Kalimantan, between September and October 2025, using a 16-item Likert questionnaire (1–5). A separate pilot of 25 respondents across five stores informed instrument refinement. Multiple linear regression with cluster-robust standard errors (30 store clusters) was the primary analytic method. All three predictors were positively and significantly associated with decision confidence after controlling for dashboard experience and role. Information quality (B = 0.357, 95% CI [0.237, 0.478], p < .001), contextual relevance (B = 0.395, 95% CI [0.239, 0.552], p < .001), and recommendation explainability (B = 0.170, 95% CI [0.036, 0.304], p = .014) each contributed independently. The three predictors jointly accounted for ΔR² = .327 beyond the control variables (R² = .338 overall). Findings support the value of distinguishing information quality from the interpretability and operational applicability of dashboard recommendations. Limitations include the cross-sectional design, single organizational context, and the self-report nature of the dependent variable. Practitioners seeking to improve user confidence in automated replenishment systems should attend to both the accuracy of underlying data and the clarity and local fit of generated recommendations.
Artificial Intelligence Readiness, Governance Practices, and Innovation Performance among SMEs in Surakarta: An Explanatory Sequential Mixed-Methods Study Onasoga Olukayode; Widaningsih; Dodi Yudiardi; Mohammad Wasil
Novatio : Journal of Management Technology and Innovation Vol. 4 No. 1 (2026): January 2026
Publisher : PT Penerbit Ilmiah Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/novatio.v4i1.2028

Abstract

Artificial intelligence (AI) adoption is expanding among small and medium enterprises (SMEs), yet its contribution to innovation performance remains insufficiently understood. This study examined the associations between AI readiness, AI governance practices, employee digital capability, and innovation performance among technology-oriented SMEs in Surakarta, Indonesia. An explanatory sequential mixed-methods design was employed, involving 220 survey respondents analyzed using multiple linear regression with IBM SPSS Statistics 30 and 12 interview informants analyzed through thematic analysis. Results demonstrated significant positive associations between innovation performance and AI readiness (β = 0.343, p < .001), AI governance practices (β = 0.292, p < .001), and employee digital capability (β = 0.289, p < .001), explaining 47.0% of the variance (adjusted R² = 0.470). Qualitative findings highlighted infrastructure and data preparedness, responsible AI experimentation, and employees’ ability to translate AI applications into business improvements. Integrated findings indicated that these dimensions complement one another, although resource constraints may limit their effectiveness. Grounded in the Technology–Organization–Environment framework, this study provides empirical insights into the organizational conditions supporting AI-driven innovation in SMEs.
Co-Authors C. Tri Widiastuti Mohammad Wasil P. Natarajan Setyoningsih Wibowo Widaningsih Yudiardi, Dodi