Customer Participation, Perceived Service Innovation, and Loyalty–Model Inventory

Abstract

The Customer Participation, Perceived Service Innovation, and Loyalty–Model Inventory is an empirically validated psychometric and behavioral assessment instrument formulated by Dr. Munyaradzi W. Nyadzayo, Dr. Civilai Leckie, and Prof. Lester W. Johnson in 2023. Grounded in Service-Dominant (S-D) Logic, Diffusion of Innovations Theory, and relationship marketing frameworks, the instrument systematically captures the complex interplay between active consumer resource integration (co-creation), multifaceted consumer appraisals of service innovation, cognitive-affective evaluative states, and behavioral loyalty intentions. Comprising a total of 22 items distributed across seven distinct reflective dimensions, the inventory measures: (1) Customer Participation, (2) Perceived Innovativeness, (3) Perceived Service Newness, (4) Perceived Relative Advantage, (5) Perceived Value, (6) Customer Satisfaction, and (7) Customer Loyalty.

Items in the inventory were adapted from established operationalizations in marketing literature (e.g., Yim et al., 2012; Lowe & Alpert, 2015; Harris & Goode, 2004) and contextualized within digitally mediated and technology-enabled service platforms (such as the app-based mobility service Uber). All constructs are measured on a standard 7-point Likert scale ranging from 1 (“Strongly Disagree”) to 7 (“Strongly Agree”). In the baseline psychometric evaluation conducted among adult consumers in Australia, the measurement model was validated using covariance-based structural equation modeling (CB-SEM). The measurement model demonstrated exemplary goodness-of-fit indices: χ²(188) = 326.93 (χ²/df = 1.74, p < 0.001), Root Mean Square Residual (RMR) = 0.038, Tucker–Lewis Index (TLI) = 0.97, Comparative Fit Index (CFI) = 0.98, and Root Mean Square Error of Approximation (RMSEA) = 0.047. Internal consistency reliability is robust across all subscales, with Cronbach’s α coefficients ranging from 0.81 to 0.95 and Composite Reliability (CR) values exceeding 0.70. Convergent and discriminant validities were supported via Average Variance Extracted (AVE > 0.50), the Fornell–Larcker criterion, and the Heterotrait–Monotrait ratio of correlations (HTMT ratios between 0.39 and 0.76). This inventory serves as a vital diagnostic and predictive framework for researchers and practitioners examining customer co-production, technology-driven service disruption, and long-term brand equity.

Keywords

Customer Participation, Service Innovation, Perceived Innovativeness, Perceived Service Newness, Perceived Relative Advantage, Perceived Value, Customer Satisfaction, Customer Loyalty, Structural Equation Modeling, Value Co-Creation, Service-Dominant Logic, Psychometrics.

Authors

The Customer Participation, Perceived Service Innovation, and Loyalty–Model Inventory was developed and validated by an international research team specializing in marketing analytics, relationship marketing, consumer psychology, and service innovation:

• Munyaradzi W. Nyadzayo, Ph.D. (Corresponding Author)
  Affiliation: Faculty of Business, University of Wollongong in Dubai, Dubai, United Arab Emirates.
  ORCID: 0000-0003-0802-2119
  Email: [email protected]
• Civilai Leckie, Ph.D.
  Affiliation: Department of Management and Marketing, Swinburne University of Technology, Melbourne, Australia.
• Lester W. Johnson, Ph.D.
  Affiliation: Department of Management and Marketing, Swinburne University of Technology, Melbourne, Australia.
  ORCID: 0000-0003-3577-2804

Purpose

The primary purpose of the Customer Participation, Perceived Service Innovation, and Loyalty–Model Inventory is to provide an empirically validated, theoretically comprehensive diagnostic tool that explains how collaborative customer actions interface with perceived characteristics of technological and procedural service innovations to drive customer evaluation and behavioral retention. In the modern service economy, the boundaries between the firm as a producer and the consumer as a passive recipient have dissolved. Through ubiquitous smartphone applications, cloud computing, and real-time algorithmic platforms (such as ridesharing, peer-to-peer accommodation, and fintech), consumers actively configure services by inputting operational information, co-designing service experiences, and engaging in reciprocal performance. Simultaneously, service providers continuously implement systemic innovations to streamline customer experiences and secure market differentiation.

Despite the proliferation of research on customer engagement and technological innovation, empirical investigations often examine customer participation or service innovation in isolation, treating innovation as an objective organizational attribute rather than a subjective consumer perception. The instrument addresses this void by operationalizing service innovation as a three-dimensional cognitive appraisal: innovativeness (the firm’s perceived originality and creative dynamism), service newness (the extent to which the core service delivery departs from traditional norms), and relative advantage (the perceived functional, economic, or convenience superiority over preexisting alternatives). By bridging these innovation facets with active customer participation behaviors, the inventory allows researchers and managers to delineate how direct consumer inputs amplify or attenuate the psychological utility derived from innovative service designs.

