The Role of Process Standardisation on Managerial Perception of Customer Satisfaction in Manufacturing Firms of Malaysia

3. METHODOLOGY

The proposed study is based on a primary research design that employs a quantitative approach, utilising Structural Equation Modelling with Multi-Group Analysis to investigate the relationship between process standardisation and customer satisfaction among manufacturing companies. This study assessed both the correlational and predictive relationships between the variables. The constructs are loosely connected with the TQM model, but the main aim is to examine the predictive relationship between process standardisation mechanisms and the managerial perception of customer satisfaction. Hence, the hypotheses were formulated and examined using PLS-SEM to determine the strength and significance of the hypothesised relationships. Data were gathered using a structured questionnaire on a five-point Likert scale (Appendix A). This method is well suited for capturing managers’ views and for evaluating structural equation models (SEM), as also proposed by (Lionello et al., 2021). The questionnaire, as depicted in the Appendix, conceptualised seven dimensions: process execution, process inputs and outputs, Collaboration and Communication, Governance, Strategy, Data, and managerial perception of customer satisfaction. In addition, the dependent variable, perceived customer satisfaction, is conceptualised as a unidimensional managerial evaluation construct. The measurement items of the questionnaire associated with the dimensions of process standardisation were adopted from (Wurm et al., 2018). In addition, the measurement items or sub-dimensions of perceived customer satisfaction were adapted from (Ribbink et al., 2004). The items are comprised of “Our customers are generally pleased with the consistency and reliability of the products delivered by our company”. “Our customers are satisfied with our products as they meet their quality expectations due to well-controlled and standardised operational processes”. “Our customers enjoy using our products, produced through standardised manufacturing process”.

The classification of firms into low- and high-automation groups was carried out according to their average automation scores on a 5-point scale for digital integration, system coordination, automated workflows, and real-time information use, as developed by (Atieh et al., 2025). Multi-group analysis (MGA) is conducted using a median-based threshold to classify firms into high and low automation groups (125 firms each). Scores that fell near the median cut-off were categorised to have a balanced number of scores within each score category and help facilitate meaningful comparison across groups. No changes were made to the response values, automation scores, or original observations during this process.

A sample of 250 respondents, consisting of Malaysian manufacturing companies, was selected, and 125 respondents each in the sample represented high and low business process automation firms. Renowned firms such as Top Glove Corporation, Hartalega Holdings, Petronas Chemicals Group, Nestlé Malaysia, Vitrox Corporation, and Inari Amertron were approached. The respondents were department heads, production managers, supervisors, and IT experts with first-hand knowledge of the operations process. As suggested by (Shamsudin et al., 2024), purposive sampling was used in this study because of the specific emphasis on respondents who possess certain functional expertise that applies to the manufacturing operations. Information-rich respondents are essential for assessing managerial perceptions of customer satisfaction in low- and high-automation environments.

This sampling approach has the potential to include business entities that engage digitally and have professional links or networks at higher rates than common business entities, thus causing distortions in the perceived evaluations of Digital Manufacturing’s overall processes. The survey respondents are likely familiar with the processes and tools used digitally, which affects their ability to provide an accurate representation of process implementation, supply chain performance, and customer satisfaction. Ultimately, the results do not adequately characterise all samples in the broad manufacturing sector that have low levels of digital adoption and/or low levels of connectivity among management personnel. The z-probability formula was used in estimating the sample size, and the formula takes into account the confidence level, population variance, and margin of error.

n = (Z × Z × p × (1 − p)) ÷ (e × e)

n = (1.96 × 1.96 × 0.50 × 0.50) ÷ (0.06 × 0.06)

n = (3.8416 × 0.25) ÷ 0.0036

n = 0.9604 ÷ 0.0036

n = 266.78, rounding to 267 final participants).

