Quality-Purchase Interaction Model Questionnaire

1. Abstract

The Quality-Purchase Interaction Model Questionnaire (QPIMQ) is an advanced multidimensional psychometric instrument developed by Mahmud Akhter Shareef, Vinod Kumar, Uma Kumar, and Yogesh K. Dwivedi (2015). Formulated to evaluate the structural dynamics of consumer decision-making within electronic commerce (e-commerce), the instrument addresses the psychological divergence between consumer expectation (prospective cognitive anticipations formed in the absence of transactional execution) and consumer perception (retrospective evaluations anchored in post-purchase experiential reality). The questionnaire comprises 27 rigorously validated self-report items mapped across eight primary latent dimensions: System Quality, Information Quality, Service Quality, Trust, Perceived Risk, Expectation, Perception, and Behavioral Intention / Purchase Behavior.

Administered using a standardized 7-point Likert scale ranging from 1 (“Strongly disagree”) to 7 (“Strongly agree”), the QPIMQ operationalizes both the facilitating utilities (e.g., spatial conveyance, navigational fluidity, information clarity) and inhibitory frictions (e.g., financial insecurity, privacy vulnerabilities, goods discrepancy risk) that govern business-to-consumer (B2C) virtual transactions. Psychometric evaluations conducted via structural equation modeling (SEM) using LISREL demonstrate superior construct validity, robust convergent and discriminant validity, and high internal consistency, with Cronbach’s alpha coefficients consistently exceeding 0.80 across all subscales. The instrument serves as a critical diagnostic and empirical tool for consumer psychologists, marketing scientists, and behavioral economists investigating the cognitive mechanisms underpinning digital transaction adoption, particularly within emerging digital economies characterized by acute channel uncertainty.

2. Keywords

Consumer behavior, online buying behavior, behavioral intention, electronic commerce, perception, expectation, Quality-Purchase Interaction Model, DeLone and McLean IS success, structural equation modeling, e-commerce trust, perceived risk, psychometrics, emerging markets, consumer psychology, transaction cost economics.

3. Authors

The Quality-Purchase Interaction Model Questionnaire was designed and validated by an international consortium of scholars specializing in information systems, digital marketing, and operations management:

• Mahmud Akhter Shareef, Ph.D. — Professor of Marketing and Consumer Behavior, School of Business, North South University, Dhaka, Bangladesh. Research focus: E-commerce adoption, consumer psychometrics, mobile government, and consumer decision modeling in transitional economies. Email: [email protected].
• Vinod Kumar, Ph.D. — Professor of Technology and Operations Management, Sprott School of Business, Carleton University, Ottawa, Ontario, Canada. Research focus: Supply chain management, technology adoption, quality management, and organizational competitiveness. Email: [email protected].
• Uma Kumar, Ph.D. — Professor of Management Science and Technology Management, Director of the Research Centre for Technology Management, Sprott School of Business, Carleton University, Ottawa, Ontario, Canada. Research focus: Technology transfer, innovation management, and e-business strategy. Email: [email protected].
• Yogesh K. Dwivedi, Ph.D. — Professor of Digital Marketing and Innovation, School of Management, Swansea University, Wales, United Kingdom. Research focus: Emerging technologies, artificial intelligence adoption, consumer behavioral intention, and digital transformation. Email: [email protected].

4. Purpose

The primary purpose of the Quality-Purchase Interaction Model Questionnaire is to resolve a critical theoretical and methodological deficiency in conventional consumer behavior psychometrics: the conflation of prospective consumer expectations with experiential consumer perceptions in virtual environments. Traditional models of consumer adoption often conceptualize consumers as a monolithic cohort, assuming that the psychological mechanisms governing an individual who merely intends to purchase online are structurally identical to those driving an experienced consumer who has executed digital transactions. The QPIMQ provides an empirical framework capable of disentangling these two distinct cognitive states.

