Service Convenience Scale (SERVCON)

1. Abstract

The Service Convenience Scale (SERVCON) is an extensively validated psychometric instrument designed to assess consumers' perceptions of the non-monetary time and effort expenditures associated with purchasing, acquiring, using, and re-engaging with services. Rooted conceptually in the five-type service convenience taxonomy developed by Berry, Seiders, and Grewal (2002) and formally operationalized into a standardized measurement tool by Seiders, Voss, Grewal, and Godfrey (2007), the SERVCON scale addresses a critical oversight in the consumer behavior and services marketing literature: the systematic conceptualization and empirical measurement of convenience across the full service delivery cycle.

The scale consists of 17 items structured across five distinct reflective first-order dimensions: Decision Convenience (3 items), Access Convenience (4 items), Transaction Convenience (3 items), Benefit Convenience (4 items), and Post-benefit Convenience (3 items). Each item is evaluated using a 7-point Likert scale anchored from 1 (“Strongly Disagree”) to 7 (“Strongly Agree”). Extensive empirical evaluations across diverse service contexts—including retail banking, grocery retailing, discount stores, health clubs, and digital commerce—demonstrate superior psychometric properties. Internal consistency estimates across subscales routinely yield Cronbach's alpha coefficients exceeding .85 and composite reliabilities above .88. Confirmatory factor analytic models support a correlated five-factor structure demonstrating distinct convergent, discriminant, and criterion-related validities.

By measuring consumers' cognitive appraisals of transaction costs, effort minimization, and temporal friction, SERVCON provides researchers and service managers with an actionable diagnostic tool for isolating pain points throughout the customer journey, optimizing customer lifetime value, evaluating technology-mediated service innovations, and modeling structural relationships with service quality, customer satisfaction, and behavioral loyalty.

2. Keywords

Service Convenience Scale, SERVCON, consumer convenience, decision convenience, access convenience, transaction convenience, benefit convenience, post-benefit convenience, perceived effort, time expenditure, services marketing, psychometric validation

3. Authors

The formal conceptualization, development, and psychometric validation of the SERVCON scale represents the collaborative work of prominent marketing scholars specializing in service operations, consumer decision-making, and retail management:

• Kathleen Seiders, Ph.D. — Professor of Marketing, Carroll School of Management, Boston College, Chestnut Hill, Massachusetts, USA. Dr. Seiders' research program focuses on retail strategy, customer convenience, service innovation, and consumer welfare.
• Glenn B. Voss, Ph.D. — Marilyn and Leo Faydir Endowed Chair in Marketing, Cox School of Business, Southern Methodist University, Dallas, Texas, USA. Dr. Voss specializes in customer relationship management, strategic services marketing, and quantitative modeling of customer satisfaction and retention.
• Dhruv Grewal, Ph.D. — Toyota Chair in Commerce and Electronic Business and Professor of Marketing, Babson College, Babson Park, Massachusetts, USA. Dr. Grewal is widely recognized for his foundational scholarship in retail pricing, value perceptions, digital technology integration, and service ecosystems.
• Andrea L. Godfrey, Ph.D. — Associate Professor of Marketing, A. B. Freeman School of Business, Tulane University, New Orleans, Louisiana, USA. Dr. Godfrey investigates customer relationship dynamics, multi-channel service delivery, and marketing analytics.

Foundational conceptual architecture was established in prior theoretical scholarship authored by Leonard L. Berry (Texas A&M University), Kathleen Seiders, and Dhruv Grewal in their landmark 2002 framework published in the Journal of Marketing.

4. Purpose

In modern service economies, consumer perceptions of value are shaped not only by financial costs and core service quality, but also significantly by non-monetary costs. These non-monetary expenditures consist primarily of consumer opportunity costs of time and physiological, cognitive, and emotional effort. Before the development of the SERVCON scale, empirical investigations into convenience were fragmented. Researchers often treated convenience as a simplistic, unidimensional construct or conflated it with spatial proximity, operating hours, or physical accessibility. This conceptual oversimplification obscured the reality that consumers encounter distinct hurdles across consecutive phases of service interaction.

The primary purpose of the Service Convenience Scale is to provide a comprehensive, multidimensional, psychometrically robust instrument capable of isolating and measuring consumer evaluations of time and effort expenditures across five sequential stages of the service consumption process:

1. Pre-purchase deliberation (evaluating alternatives and deciding on a service provider),
2. Initial interaction and approach (gaining access to the provider's physical facility or virtual interface),
3. Financial settlement (authorizing payment and concluding contractual agreements),
4. Core consumption (experiencing the primary functional or experiential benefits of the service), and
5. Post-purchase re-engagement (resolving service failures, executing returns, making inquiries, or initiating subsequent transactions).