A total of 267 questionnaires were returned out of 500, with a response rate of approximately 53.2%. After screening for missing values and multicollinearity, 17 responses were excluded, and 250 respondents were included in the analysis. A combination of professional platforms, such as LinkedIn, and the official social media pages of manufacturing companies was used to approach the participants, which guaranteed targeted access. After data screening, 250 valid questionnaires were retained for analysis. To facilitate multi-group analysis (MGA), a median split was performed based on the average automation scores, creating two statistically comparable groups of equal size (n = 125 each for low and high automation groups).

As manufacturing companies are spread throughout various regions of Malaysia, data collection was performed through an online survey, as it was easier and less resource-consuming to undertake. The approach also made it easier to access managerial respondents, who are more difficult to reach by conventional means because of their work duties. In addition, online administration proved to be more cost-effective and quicker for data collection, while allowing a broader reach of organisations. In addition, the online survey approach also allowed access to geographically dispersed participants in Malaysia, enhancing managerial accessibility within less time and cost consumption. Such benefits ensured that the method was suitable for the data collected from manufacturing managers with limited resources and time for research.

Despite the fact that the participants under study were recruited mostly through LinkedIn and official social media platforms, they oversampled employees working in more urban areas or less digitally mature firms. To reduce the possibility of bias, purposive sampling was used to ensure that there were participants in high- and low-automation firms in different regions. To control selection bias, both automation groups were subjected to the same screening parameters. The test for non-response bias was conducted using a t-test (independent samples) between early (n = 30) and late respondents (n = 30), as suggested by (Nwosu et al., 2024), and no significant differences were found.

Procedural and statistical cures were applied to assess common method bias. Procedurally, the anonymity of respondents was guaranteed, and the wording of items was revised to minimise ambiguity, as recommended by (Shamsudin et al., 2024). The single-factor test by Harman showed that no factor explained the majority of the variance. Furthermore, full collinearity VIF was also analysed to address the CMB, against the threshold value of 3.3, suggested by (Castillo et al., 2026). As observed in Table 1, the values of VIF are lower than the standard threshold of 3.3, indicating that there is no issue of CMB in the measurement model.

Table 1. Full-collinearity VIF.

Data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) with Multi-Group Analysis, which is appropriate for making predictions in a study and comparing groups, as recommended by (Perez et al., 2024). The measurement model was assessed to examine internal consistency reliability in terms of Cronbach’s alpha and composite reliability, and convergent validity in terms of average variance extracted. As reflected by (Hair et al., 2025), PLS-SEM was chosen over covariance-based SEM because it was best suited for testing a theory. However, as the study is mainly prediction-oriented, it was used to explain the variance in the managerial perception of customer satisfaction instead of confirming a strictly established theoretical model. It is conceptualised as a one-dimensional managerial assessment dimension which imitates the overall evaluation of managers of customer satisfaction instead of a multidimensional operational performance index. The model is structurally complex, comprising multiple interrelated dimensions and multiple group analyses between high- and low-automation firms, which can be analysed better by PLS-SEM than by CB-SEM. Moreover, the data were based on survey-based Likert responses, which might not be normally distributed in the multivariate sense, leading to the use of variance-based SEM. As a result, it met the methodological and predictive criteria of the study.

This was followed by structural path analysis to verify the hypothesised causal relationships among the process standardisation dimensions and the managerial perception of customer satisfaction. The measurement invariance of the Composite Models procedure was used to perform a multi-group analysis to determine the differences between the high and low automation groups. It is an effective method of analysis that allows for the comparison of structural relationships and considers the measurement equivalence that enhances the validity and interpretation of results.

4. RESULTS

4.1. Demographics Analysis

The results specified in Table 2 show the demographic classification of the study participants in a total sample of (n=250). The results show that among the participants, 70% were male and 30% were female. The maximum, that is 30% and 32%, fell in the age brackets of 26-35 years and 46-55 years, while 24% were 36-45 years. Among the participants, 40% were managers, 36% were supervisors, and 24% were department heads. In addition, 44% represented the production department, 24% the quality check department, and 32% the IT department of the manufacturing companies. In terms of the extent of automation in the manufacturing process, 50% reported low automation and 50% reported high automation. In terms of the mode of information sharing related to inputs and outputs, 24% reported the use of emails and manual spreadsheets, 40% reported integrated digital systems, and 36% reported real-time system integration. Lastly, regarding the level of system-based automated data capturing in production operations, 28% reported that data was captured at selected stages, 32% reported that data was captured across most processes, and 40% reported using fully system-generated data.

