The effects of knowledge management processes on service sector performance: evidence from Saudi Arabia

The paper addresses how four knowledge management (KM) processes—knowledge creation, capture, sharing, and application—affect organizational performance in Saudi Arabia’s service sector. Against a global shift to a knowledge-based economy, organizations increasingly rely on knowledge assets for competitive advantage. Prior studies highlight KM’s importance but show gaps in developing-country contexts, limited attention to service industries in Saudi Arabia, and insufficient focus on quality and operational performance. The study aims to empirically test the effects of the four KM processes on three performance dimensions—quality, operational, and innovation performance—across public and private service organizations in Saudi Arabia, responding to calls for comprehensive frameworks and evidence in this context.

The literature defines KM as an integrated, process-oriented approach encompassing knowledge creation/generation, capture/codification, sharing/transfer, and application/utilization. Multiple KM process models exist, with many converging on a four-process cycle (Alavi & Leidner, 2001). This study adopts the four-process model and details factors facilitating each process (e.g., opportunity, motivation, collaboration, technology infrastructure, training, culture, and rewards). KM’s role in organizational performance is well-documented, improving decision-making, innovation, service quality, and competitiveness, though context matters. Studies in service sectors across countries report positive links between KM processes and performance, but evidence from Saudi Arabia remains limited and fragmented, with prior local research focusing on KM barriers, strategic importance, or specific subsectors. Table 2 in the paper summarizes Saudi-based studies, showing positive effects of KM capabilities/processes on performance outcomes (e.g., customer satisfaction, financial performance, service innovation) and a need to explore the four processes’ effects on quality, operational, and innovation performance comprehensively in Saudi service organizations.

Design: Quantitative, cross-sectional survey measuring managerial perceptions of KM processes and organizational performance. Population and sample: Managers (top-, middle-, operative-level) from public, semi-governmental, and private organizations across seven service categories in Saudi Arabia: transportation/storage; government services; wholesale/retail/restaurant/hotel; finance/insurance/consultancy/business; real estate; healthcare; telecommunications/IT. Non-probability purposive sampling was used due to the absence of a comprehensive sampling frame (per GASTAT). Data collection via online survey from Nov 25, 2021 to Mar 30, 2022 yielded 605 valid responses, exceeding SEM guidelines. Instrument: Three sections, 33 items, five-point Likert scale (strongly disagree to strongly agree). Section 1: demographics (8 items). Section 2: KM processes—knowledge creation (4 items, Andreeva & Kianto, 2011), knowledge capture (3 items, Andreeva & Kianto, 2011), knowledge sharing (4 items, Donate & Sánchez de Pablo, 2015), knowledge application (2–3 items, Donate & Sánchez de Pablo, 2015; reported as 2 in table but 3 in descriptive results). Section 3: performance—quality performance (SERVQUAL-based items, Parasuraman et al., 1988, plus Lee & Wong, 2015), operational performance (Venkatraman & Ramanujam, 1986), innovation performance (Huang & Li, 2009; Oke, 2007). The survey was translated to Arabic using forward–backward methodology by language experts. Data analysis: SPSS v29 for descriptive analyses; AMOS v29 for CFA, CMV assessment, and covariance-based SEM for structural modeling and hypothesis testing. CFA model (7 constructs, 25 items) exhibited good fit: χ²=569.877, RMSEA=0.045, CFI=0.961, IFI=0.961; all standardized loadings >0.5, p<0.001. Reliability and validity supported: all CR and Cronbach’s alpha ≥ recommended thresholds (CR>0.6, alpha>0.70); AVE>0.50 for all constructs; Fornell–Larcker discriminant validity generally held (minor KS–KA proximity deemed acceptable). CMV checks: Harman’s single-factor explained 36.91% (<50%); common latent factor method indicated CMV below 50%. Structural model fit: χ²=1017.423, df=258, χ²/df=3.943, IFI=0.907, CFI=0.907, RMSEA=0.070. Control variables (gender, age, work experience, education, type of organization) were examined; type of organization positively affected quality and innovation performance, without altering hypothesized relationships.