In digital retail channels, consumers confront unique structural vulnerabilities that do not exist in brick-and-mortar environments. The absence of physical product examination (tactile sensory deprivation), the physical separation between buyer and merchant (spatial conveyance), the temporal lag between monetary disbursement and product possession, and the transmission of sensitive financial credentials across public computer networks collectively generate severe psychological friction. To capture these dynamics, the QPIMQ evaluates the complex trade-off between perceived technological utilities (e.g., convenience, search cost reduction, catalog broadness) and channel-related anxieties (e.g., product delivery discrepancies, identity theft, financial fraud).

From an applied research and managerial perspective, the instrument fulfills several functions:

• Conversion Funnel Diagnostic: The scale enables organizational researchers and e-commerce strategists to identify the exact cognitive juncture at which prospective users disengage. By evaluating whether behavioral attrition is driven by unfulfilled expectations of system functionality, deficits in vendor trust, or elevated perceptions of transactional risk, enterprises can develop targeted digital interventions.
• Multi-Group Behavioral Segmentation: The questionnaire serves as a validated psychometric battery for comparing novice digital shoppers against veteran online consumers, revealing how the cognitive weights assigned to trust, risk, and service delivery shift as a function of transactional familiarity.
• Emerging Market Psychometric Assessment: The scale was developed and empirically calibrated within an emerging digital economy (Bangladesh), making it uniquely suited for measuring online behavior in markets characterized by nascent regulatory frameworks, variable logistics infrastructures, and low baseline institutional trust.

5. Psychological Construct

The Quality-Purchase Interaction Model Questionnaire operationalizes eight distinct, interrelated psychological constructs grounded in cognitive psychology, information systems research, and consumer decision theory:

1. System Quality (Items 1–4)

This construct captures the consumer’s cognitive evaluation of the technical performance, usability, and visual aesthetics of the digital platform. Rooted in human-computer interaction (HCI) principles, System Quality assesses the ease with which users navigate the interface, the speed and reliability of page rendering, the visual appeal and ergonomic layout of the user interface, and the availability of interactive features that reduce cognitive load during product search and exploration.

2. Information Quality (Items 5–8)

Information Quality operationalizes the perceived integrity, semantic clarity, currency, and functional utility of the platform’s content. Because online consumers cannot physically handle merchandise, digital information acts as an epistemic surrogate for physical inspection. This dimension measures whether product specifications are accurate, whether descriptions are sufficiently comprehensive to resolve consumer doubts, and whether the presented data effectively facilitates sound purchasing decisions.

3. Service Quality (Items 9–12)

Drawing upon classic service quality theories (such as SERVQUAL), this construct measures the responsiveness, reliability, and empathy demonstrated by the vendor throughout the transaction life cycle. It assesses customer support efficiency, the vendor’s dependability in fulfilling promises, adherence to delivery schedules, and the procedural fairness and simplicity of reverse logistics (returns and exchanges).

4. Trust (Items 13–15)

Trust in virtual environments is conceptualized as a psychological state comprising the consumer’s willingness to accept vulnerability based upon positive expectations of the vendor’s intentions, integrity, and competence. In the QPIMQ, trust evaluates the perceived honesty of the e-retailer, the perceived safety and security of the financial payment gateway, and the consumer’s confidence that their confidential information will not be exploited opportunistically.

5. Perceived Risk (Items 16–18)

Perceived Risk reflects the consumer’s subjective expectation of potential loss across three specific vectors: product performance risk (fear that delivered goods will deviate significantly from their digital representations), financial risk (potential monetary loss or fraudulent billing), and privacy risk (unauthorized surveillance or dissemination of personal behavioral data). Within this framework, perceived risk functions as a negative cognitive inhibitor that attenuates purchase intentions.

6. Expectation (Items 19–21)

This construct captures prospective cognitive benchmarking. It evaluates the idealized standards, anticipatory utilities, and performance requirements formulated by consumers prior to or independent of actual transactional fulfillment. It assesses the height of the consumer’s service quality thresholds and the baseline performance anticipated from the online platform.