Table 2. Demographics analysis.

4.2. Descriptive Statistics Analysis

Table 3 presents the descriptive data for the study variables. The managers’ average scores were highest for Data (Mean = 3.737, SD = 0.738) and Strategy (Mean = 3.737, SD = 0.800), indicating a good level of consensus on the presence of these practices. The mean of Perceived Customer Satisfaction was also relatively high (Mean = 3.731, SD = 0.676), indicating that managers rated the satisfaction of the customers favourably. Governance reported a mean of (Mean = 3.576, SD = 0.842), followed by Collaboration and Communication (Mean = 3.377, SD = 0.917) and Process Execution (Mean = 3.352, SD = 0.933). Inputs and Outputs recorded the lowest mean (Mean = 3.333, SD = 0.814). The standard deviation values were moderate, indicating moderate consistency between the responses of the sampled companies.

Table 3. Descriptive statistics.

4.3. Measurement Model Analysis

The measurement model of the study was examined by applying measurement model approaches, which consisted of Cronbach’s Alpha, Composite Reliability, and Average Variance Extracted (AVE). The results are presented in Table 4.

Table 4. Measurement model.

The results of the measurement model indicate a satisfactory level of reliability and convergent validity for high automation, low automation, and the overall sample (Table 4). In Collaboration and Communication, the factor loadings were above the recommended factor loading of 0.70 in all groups, with a range of 0.831 to 0.928, a Cronbach’s alpha of over 0.87, and an AVE of over 0.79, indicating excellent internal consistency. The Data dimension recorded high loadings between 0.844 and 0.944 and a satisfactory reliability, but the comparison of the AVE values of the low automation group was relatively lower, indicating weak but tolerable convergence. Governance presented a high level of measurement within groups, with loadings above 0.90 and an AVE of more than 0.83. Moderate reliability scores were observed in the high automation group in the report of inputs and outputs, as they showed marginal loadings of less than 0.70, but overall reliability and AVE were acceptable. Process Execution shows variation among the groups, where weaker convergence is observed in high-automation firms and stronger convergence in low-automation firms. Strategy was a consistent dimension with large loadings, reliability, and AVE in all samples. In general, the measurement model met the criteria of reliability and validity and can be considered appropriate for structural analyses. The indicators PE1 (0.654) and IO3 (0.672) were retained for further use in the exploratory and early stages of developing the model using PLS-SEM because their loadings were higher than the minimum requirement of 0.60. They should be retained because of their theoretical importance, and previous studies, including (Chou & Lin,. 2023), have argued that variables with validity of 0.6 or higher need to be retained to maintain content validity. Furthermore, composite reliability and AVE values were satisfactory, even with lower item loadings, with all being above the recommended levels, suggesting that the scales had good internal consistency and convergent validity.

4.4. Discriminant Validity Analysis

The discriminant validity assessment using the HTMT ratio allows for the analysis of the dimensions’ separability and distinctness along conceptual overlapping by analysing a threshold value of 0.85. The results are presented in Table 5.

Table 5. Discriminant validity.

The HTMT results in Table 5 were analysed to evaluate the discriminant validity of the high-automation, low-automation, and general samples. A substantial part of the HTMT values fell below the conservative level of 0.85, which means that the dimensions are conceptually different and can be separated. Most of the dimension pairs within the high-automation group indicated acceptable discriminant validity; for instance, the value of HTMT between Data and Managerial perception of customer satisfaction lies below the threshold, indicating no conceptual overlap in the highly automated environment. Furthermore, within the low automation and overall segments, the HTMT values were also lower than the threshold of 0.85, which establishes discriminant validity with no conceptual overlap. Overall, the HTMT findings show that discriminant validity is achieved, and the measurement model has shown that dimension distinctiveness is acceptable, which in turn evidences the appropriateness of the model in future structural models and multi-group analyses.

