A Dataset on the Impact of Leader-Member Exchange (LMX) and Capacity-Based LMX Differentiation (CLMXD) on Perceived Overqualification and Cyberloafing in the Workplace

This dataset originates from a multi-method research project titled "Is High-Quality LMX Always a Blessing? Exploring the Impact of LMX and Capacity-Based LMX Differentiation on Followers’ Perceived Overqualification and Cyberloafing Engagement." The project investigates how the interaction between leader-member exchange (LMX) quality and capacity-based LMX differentiation (CLMXD) shapes employees’ perceptions of overqualification and their engagement in cyberloafing behaviors. It further explores how (in)congruence between LMX and CLMXD influences employee outcomes through perceived overqualification.1 Study 1: Multi-Wave Field SurveyData generation procedures: Data for Study 1 were collected through an online questionnaire survey using a snowball sampling approach. Participants were corporate employees from various industries located in Beijing, China. Recruitment was initiated via the researcher's personal network, followed by distribution through a WeChat group.Temporal and geographical scope: The survey was conducted over two waves, separated by a two-week interval, in Beijing, China, during 2024.Data processing methods: Data were matched across the two time points using the last four digits of participants’ telephone numbers. Participants failing attention checks were excluded. Cookies were used to prevent multiple submissions from the same device.Data structure: The dataset consists of 271 valid cases. Each row represents one participant. Variables include Leader-Member Exchange (LMX), Capacity-Based LMX Differentiation (CLMXD), Perceived Overqualification (POQ), Cyberloafing behaviors, and demographic variables such as gender, age, education, position, and tenure. All scales used a 7-point Likert format.Measurement and units: Responses were recorded on scales from 1 (strongly disagree) to 7 (strongly agree).Missing data: There were no missing data, as all questions were mandatory.Error handling: Participants with inconsistent or careless responses (e.g., failing attention checks) were excluded to ensure data quality.Data file details: The file is provided in .sav format (SPSS file), containing 271 rows and 43 columns.2 Study 2: Experimental Vignette StudyData generation procedures: Study 2 employed an experimental vignette methodology (EVM). Participants were randomly assigned to one of four conditions in a 2 (high vs. low LMX) × 2 (high vs. low CLMXD) between-subjects design. They were asked to vividly imagine themselves as protagonists in the scenarios and subsequently respond to a series of measures.Temporal and geographical scope: The data were collected in 2024 from MBA students enrolled in a Chinese university.Data processing methods: Responses were screened for data quality. Surveys with missing answers, multiple selections on single-choice questions, or indications of non-serious answering were excluded.Data structure: The dataset consists of 164 valid cases. Each row corresponds to one participant. Variables include LMX manipulation check, CLMXD manipulation check, and Perceived Overqualification (POQ). Demographic variables such as gender, age, education, and position are also included.Measurement and units: All variables were measured using 7-point Likert-type scales, from 1 (strongly disagree) to 7 (strongly agree).Missing data: All missing or invalid entries were excluded during the cleaning process; thus, the final dataset contains no missing data.Error handling: Data quality was ensured through attention to missing responses and response patterns. Only complete and reliable responses were retained.Data file details: The file is provided in .sav format (SPSS file), containing 164 rows and 34 columns.