Dataset on Strategic Competitive Positioning, Service Quality, Servicescape, Perceived Value, and Customer Satisfaction in Malaysian Hypermarkets
Description
The dataset provides cross-sectional survey data on service quality, servicescape, perceived value, and customer satisfaction among hypermarket shoppers in Malaysia, with respondents classified according to the strategic positioning of the hypermarket, namely cost-leadership or differentiation. Data were collected in Kuala Lumpur, Malaysia, from August to October 2025 using a self-administered structured questionnaire distributed to hypermarket visitors at store entrances and exits. Of the 480 questionnaires distributed, 405 usable responses were retained, representing an 84.4% usable response rate. The final sample comprised 201 respondents from cost-leadership strategy hypermarkets and 204 respondents from differentiation strategy hypermarkets. The dataset contains respondent-level demographic variables, including gender, age, race, highest qualification, and monthly income, together with a strategic group variable identifying the hypermarket positioning category. The substantive measurement section consists of 37 items measured on a seven-point Likert scale ranging from 1 = strongly disagree to 7 = strongly agree. These items measure four constructs: service quality with 9 items, servicescape with 14 items, perceived value with 4 items, and customer satisfaction with 10 items. The SPSS dataset also includes computed composite scores for the four constructs and a Mahalanobis distance variable used during data screening. Each row represents one respondent, and each column represents one survey variable, making the dataset suitable for descriptive analysis, reliability and validity assessment, regression analysis, PLS-SEM, and multi-group comparison between cost-leadership and differentiation hypermarkets.
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Steps to reproduce
Data were obtained through a cross-sectional field survey of hypermarket shoppers in Kuala Lumpur, Malaysia, conducted between **August and October 2025**. The study followed a standardized workflow designed to ensure that the dataset can be reproduced in comparable retail settings. First, a structured questionnaire was developed based on established measures for **service quality, servicescape, perceived value, and customer satisfaction** (see Table 2: item codebook). All items were operationalized using a **7-point Likert scale** (1 = strongly disagree to 7 = strongly agree). The instrument also included a demographic section capturing **gender, age, race, education level, and monthly income** (Table 1). The questionnaire was formatted for self-administration and used identical wording and response anchors across respondents. Second, data collection was implemented using **non-probability convenience sampling** at selected hypermarkets. Respondents were approached at store entry/exit points and invited to participate voluntarily. After a brief explanation of the study purpose, participants provided informed consent and completed the questionnaire anonymously. A total of **480** questionnaires were distributed and **405 usable responses** were retained after basic screening for completeness. Respondents were classified into two groups—**cost-leadership (n = 201)** and **differentiation (n = 204)**—based on the strategic orientation of the hypermarket where the survey was administered, enabling group-based comparisons. Third, data were coded and compiled into **Raw Data.xlsx**, where each row represents one respondent and columns represent demographics and item responses (SQ1–SQ9; SS1–SS14; PV1–PV4; SF1–SF9). Descriptive statistics were computed for all items (Table 3). Measurement quality and construct validity were evaluated using **PLS-SEM** in **SmartPLS 4**, including indicator loadings, Cronbach’s alpha, composite reliability, and AVE (Table 4), discriminant validity via **HTMT** (Table 5), and predictive/explanatory metrics (**R² and Q²**) for customer satisfaction (Table 6). This end-to-end protocol—instrument specification, in-store recruitment and anonymous self-completion, dataset coding in spreadsheet format, and validation using SmartPLS—can be replicated by applying the same questionnaire and grouping procedure in other hypermarkets or regions.