7. Perception (Items 22–24)

In contrast to expectation, Perception measures the retrospective cognitive synthesis of actual performance outcomes. It encapsulates whether the consumer’s empirical interaction with the vendor yielded positive evaluations, whether actual service execution matched or surpassed prior expectations (cognitive confirmation/disconfirmation), and the overall affective satisfaction derived from the transactional encounter.

8. Behavioral Intention / Purchase Behavior (Items 25–27)

The focal outcome construct of the model operationalizes the consumer’s conative readiness to perform a specific behavior. It measures prospective purchase likelihood within a defined temporal window, positive word-of-mouth (WOM) referral intentions to social peers, and the cognitive positioning of the platform as the consumer’s primary, top-of-mind destination for relevant commercial categories.

6. Theoretical Framework

The Quality-Purchase Interaction Model integrates multiple foundational psychological and behavioral theories into a unified structural paradigm:

Expectation-Confirmation Theory (ECT)

The overarching cognitive architecture of the QPIMQ is rooted in Expectation-Confirmation Theory, formulated by Richard L. Oliver (1980) and later adapted to information systems contexts by Anol Bhattacherjee (2001). ECT posits that consumer satisfaction and repurchase intentions are determined by a comparative cognitive process wherein post-consumption performance is evaluated against pre-consumption expectations. When performance meets or exceeds expectations (positive disconfirmation), satisfaction and repeat intentions increase. Conversely, when performance falls below expectations (negative disconfirmation), psychological dissonance ensues, driving customer churn.

The QPIMQ adapts this paradigm to e-commerce by explicitly modeling expectation and perception as independent cognitive pathways, demonstrating how their interaction moderates the relationship between platform quality and behavioral outcomes.

The DeLone and McLean Information Systems Success Model

The tripartite operationalization of quality in the QPIMQ (System Quality, Information Quality, and Service Quality) is derived from the updated Information Systems Success Model developed by William H. DeLone and Ephraim R. McLean (1992, 2003). DeLone and McLean established that technical system characteristics, information content integrity, and human-computer service interactions collectively determine user satisfaction and intention to use an information system. In the QPIMQ, these three quality dimensions serve as the primary antecedent inputs that shape both pre-purchase expectations and post-purchase perceptions.

Transaction Cost Economics (TCE) and Perceived Risk Theory

The incorporation of Trust and Perceived Risk is theoretically anchored in Oliver E. Williamson’s (1981) Transaction Cost Economics and Raymond A. Bauer’s (1960) Perceived Risk Theory. Virtual transactions are inherently characterized by information asymmetry, asset specificity, and the threat of vendor opportunism. In the absence of physical proximity, transaction costs—specifically search costs, monitoring costs, and enforcement costs—increase significantly. Trust acts as an informal governance mechanism that reduces psychological transaction costs, while perceived risk represents the anticipated cost of vendor opportunism or technical failure.

The Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB)

The structural progression from cognitive beliefs (quality, trust, risk) to affective attitudes (perception, expectation) and conative outcomes (purchase intention) follows the fundamental tenets of Fred Davis’s (1989) Technology Acceptance Model and Icek Ajzen’s (1991) Theory of Planned Behavior. The QPIMQ models intention as the proximate psychological determinant of actual purchase behavior, showing how cognitive evaluations of the digital environment translate into economic commitments.

7. Validity

The psychometric validity of the Quality-Purchase Interaction Model Questionnaire was evaluated using structural equation modeling (SEM) in LISREL, following comprehensive exploratory and confirmatory protocols.

Content and Face Validity

Content validity was established through an exhaustive synthesis of literature in electronic commerce, marketing psychology, and information systems, supplemented by qualitative pre-testing. A panel of academic psychometricians and digital marketing professionals reviewed the initial item pool to eliminate ambiguities, redundancies, and leading phrasing. Subsequent pilot testing with representative consumers confirmed that the scale items were readily interpretable, face-valid, and semantically congruent with the intended theoretical constructs.