4.5. Path Coefficient Analysis

The path analysis outcomes (Table 6) show a high degree of relationship between all the process standardisation dimensions on one hand and managerial perception of customer satisfaction in both high and low automation groups, as well as the overall sample.

Table 6. Path coefficient.

Note: **shows Significance at 5%; *** shows Significance at 1%

In the high automation group, Data (β = 0.461, p < 0.001) was strongly related to managerial perception of customer satisfaction, followed by Strategy (β = 0.230, p = 0.005), Collaboration and Communication (β = 0.215, p = 0.014), Governance (β = 0.188, p = 0.034), Process Execution (β = 0.162, p = 0.040), and Inputs and Output Data also showed a significant relationship (β = 0.460, p < 0.001) with Strategy (β = 0.225, p = 0.011).

For the low-automation group, all paths were found to be statistically significant, implying that all dimensions of standardisation in processes have a positive association with managerial perceptions of customer satisfaction. The strongest relationship was found for Data, where β = 0.460, p < 0.001, followed by Inputs & Outputs, where β = 0.204, p = 0.010, and Strategy, where β = 0.225, p = 0.011. Collaboration and Communication, Governance, and Process Execution were found to have a significant positive relationship with customer satisfaction (p < 0.05), which indicates that despite lower levels of technological integration, managers still rate standardisation, control, and coordination as critical to operational consistency.

Overall, Data held the strongest relationship (β = 0.465, p < 0.001), while Collaboration and Communication (β = 0.155, p = 0.035), Governance (β = 0.101, p = 0.029), Process Execution (β = 0.142, p = 0.044), Strategy (β = 0.141, p = 0.047), and Inputs also observed a significant association with the managerial perception of customer satisfaction. The combination of these results suggests that process standardisation dimensions have a substantial relationship with the managerial perception of customer satisfaction with data, being the most influential in all automation levels, which is truly important in providing customer-focused outcomes in Malaysian manufacturing scenarios.

Lastly, with regard to Cohen’s effect size guidelines (small effect ≈ 0.02, medium effect ≈ 0.15, large effect ≈ 0.35), there are clear differences between dimensions and automation levels. In high-automation firms, the small effects of the other dimensions (Collaboration, Governance, Inputs & Outputs, Process Execution, Strategy) are not significantly different from each other, except for data, which shows a large effect (f² = 0.392), suggesting that this is not significantly different from any of the other dimensions. In low-automation firms, data have a large effect (f² = 0.561), indicating even further overall dominance, whereas Inputs & Outputs have a small-to-moderate effect (f² = 0.066), and all other variables have negligible effects (<0.014). In the overall model, data had a medium effect (f² = 0.229), thus reinforcing its core explanatory role in the context. Small effects were shown by Strategy (f2 = 0.061) and Inputs & Outputs, and minimal effects were shown by Governance, Process Execution, and Collaboration.

4.6. Model Fitness Analysis

The model-fit indices in Table 7 suggest a reasonable overall model fit for all groups. All SRMR values were below 0.08, which is the cutoff for a good fit for the overall (0.058), high-automation (0.069), and low automation (0.071) models. The d_ULS and d_G values exhibited consistent differences between the saturated and estimated models, with no significant misfits. The chi-square values are quite high in all models, which is not surprising in large-sample studies and does not imply poor model fit in PLS-SEM. The model showed good predictive and structural fit in both automation groups.

Table 7. Model fitness.

4.7. Model Explanatory Power Analysis

The model’s explanatory power can be explained by the R-square and Adjusted R-square values, which are helpful in examining the predictive power or the level of variance in the dependent variable predicted by the independent dimensions. The results are presented in Table 8.

Table 8. Model explanatory power.

From the results specified in Table 8, it can be observed that in the case of high automation, 75.7% variance in managerial perception of customer satisfaction can be predicted by business process standardisation, in the case of low automation environment the predictive power is reduced to 66.8% and in overall environment 70% variance in managerial perception of customer satisfaction can be predicted by business process standardisation dimensions.