Beyond theoretical modeling, the inventory fulfills practical and clinical needs in strategic market management, consumer analytics, and organizational design:

• Deconstructing Value Co-Creation: It identifies whether customer operational burden (participation) yields downstream psychological capital (perceived value and satisfaction) or creates friction when innovation is perceived as overly complex or unfamiliar.
• Platform and Interface Optimization: For digital service designers, the inventory pinpoints whether consumer resistance or attrition stems from deficits in functional advantage, insufficient perceived novelty, or an unrewarding co-production workflow.
• Customer Lifetime Value and Retention Modeling: By charting the direct and indirect pathways terminating in customer loyalty, the instrument provides an empirical baseline for quantifying the commercial return on service innovation investments.
• Cross-Industry Diagnostic Benchmarking: The scale enables comparative benchmarking across diverse service environments—ranging from digital gig economy ecosystems to technology-mediated banking, healthcare portals, and automated hospitality.

Psychological Construct

The inventory models a structured nomological network comprising seven distinct, theoretically anchored constructs. Each dimension captures an essential cognitive, affective, or behavioral facet of the service consumption episode:

1. Customer Participation

Rooted in value co-creation theory, Customer Participation represents the degree to which customers actively allocate mental, physical, temporal, and informational resources to the service delivery process. In platform-mediated environments, this is not merely passive compliance; it involves providing accurate contextual information, exerting effort to navigate self-service technologies, collaborating with service personnel or algorithms, and taking co-responsibility for the service outcome. For instance, in an app-based ridesharing scenario, participation includes entering precise geographic destinations, tracking vehicle progress, interacting constructively with drivers, and offering post-trip ratings.

2. Perceived Innovativeness

Perceived Innovativeness reflects the consumer’s subjective perception of the service provider as an innovative, forward-thinking, and dynamic market leader. Rather than assessing specific features, this dimension captures the overarching reputation and corporate capacity of the firm to pioneer novel ideas, embrace technological disruption, and proactively modernize traditional service delivery paradigms. It operates as a higher-order halo that elevates consumer confidence and brand prestige.

3. Perceived Service Newness

Perceived Service Newness measures the consumer’s cognitive appraisal of the extent to which the specific service offering represents an unfamiliar, distinct, or radically differentiated departure from conventional service configurations. It taps into the novelty aspect of the innovation continuum, assessing whether the service is experienced as fresh, unprecedented, and distinct from legacy marketplace alternatives.

4. Perceived Relative Advantage

Derived from Everett Rogers’ classic diffusion paradigms, Perceived Relative Advantage captures the customer’s evaluation of the functional, operational, temporal, and economic superiority of the innovative service compared to existing substitutes. Consumers weigh whether the innovative service provides greater efficiency, convenience, speed, and cost-effectiveness than traditional, non-innovative channels.

5. Perceived Value

Perceived Value represents an overarching cognitive appraisal of the trade-off between the give-and-get components of the service encounter. It encompasses the customer’s holistic evaluation of the net utility received, balancing the sacrifices incurred (monetary cost, effort, cognitive load, time invested) against the benefits obtained (convenience, speed, reliability, status). Perceived value operates as a critical cognitive mediator linking the technical attributes of innovation and customer exertion to subsequent emotional satisfaction.

6. Customer Satisfaction

Customer Satisfaction is operationalized as an evaluative, emotionally grounded, cumulative affective state resulting from the overall consumption history with the service brand. Following expectancy-disconfirmation theory, it evaluates whether the cumulative service experience meets, exceeds, or falls short of the customer’s initial expectations, desires, and subjective standards of performance.

7. Customer Loyalty

The terminal dependent construct, Customer Loyalty, encompasses both attitudinal and behavioral commitment toward the service provider. Attitudinal loyalty is reflected in positive word-of-mouth recommendations, brand advocacy, and psychological attachment. Behavioral loyalty is manifested through repeat purchase intentions, increased wallet share, continuous patronage over time, and resistance to competitive switching incentives.

Theoretical Framework

The conceptual architecture of the Customer Participation, Perceived Service Innovation, and Loyalty–Model Inventory is anchored at the intersection of three foundational paradigms within consumer psychology and marketing science:

1. Service-Dominant Logic (S-D Logic)

Pioneered by Stephen L. Vargo and Robert F. Lusch (2004, 2008), S-D Logic posits that value is not merely embedded in physical goods or output units produced by a firm (the traditional “Goods-Dominant Logic”); rather, value is inherently idiosyncratic, experiential, and continuously co-created through the integration of resources between actors. Within S-D Logic, the customer is always an active co-creator of value. Operant resources (knowledge, skills, digital literacy, and communicative effort) contributed by the customer are integrated with the firm’s operant resources (algorithmic dispatching, software design, logistical infrastructure) to yield “value-in-use.” The inventory captures this theoretical reality by situating Customer Participation as an antecedent driver that directly shapes the customer’s perception of the service’s utility and outcomes.