• Descriptive implementation levels: KM processes moderately implemented (means <4.0). Knowledge creation items ranged ~3.77–3.96; knowledge capture lowest on patents/licenses (mean 3.27). Performance means: quality items ~3.70–4.03; operational ~3.68–4.04; innovation ~3.74–4.13.
• Measurement model: CFA supported good fit; all loadings significant; reliability (alpha, CR) and convergent/discriminant validity satisfied (with minor KS–KA caveat).
• Control variables: Type of organization positively influenced quality and innovation performance; other demographics non-significant.
• Structural paths (standardized β, all p-values shown): • Knowledge creation → Innovation performance β=0.589 (p<0.001); → Quality performance β=0.300 (p<0.001); → Operational performance β=0.246 (p<0.001). • Knowledge capture → Innovation performance β=0.275 (p<0.001); → Quality performance β=0.241 (p=0.002); → Operational performance β=0.214 (p=0.005). • Knowledge sharing → Quality performance β=0.034 (p=0.740); → Operational performance β=−0.095 (p=0.357); → Innovation performance β=−0.133 (p=0.143); all nonsignificant. • Knowledge application → Operational performance β=0.327 (p<0.001); → Innovation performance β=0.265 (p<0.001); → Quality performance β=0.187 (p=0.012).
• Summary: Knowledge creation, capture, and application significantly improve quality, operational, and innovation performance (with varying magnitudes). Knowledge sharing shows no significant direct effect on these performance outcomes in this context.

Findings confirm that KM processes are differentially associated with performance dimensions in Saudi service organizations. Knowledge creation exhibits the strongest effect on innovation and a meaningful positive effect on quality and operations, aligning with the knowledge-based view and prior studies; this likely reflects cultural and policy drivers (e.g., Vision 2030) emphasizing innovation and learning. Knowledge capture positively, though modestly, affects all performance dimensions, underscoring the need for effective tools and training to document and retain critical knowledge. Knowledge sharing shows no significant direct effect on performance, suggesting contextual impediments such as inadequate sharing technologies, misaligned reward systems, and employee reluctance to share (status concerns, lack of incentives). Improving these antecedents may unlock sharing’s impact. Knowledge application most strongly enhances operational performance, supporting the view that effectively using knowledge streamlines processes, reduces errors, and boosts productivity; its lower effects on quality and innovation may reflect the need for stronger capture foundations and strategic planning (e.g., R&D support) to translate applied knowledge into broader outcomes. Overall, the results address the research question by demonstrating which KM processes most influence specific performance outcomes and highlighting actionable levers to enhance impact in the service sector.

The study contributes a comprehensive KM–performance framework for Saudi Arabia’s service sector and provides empirical evidence that knowledge creation, capture, and application significantly improve quality, operational, and innovation performance, whereas knowledge sharing’s direct effect is nonsignificant. Four key implications arise: (1) organizations should assess KM effects on quality, operations, and innovation to guide resource allocation and justify KM investments; (2) strengthening knowledge creation capabilities is critical for high-quality and innovative services; (3) prioritizing knowledge application practices can yield substantial operational gains; (4) improving the structural and technological enablers of knowledge sharing is necessary to realize its potential effect on performance. Future research should examine KM infrastructure (tools/technology), reward systems, leadership, and culture as antecedents/moderators; test financial and market outcomes; explore other sectors (e.g., manufacturing) in Saudi Arabia; and employ qualitative methods (interviews, focus groups) to deepen understanding of mechanisms linking KM processes to performance.

• Omitted personal factors (e.g., trust, attitudes toward sharing) may influence the knowledge sharing–performance relationship.
• Self-reported survey data raise common method variance concerns, though Harman’s single-factor (36.91%) and common latent factor tests indicate CMV is not substantial.
• Non-probability purposive sampling limits generalizability; efforts were made to include all seven service categories to enhance representativeness.