4.8. Predictive Relevance Analysis

The Q²predict value of 0.684 (Table 9) is considerably higher than zero, which shows the robust predictive relevance of the structural model for managerial perception of customer satisfaction. In addition, the comparatively low RMSE (0.570) and MAE (0.423) values signify satisfactory accuracy of prediction, which also indicates that the model holds significant out-of-sample predictive capability as well as practical usefulness. The comparatively high Q²predict value is attributed to the robust explanatory power of the process standardisation dimensions, specifically practices related to data which showed a significantly predictive relationship. This demonstrates that managerial perceptions of customer satisfaction hold a robust association with standardised operational processes.

Table 9. Predictive relevance.

4.9. MICOM Analysis

Both groups prompted by identical indicators, paths, and data treatment procedures established Configural Invariance. The results in Table 10 from all three dimensions show that the compositions are invariant within the high- and low-automation groups (P > 0.05), suggesting consistency in the measurement of the dimensions across groups. Step 3 results, however, suggest that the means are the only measures where there is partial measurement invariance, as variance differences were found for Collaboration & Communication, Data, Perceived Customer Satisfaction, and Strategy (p < 0.05). Dimensions seem to be equally measured across groups but are differently distributed, indicating that there are real structural differences between automation contexts. Importantly, the finding of compositional invariance and partial scalar invariance affords sufficient statistical support for using multi-group analysis (MGA), so that the observed group differences in path coefficients are not attributable to measurement error but are reflective of genuine structural differences.

Table 10. MICOM.

4.10. MGA Analysis

Multi-Group Analysis (MGA) (Table 11) is carried out to identify whether the structural relationship between process standardisation dimensions and the management’s perception of customer satisfaction is significantly different in high- and low-automation firms. Both PLS-MGA and Welch-Satterthwaite tests result in differentiated effects that Data, Inputs, and outputs, and Strategy have on managerial perception of customer satisfaction between the two levels of automation, as indicated by the p-values below the 0.05 level (Data: p = 0.008, 0.009; Inputs and Outputs: p = 0.038, 0.041; Strategy: p = 0.014, 0.016). Conversely, the path coefficient differences between Collaboration and Communication, Governance, and Process Execution were not statistically significant (p > 0.05), indicating the similarity of the effects of these dimensions in different automation contexts. Comprehensively, the results indicate that the managerial assessment of customer satisfaction in terms of standardisation processes depends on the maturity of automation in the specific dimensions to which these specific dimensions are associated with the effect of automation, especially data governance, operational inputs/outputs, and strategic alignment. The remaining dimensions were perceived consistently across companies, regardless of automation, indicating the higher effects of automation on process standardisation.

Table 11. Multi-group analysis.

Note: **: Significance at 5%; ***: Significance at 1%

5. DISCUSSION

The results suggest a statistically significant relationship between the degree of process execution and managers’ perception of customer satisfaction. Nonetheless, the effect size of this corresponding measure is very small, indicating that process execution contributes to managerial evaluations but is not a significant contributor compared to other aspects of the standardisation process. Process rigidity reduces variability among similar products and stabilises the production process. Consequently, managers perceive that greater operational consistency in production environments, as suggested by (Baiyere et al., 2020), enhances customer satisfaction. The results support the TQM literature by implying that operational consistency is the main driving force linking process execution to the managerial assessment of customer satisfaction, irrespective of the level of technological intensity. It is not the high automation that makes it effective; rather, it is the way it enhances the reliability of the processes it supports, as it eliminates uncertainty, stabilises workflow, and ensures predictable production results. This interpretation adds to (Al-Shami, 2023; and Teeroovengadum, 2023), who both suggested that process standardisation should be interpreted as more than just a technological characteristic and more as an organisational capability. Consequently, the results shift the focus of execution quality to an instrument for building the confidence of managers in a standardised manufacturing context. A wider implication of this finding is that process execution serves as a dimension of organisational signalling by which operational performance is interpreted by managers. Steady execution decreases uncertainty associated with whether outcomes are attributable to manageable procedures or random disparity, thus firming the confidence of managers in organisational capability. This extends current TQM arguments by signifying that standardisation leads not only to disciplined and improved processes but also to managerial sensemaking. Subsequently, managers evaluate outcomes related to customer satisfaction using the predictability of organisational routines rather than technological complexity alone.