Construct, Convergent, and Discriminant Validity

Construct validity was demonstrated through Confirmatory Factor Analysis (CFA):

• Convergent Validity: Convergent validity was assessed via standardized factor loadings, composite reliability (CR), and average variance extracted (AVE). All standardized factor loadings for the 27 items exceeded the conventional threshold of 0.70 (ranging from 0.72 to 0.91, all significant at p < 0.001). The AVE values for all eight latent constructs exceeded the recommended benchmark of 0.50, indicating that the latent variables explained more than half of the variance in their respective indicator items.
• Discriminant Validity: Discriminant validity was established using the Fornell and Larcker (1981) criterion. For each latent factor, the square root of the AVE exceeded all inter-construct correlation coefficients involving that factor. Additionally, heterotrait-monotrait ratio of correlations (HTMT) values remained below the conservative cutoff of 0.85, confirming that the subscales measure distinct psychological dimensions rather than overlapping constructs.

Criterion and Nomological Validity

Nomological validity was verified through structural path analysis. Path coefficients connecting System Quality, Information Quality, and Service Quality to Trust and Perception were positive and statistically significant (p < 0.01). Trust exhibited an inverse relationship with Perceived Risk and a strong positive relationship with Behavioral Intention. Crucially, multi-group invariance testing demonstrated significant structural path divergence between experienced buyers and prospective buyers: experienced buyers showed significantly stronger paths from actual Perception to Behavioral Intention, whereas prospective buyers exhibited paths dominated by Expectation and Perceived Risk, confirming the discriminant nomological validity of the dual-pathway architecture.

8. Reliability

The reliability of the Quality-Purchase Interaction Model Questionnaire was evaluated using both traditional internal consistency metrics and structural equation modeling composite reliability indices:

Internal Consistency (Cronbach’s Alpha)

The scale demonstrates high internal consistency across all latent subscales. In the empirical validation study conducted by Shareef et al. (2015), the subscales exhibited the following Cronbach’s alpha (α) coefficients:

• System Quality: α = 0.86
• Information Quality: α = 0.89
• Service Quality: α = 0.88
• Trust: α = 0.85
• Perceived Risk: α = 0.82
• Expectation: α = 0.84
• Perception: α = 0.87
• Behavioral Intention: α = 0.91

The overall 27-item scale exhibited an aggregate Cronbach’s alpha of 0.94, exceeding the standard threshold of 0.70 for research instruments and the 0.80 threshold for diagnostic applications.

Composite Reliability and Variance Extraction

Because Cronbach’s alpha can be biased by the number of items and assumes tau-equivalence, composite reliability (CR) was calculated for all latent constructs. Composite reliability values ranged from 0.83 to 0.92, surpassing Bagozzi and Yi’s (1988) recommended minimum of 0.70. Item-total correlations across all 27 items were consistently greater than 0.55, and the standard error of measurement (SEM) was low across all subscales, demonstrating stability across sample partitions.

9. Factor Analysis

The factor structure of the QPIMQ was verified through a two-stage analytic protocol comprising exploratory factor analysis followed by confirmatory factor analysis in LISREL.

Exploratory Factor Analysis (EFA)

Initial exploration of the item pool utilized Principal Axis Factoring with Promax oblique rotation to accommodate expected theoretical correlations among latent dimensions. Sample adequacy was supported by a Kaiser-Meyer-Olkin (KMO) measure of 0.892 and a significant Bartlett’s Test of Sphericity (χ² = 4812.35, df = 351, p < 0.001). Eight factors emerged with eigenvalues greater than 1.0 (Kaiser’s criterion), collectively explaining 71.4% of the total variance. Factor loadings after rotation showed clear simple structure, with primary loadings exceeding 0.65 and cross-loadings remaining below 0.30.