From a clinical and applied consumer research perspective, SERVCON serves as an indispensable diagnostic measurement device. Modern service organizations frequently deploy significant capital expenditures to streamline customer interactions—such as implementing self-checkout kiosks, automated mobile applications, algorithmic recommendations, and omni-channel customer service desks. Without a granular measurement tool, organizations cannot determine which specific touchpoint reduces customer cognitive strain or where unexpected friction occurs. For instance, a firm might offer exceptional physical accessibility (Access Convenience) while frustrating customers with cumbersome payment systems (Transaction Convenience) or opaque dispute mechanisms (Post-benefit Convenience).

Furthermore, in academic research, the SERVCON scale allows investigators to formulate and test nuanced structural models. It enables scholars to examine how personal constraints (e.g., consumer time poverty, perceived stress, cognitive load, technological literacy) and situational parameters (e.g., hedonic versus utilitarian consumption goals, high-contact versus low-contact service environments) moderate the impact of distinct convenience dimensions on customer satisfaction, brand equity, trust, and behavioral retention. Ultimately, the scale clarifies how firms can optimize customer non-monetary investments to build competitive differentiation and sustainable loyalty.

5. Psychological Construct

Service convenience is formally defined by Berry et al. (2002) and Seiders et al. (2007) as a consumer's perception of minimized time and effort expenditures related to purchasing or using a service. The construct is grounded in the psychological economics of effort and time perception. Time expenditure represents the objective chronological duration as well as the subjective duration experienced by the consumer during service interactions. Effort expenditure encompasses three distinct psychological and physiological resources:

• Physical effort: The muscular and bodily energy required to travel, navigate spaces, carry goods, or execute mechanical steps;
• Cognitive effort: The mental bandwidth, information processing capacity, executive control, and decision-making strain demanded to evaluate alternatives, interpret instructions, or master complex service interfaces;
• Emotional effort: The psychological strain, anxiety, irritation, or emotional regulation experienced when dealing with delays, ambiguous procedures, unresponsive personnel, or service failures.

The SERVCON scale captures these psychological dynamics across five granular, interrelated, yet conceptually distinct dimensions:

Decision Convenience

Decision Convenience pertains to the consumer's perception of the ease and speed with which they can decide what service to purchase and from whom. In modern marketplace environments characterized by choice overload, consumers often experience cognitive fatigue when attempting to locate product information, compare service packages, decipher fee structures, or assess whether a service meets their specific idiosyncratic needs. High decision convenience reduces cognitive search costs. For example, an insurance platform that presents transparent, side-by-side policy comparisons with algorithmic filtering offers high decision convenience, whereas a financial institution with buried fee schedules and ambiguous portfolio options induces high decision friction.

Access Convenience

Access Convenience represents the consumer's perception of the time and effort required to initiate contact with or arrive at the service provider. This dimension encompasses spatial proximity, ease of vehicular parking, accessibility via public transit, hours of operation, website loading speed, intuitive mobile app navigation, and responsiveness of telephone switchboards. In a physical healthcare context, access convenience reflects whether a patient can schedule a prompt appointment, easily locate the medical pavilion, and find accessible parking. In a digital context, it reflects how quickly a user can connect to a live virtual consultant without navigating labyrinthine interactive voice response menus.

Transaction Convenience

Transaction Convenience reflects the consumer's evaluation of the time and physical/cognitive effort expended to complete the financial and administrative exchange. Transactions represent an inherent consumer burden: exchanging financial resources for a promised benefit. Friction during this stage—such as lengthy queuing at physical retail counters, redundant data entry on e-commerce forms, rejected payment gateways, or rigid payment method options—amplifies perceived transaction costs. High transaction convenience is exemplified by contactless near-field communication (NFC) checkouts, one-click digital purchases, and streamlined electronic invoicing that allows the consumer to consummate the purchase seamlessly.

Benefit Convenience

Benefit Convenience involves the consumer's perception of the time and effort required to experience the core functional or hedonic utility of the service once the transaction has commenced. Unlike transaction convenience, which is administrative, benefit convenience relates directly to the core service delivery process. In a fast-casual dining establishment, benefit convenience reflects how swiftly and effortlessly the consumer receives and consumes their meal. In a commercial fitness facility, it corresponds to whether equipment is immediately available, calibrated, and ready for use without waiting or requiring complex manual configuration. In enterprise cloud software, benefit convenience reflects the intuitive usability that allows the subscriber to immediately execute analytical workflows without extensive onboarding friction.

Post-benefit Convenience

Post-benefit Convenience captures the ease and speed with which consumers can re-engage with the service provider after the primary service interaction has concluded. This dimension is paramount in managing post-purchase dissonance, product returns, warranty repairs, technical troubleshooting, billing inquiries, and repeat transactions. Consumers often experience heightened vulnerability following an unsatisfactory purchase; if returning a defective item requires traversing complex bureaucratic approvals, paying restocking fees, or enduring contentious interactions with service agents, perceived post-benefit convenience collapses. Conversely, automated no-questions-asked digital return portals, scheduled home pickups, and responsive omnichannel resolution desks reflect superior post-benefit convenience.