The findings reveal a significant relationship between input-output controls and managerial perceptions of customer satisfaction. However, the effect of input-output standardisation is small and therefore does not form a major consideration in managerial evaluations. It appears that its influence comes from reinforcing operational predictability and process discipline rather than exerting a significant independent effect. Managers consider the consistent use of resources and outputs measured regularly as a reflection of operational discipline and trustworthiness, which are key to defect-free production in Total Quality Management (TQM) (De Ramon et al., 2020). In terms of their impact on customer satisfaction, both high-automation and low-automation environments have positive results; however, the dimensions that create these positive impacts are quite different. The results show that managers view stable input-output control as an indicator of the predictability of the organisation and production discipline, not just technical efficiency. This builds upon the defect prevention logic of TQM, showing that assessments of managers are not just based on output quality, but also on any trust in the consistency of operational controls. The results thus move beyond the discussion of automation differences and allow conclusions to be drawn on how effective a system of control is in terms of standardisation. In line with the findings of (De Ramon et al., 2020), this study underscores the importance of process reliability for managerial assessments by decreasing uncertainty about the continuity of production, compliance with requirements, and process responsiveness. The findings further indicate that input-output standardisation does not only have a function of quality control, but it also has an informational role. If resource inputs are predictable and consistent, managers can see that organisational processes are working as they would expect them to work for the organisation’s service levels. Instead of merely averting defects, standardised controls seem to decrease the uncertainty associated with operational outcomes and strengthen confidence in the reliability of the processes. This is a more comprehensive alternative to previous TQM-related discussions, as it places input-output management in the context of negotiating organisational stability and assessing the chances of maintaining customer-focused results.

Collaboration and Communication had significant effects on the managerial perception of customer satisfaction, and the intensity of its effects was stronger in companies with high automation, supporting H3. Collaboration and communication were statistically significant predictors, but the practical relationship was limited. The results indicate that communication is mostly a facilitator for other mechanisms to work and does not provide a significant independent source of managerial assessments. In Europe and North America, (Al-Shami, 2023) demonstrated that integrated digital systems boost cross-functional coordination because they help resolve problems faster and minimise errors in the processes. The results indicate that cooperation and communication impact managerial perceptions of customer satisfaction by creating process transparency, reliability of the process, and responsiveness of the organisation. These dimensions appear to function as integrative capacities. Instead of being simple communication tools, they help decrease ambiguity in production processes and increase certainty about the continuity of production processes. This perspective builds on an earlier discussion on TQM that focused on the importance of information consistency, in addition to process consistency, in forming managerial assessments of organisational performance. The findings are also consistent with those of (Hossain et al., 2024; and Kanan et al., 2023), who found that structured coordination systems boost managerial confidence, which facilitates predictable decision-making, prompt problem resolution, and transparent accountability for operations. In addition to coordination efficiency, the results show that collaboration and communication can help develop a “shared operational understanding” between organisational functions. As information is shared consistently and transparently, managers can better see how processes relate to each other and understand the potential causes of breakdowns. The findings align with the idea that communication is not only an administrative process but also an organisational tool that facilitates and enables the interpretation of operational outcomes and channels managerial thinking and evaluations of customer outcomes and organisational effectiveness.