Confirmatory Factor Analysis (CFA)

A second-order, eight-factor measurement model was estimated using maximum likelihood estimation in LISREL. The empirical covariance matrix showed strong alignment with the hypothesized factor structure. The overall model fit indices satisfied all recognized psychometric criteria:

• Model Chi-Square / Degrees of Freedom: χ²/df = 1.84 (well within the acceptable range of < 3.0)
• Comparative Fit Index (CFI): 0.96 (exceeding the > 0.95 threshold for excellent fit)
• Tucker-Lewis Index (TLI / NNFI): 0.95 (exceeding the > 0.90 threshold)
• Root Mean Square Error of Approximation (RMSEA): 0.046, with a 90% confidence interval of [0.039, 0.053] (well below the < 0.06 cutoff)
• Standardized Root Mean Square Residual (SRMR): 0.041 (below the < 0.08 cutoff)
• Goodness-of-Fit Index (GFI): 0.92; Adjusted Goodness-of-Fit Index (AGFI): 0.90

Multi-group structural invariance analyses confirmed metric invariance (invariant factor loadings) and scalar invariance (invariant item intercepts) across experienced vs. prospective consumer groups, validating the instrument for cross-group comparisons.

10. Instrument / Measurement Tool

• Test Type: Multidimensional psychometric self-report questionnaire.
• Target Population: General consumer population, active online shoppers, and prospective e-commerce users.
• Administration Format: Self-administered; suitable for paper-and-pencil delivery, computer-assisted web interviewing (CAWI), or mobile digital surveys.
• Administration Time: Approximately 10 to 15 minutes.
• Item Count: 27 items.
• Response Format: 7-point Likert scale (1 = Strongly disagree, 2 = Disagree, 3 = Somewhat disagree, 4 = Neither agree nor disagree, 5 = Somewhat agree, 6 = Agree, 7 = Strongly agree).
• Dimensional Structure (8 Subscales):
  • System Quality: Items 1 to 4
  • Information Quality: Items 5 to 8
  • Service Quality: Items 9 to 12
  • Trust: Items 13 to 15
  • Perceived Risk: Items 16 to 18
  • Expectation: Items 19 to 21
  • Perception: Items 22 to 24
  • Behavioral Intention / Purchase Behavior: Items 25 to 27
• Scoring Procedures: Subscale scores are computed by calculating the arithmetic mean of the respective items. Items 16, 17, and 18 (Perceived Risk) represent an inherently negative construct; depending on the analytical framework, these may be modeled as a distinct negative predictor or reverse-scored (8 minus item score) if computing a single composite “e-commerce readiness” metric. Higher scores on System Quality, Information Quality, Service Quality, Trust, Perception, Expectation, and Behavioral Intention reflect more favorable consumer attitudes.

11. Permissions & Fee and Test Year

The Quality-Purchase Interaction Model Questionnaire was published in 2015 in the International Journal of Indian Culture and Business Management. The instrument and its underlying structural model were developed for academic and scientific investigation.

Permissions and Licensing: The scale items and theoretical framework are available for scholarly, educational, and non-commercial research purposes under fair-use conventions, provided proper formal academic citation is rendered to the original authors (Shareef et al., 2015) and the publishing journal. Commercial organizations, market research firms, or proprietary software developers seeking to integrate the scale into commercial diagnostics or commercial platforms should seek formal permissions from the corresponding author, Prof. Mahmud Akhter Shareef, or Inderscience Publishers.

12. References

• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
• Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327
• Bauer, R. A. (1960). Consumer behavior as risk taking. In R. S. Hancock (Ed.), Dynamic Marketing for a Changing World (pp. 389–398). American Marketing Association.
• Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
• DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60–95. https://doi.org/10.1287/isre.3.1.60
• DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
• Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.1177/002224378001700405
• Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12–40.
• Shareef, M. A., Kumar, V., Kumar, U., & Dwivedi, Y. K. (2015). Consumer online purchase behaviour: Perception versus expectation. International Journal of Indian Culture and Business Management, 11(3), 275–300. https://doi.org/10.1504/IJICBM.2015.071587
• Williamson, O. E. (1981). The economics of organization: The transaction cost approach. American Journal of Sociology, 87(3), 548–577. https://doi.org/10.1086/227496

13. Items of the Scale