2. Diffusion of Innovations Theory

The multidimensional modeling of service innovation in this instrument draws directly upon Everett M. Rogers’ (1995, 2003) Diffusion of Innovations paradigm. Rogers articulated that the rate and depth of adoption of an innovation depend fundamentally on perceived innovation attributes: relative advantage, compatibility, complexity, trialability, and observability. Nyadzayo, Leckie, and Johnson (2023) synthesized these attributes into three focused consumer dimensions: Innovativeness, Service Newness, and Relative Advantage. By disentangling the abstract corporate capability (innovativeness) from the novelty of the product form (service newness) and its practical benefit over alternatives (relative advantage), the inventory offers a nuanced lens for studying why certain technological service transitions succeed while others encounter consumer resistance.

3. Cognitive-Affective-Conative Hierarchy

The structural flow of the inventory reflects the classic psychological hierarchy of effects (Cognition → Affect → Conation) articulated by Richard L. Oliver (1997, 1999). In this nomological sequence:

• Cognitive Phase: Customers cognitively process structural and functional attributes (Participation, Innovativeness, Service Newness, Relative Advantage) and appraise overall net worth (Perceived Value).
• Affective Phase: Cognitive appraisal triggers cumulative emotional satisfaction (Satisfaction), representing the affective evaluation of the ongoing service relationship.
• Conative Phase: Affective satisfaction converts into conative intent and behavioral commitment (Loyalty), characterized by repeat patronage and proactive advocacy.

Validity

The psychometric validity of the Customer Participation, Perceived Service Innovation, and Loyalty–Model Inventory has been rigorously established using rigorous methodological protocols in structural equation modeling (SEM) and psychometric testing.

Content Validity

Content validity was confirmed prior to empirical data collection through systematic panels of academic experts specializing in marketing theory and psychometric measurement. Experts evaluated the initial item pool to ensure exhaustive domain sampling, semantic clarity, contextual relevance to app-based digital services, and theoretical fidelity to the underlying constructs. Items were refined, adapted from premier peer-reviewed scales, and screened to eliminate ambiguities or double-barreled phrasings.

Convergent Validity

Convergent validity was examined using standard criteria established by Hair, Black, Babin, and Anderson (2010):

• All standardized factor loadings (λ) from the confirmatory factor analysis were statistically significant at p < 0.001 and substantially exceeded the standard threshold of 0.70.
• The Average Variance Extracted (AVE) for each of the seven latent constructs exceeded the recommended benchmark of 0.50, demonstrating that the latent constructs account for more than 50% of the variance in their respective measurement indicators.
• Composite Reliability (CR) values across all constructs comfortably surpassed the 0.70 cut-off, confirming strong convergent validity.

Discriminant Validity

Discriminant validity was established via two stringent statistical methodologies:

1. Fornell–Larcker Criterion (1981): The square root of the AVE for each latent construct along the diagonal was strictly greater than any bivariate correlation between that construct and any other latent variable in the model. This confirmed that each construct shares more variance with its own indicators than with any other theoretical dimension.
2. Heterotrait–Monotrait Ratio of Correlations (HTMT): Following recent psychometric recommendations by Henseler, Ringle, and Sarstedt (2015), HTMT ratios were calculated. All observed HTMT values ranged between 0.39 and 0.76, falling well below the conservative threshold of 0.85 (and the absolute ceiling of 0.90), definitively proving the absence of multicollinearity or construct overlap.

Nomological and Predictive Validity

Structural path modeling supported the nomological and predictive validity of the scale. Significant path coefficients confirmed the theoretical hypotheses: Customer Participation, Innovativeness, and Relative Advantage positively drive Perceived Value and Satisfaction, which in turn account for substantial variance in Customer Loyalty (R² values confirming strong explanatory power).

Reliability

The reliability of the Customer Participation, Perceived Service Innovation, and Loyalty–Model Inventory was thoroughly assessed through internal consistency metrics and composite score estimations across the entire sample of consumers:

Internal Consistency Reliability

Internal consistency was calculated using Cronbach’s alpha (α) for each subscale:

• Customer Participation: α = 0.84
• Perceived Innovativeness: α = 0.91
• Perceived Service Newness: α = 0.81
• Perceived Relative Advantage: α = 0.88
• Perceived Value: α = 0.90
• Customer Satisfaction: α = 0.95
• Customer Loyalty: α = 0.93

All observed alpha coefficients ranged between 0.81 and 0.95, comfortably exceeding the widely accepted psychometric threshold of 0.70 for research instruments (Nunnally & Bernstein, 1994; Hair et al., 2010), demonstrating exceptional item homogeneity and minimal measurement error.