The results validate H4, which states that governance structures significantly impact managerial perceptions of customer satisfaction. However, statistically significant governance had only marginal effects in practice. This suggests that the relationship between governance and managerial confidence is indirect through the policy of accountability and procedural clarity but not as a dominant factor affecting perceived customer satisfaction. This can be explained by the fact that managers view governance structures as a reflection of operational reliability and accountability, which aligns with the findings of (Chiarini, 2020). In the context of Malaysian manufacturing, administrative control in a hierarchical structure is combined with process-based regulations to ensure compliance with standard procedures (Al-Omari et al., 2020). The findings suggest that governance influences managerial perceptions of customer satisfaction because of the way it establishes accountability, process discipline, and transparency of operations throughout manufacturing activities. This extends the argument of (Baiyere et al., 2020) as it shows that the transparency of operations shows that governance shapes managerial evaluations by their ability to provide procedural clarity and ensure organisational consistency in variable operational contexts. The findings place governance as a compliance instrument and as a structure that can be relied upon to provide stability in processes and assure quality. The findings show that governance in the manufacturing process is considered a process of making accountability and behavioural expectations institutionalised in the manufacturing process. Thus, governance helps to “de-risk” the framework of responsibility, decision authority, and process ownership, which are elements of customer-related managerial evaluations, not just compliance. This interpretation builds on previous TQM research by focusing on the role of governance in establishing conditions for the organisation to deliver products and services reliably and in a way which the performance is clearly evaluated. Therefore, governance is no longer a control dimension but an enabling structure which builds managerial confidence.

The alignment of process standardisation with organisational goals was one of the factors that strategic planning had a significant influence on managerial perception of customer satisfaction, which proved H5. The overall practical effect of the strategy on managers’ perception of customer satisfaction was statistically significant but small. The results indicate that strategic alignment is used to provide an interpretative perspective that managers use to evaluate operational coherence, but its impact is much smaller than that of data-related practices. As suggested by (Hossain et al., 2024), managers also use strategy integration as an indicator of operational coherence and effectiveness, especially in export-oriented industries, where adherence to global standards is a major concern. The results indicate that strategic alignment enhances managerial perception as it connects process standardisation to the other priorities of the organisation and performance expectations. This interpretation builds upon (Al-Shami, 2023) by showing how managers appreciate the value of operational effectiveness better when the process of standardisation is part of a clear and integrated set of strategic goals than in the form of stand-alone technical procedures. Thus, the findings are presented as a point of strategic integration as an interpretive framework for managers to evaluate the consistency of their operations, their ability to adapt, and guarantee the reliability of their quality results. The overall message is that successful process standardisation is more likely to be strategically embedded as an institutional structure where managers can make sense of operational data based on a predictable and coordinated sense of direction.

The results show that data was the strongest predictor in both automation groups, which justifies H6. Managers view proper, timely, and actionable information as a promise of reliable operations and process stability, which informs the managerial evaluation of quality and operational performance, as supported by (Baiyere et al., 2020). The processes by which data inform managerial assessments indicate variations in process combinations and the flow of information, not technology itself. Adequate data governance allows managers to track deviations, identify defects beforehand, and make sound corrective decisions, consequently improving the perception of operational uniformity (Rane, 2023). There is global evidence that organised data practices enhance responsiveness and oversight in manufacturing systems in the US, Europe, and East Asia, as stated in the same study. Practically, the reliability of the output is reinforced when the data collection, monitoring, and evaluation approaches are systematic, and the manager can make proactive adjustments to the processes (Hamoud et al., 2020). The saliency of data as the best predictor indicates that data is becoming a key factor in the way managers assess the performance of their organisation. Data eliminate informational asymmetry by providing evidence-based information on process behaviour, deviation, and improvement options. This is an important finding that complements past research by revealing that information visibility alone can be a strategic organisational resource. Data do not just inform; they seem to influence decision-making by managers because they make the results of operations more comprehensible and help make the decision-making process more reliable.

Altogether, the findings provide evidence that the success of process standardisation is less a function of the level of automation than a function of the amount that organisations can achieve through the creation of consistency, reliability, and confidence in process control. Theoretically, this study extends the traditional view of the concept of technical quality systems in TQM to a broader perspective on standardisation as an organisational coordination capability. In manufacturing environments, managers seem to measure customer satisfaction based on the level of predictability, accountability, and process stability rather than on the level of technology. The results present a more complex picture of the role of standardisation in managerial evaluations in technologically heterogeneous manufacturing settings.