Composite Reliability and Variance Extraction

To guard against the known limitations of Cronbach’s alpha (such as its assumption of tau-equivalence), the authors evaluated Composite Reliability (CR) for each construct. All CR values surpassed 0.70, ranging from 0.83 to 0.95. Average Variance Extracted (AVE) values ranged from 0.58 to 0.82, reinforcing the robust reliability of the measurement model.

Factor Analysis

The factorial structure and underlying dimensionality of the 22-item inventory were comprehensively examined through Covariance-Based Structural Equation Modeling (CB-SEM) using maximum likelihood estimation in AMOS.

Confirmatory Factor Analysis (CFA)

A rigorous seven-factor Confirmatory Factor Analysis (CFA) was conducted to test the hypothesized measurement structure where each of the 22 indicators was constrained to load exclusively onto its assigned latent construct, with construct covariances freely estimated. The global goodness-of-fit indices demonstrated an exemplary fit to the empirical data:

• Chi-Square Statistic: χ²(188) = 326.93
• Normed Chi-Square: χ²/df = 1.74 (substantially below the recommended ceiling of 3.0, indicating excellent fit)
• Tucker–Lewis Index (TLI): 0.97 (well above the 0.95 threshold)
• Comparative Fit Index (CFI): 0.98 (exceeding the standard 0.95 benchmark)
• Root Mean Square Residual (RMR): 0.038 (below the 0.05 cut-off)
• Root Mean Square Error of Approximation (RMSEA): 0.047 with a 90% confidence interval ranging from 0.039 to 0.055 (well below the stringent 0.06 cut-off proposed by Hu & Bentler, 1999)

Assessment of Common Method Bias (CMB)

Because all data were collected via self-report cross-sectional surveys, the authors conducted Harman’s one-factor test to ensure that common method variance did not artifactually inflate construct relationships. When all 22 indicators were forced into a single global latent factor, the resulting fit was unacceptable:

• χ²(252) = 3402.201
• χ²/df = 13.50 (indicative of severe model misspecification)
• SRMR: 0.192
• TLI: 0.57
• CFI: 0.61
• RMSEA: 0.171

The drastic deterioration in model fit from the seven-factor model to the single-factor model proves that common method bias does not pose a threat to the validity of the inventory or its empirical findings.

Instrument / Measurement Tool

The structural characteristics, administration protocols, and scoring metrics of the Customer Participation, Perceived Service Innovation, and Loyalty–Model Inventory are outlined below:

• Test Type: Original psychometric inventory / self-report survey scale.
• Format: Electronic, computer-assisted web interview (CAWI), or pen-and-paper questionnaire.
• Construct Coverage: 7 correlated latent factors measuring behavioral co-production, cognitive innovation appraisals, perceived value, satisfaction, and customer retention.
• Item Count: 22 items distributed across seven subscales:
  • Customer Participation: 3 items
  • Perceived Innovativeness: 3 items
  • Perceived Service Newness: 3 items
  • Perceived Relative Advantage: 3 items
  • Perceived Value: 3 items
  • Customer Satisfaction: 3 items
  • Customer Loyalty: 4 items
• Response Scale: 7-point Likert-type scale anchored as follows:
  • 1 = Strongly Disagree
  • 2 = Disagree
  • 3 = Somewhat Disagree
  • 4 = Neither Agree nor Disagree (Neutral)
  • 5 = Somewhat Agree
  • 6 = Agree
  • 7 = Strongly Agree
• Target Population: Adult consumers (18 years and older; spanning young adulthood, middle age, and older adults) who regularly or occasionally utilize technology-enabled, app-based, or collaborative service platforms.
• Administration Time: Approximately 6 to 10 minutes to complete the full 22-item inventory.
• Scoring and Indexing:
  • Subscale scores are computed by calculating the unweighted arithmetic mean of the items comprising each specific dimension (yielding dimensional scores ranging from 1.00 to 7.00).
  • Alternatively, factor-score weights derived from structural equation modeling can be applied for higher-order structural modeling.
  • Higher scores represent greater levels of customer participation, higher perceived innovation, enhanced perceived value, superior satisfaction, and stronger customer loyalty.

Permissions & Fee and Test Year

The Customer Participation, Perceived Service Innovation, and Loyalty–Model Inventory was formally established and published in 2023 in the peer-reviewed journal Marketing Intelligence & Planning (Emerald Publishing Limited). The authors and copyright holders grant permission for academic, educational, and non-commercial research purposes provided appropriate bibliographic citation is accorded to the original publication. For commercial applications, proprietary diagnostic adaptations, or large-scale consulting deployments, researchers and organizations should contact the corresponding author, Dr. Munyaradzi W. Nyadzayo ([email protected]), or consult Emerald Publishing Limited regarding copyright permissions.

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Items of the Scale