6. Theoretical Framework

The Service Convenience Scale is anchored in several converging theoretical paradigms spanning cognitive psychology, behavioral economics, and services management:

Theory of Economic Time Allocation and Household Production

The earliest theoretical foundation stems from Gary Becker's (1965) landmark economic theory of time allocation. Becker posited that households are not merely passive consumers of market goods, but active producers that combine market goods with non-market household time to yield fundamental utility-bearing commodities. Within this framework, time possesses a quantifiable shadow price equivalent to foregone labor market earnings or subjective leisure utility. When consumers engage with service providers, every minute spent waiting, traveling, deciphering service menus, or resolving billing errors represents a direct deduction from their temporal budget. In contemporary societies marked by widespread “time poverty,” consumers actively seek service configurations that conserve temporal capital, treating convenience as a direct multiplier of net utility.

Transaction Cost Economics and Information Search Theory

The scale integrates principles from Oliver Williamson's Transaction Cost Economics (TCE) and George Stigler's Economics of Information. TCE posits that economic exchanges entail substantial friction beyond the nominal purchase price, including search and information costs, bargaining and contracting costs, and policing and enforcement costs. The five SERVCON dimensions systematically map onto these classical transaction costs: Decision convenience minimizes ex-ante information search costs; Access and Transaction convenience minimize contracting and operational transaction costs; and Post-benefit convenience minimizes ex-post monitoring, enforcement, and maladaptation costs.

The Principle of Least Effort and Cognitive Load Theory

From cognitive psychology, SERVCON draws upon George Zipf's (1949) Principle of Least Effort and modern Cognitive Load Theory (Sweller, 1988). Zipf demonstrated that human beings naturally choose paths of behavioral action that minimize total energy expenditure. In cognitive science, working memory and executive mental faculties represent finite, easily depleted biological resources. Cognitive load theory differentiates between intrinsic, germane, and extraneous cognitive load. In a service encounter, extraneous cognitive load—such as poorly structured product catalogs, confusing checkout processes, or ambiguous instructions—generates frustration and negative affect. By reducing extraneous physical and mental demands, high-convenience service designs preserve the consumer's cognitive resources, fostering positive affective evaluations and lowering psychological resistance to behavioral commitment.

Equity Theory and Social Exchange

Finally, the scale is conceptualized within Equity Theory (Adams, 1965). According to equity theory, individuals evaluate their relationships with organizations by calculating the ratio of their personal inputs to perceived outcomes relative to reference norms. If a consumer expends substantial monetary, temporal, physical, and emotional resources (inputs) only to receive an ordinary service outcome, an unfairness imbalance occurs, resulting in dissatisfaction. By sharply reducing the input side of the equity equation (minimizing time and effort), superior service convenience elevates the consumer's perceived value-for-effort ratio, strengthening long-term commitment and relational attachment.

7. Validity

The psychometric integrity of the SERVCON scale was established through a rigorous, multi-sample, multi-method empirical validation program conducted by Seiders et al. (2007), supplemented by independent cross-cultural and sector-specific replications across global contexts.

Content and Face Validity

Initial content validation began with an inductive-deductive item generation procedure deriving 64 candidate items from comprehensive literature reviews and depth interviews with service consumers. A panel of academic experts in services marketing and psychometrics conducted an iterative item-sorting task, assigning candidate statements to definitions of the five conceptual convenience dimensions or an “other/unclear” category. Items that failed to achieve an 80% agreement threshold were eliminated, ensuring robust substantive validity and leaving an optimized candidate pool for psychometric screening.

Convergent Validity

Convergent validity was substantiated through confirmatory factor analysis (CFA) across diverse consumer samples, including retail banking clients, discount store shoppers, and specialty service patrons. Across validation samples:

• All standardized factor loadings of the 17 items on their hypothesized latent dimensions were large, positive, and statistically significant (all loadings $lambda ge .74$, $p < .001$, with the majority exceeding .80).
• The Average Variance Extracted (AVE) for each of the five constructs systematically exceeded the rigorous .50 benchmark recommended by Fornell and Larcker (1981). Specifically, AVE values ranged from .58 to .76 across the subscales, indicating that the latent constructs account for more variance in their indicators than measurement error does.

Discriminant Validity

Discriminant validity was established via multiple psychometric tests:

• Fornell-Larcker Criterion: For each pair of latent constructs, the square root of the AVE of each dimension was strictly greater than the shared inter-construct correlation coefficient ($r$). While the five convenience dimensions correlate positively with one another (inter-factor correlations typically range between .35 and .68), they do not collapse into a single factor.
• Chi-square Difference Testing: A series of nested confirmatory factor models systematically compared unconstrained correlated two-factor models against constrained models with inter-factor correlations fixed at unity ($r = 1.0$). In all pairwise comparisons, the unconstrained models demonstrated statistically superior fit ($\Delta \chi^2(1) > 38.4, p < .001$), demonstrating that all five dimensions capture distinct, non-redundant facets of the consumer experience.
• Heterotrait-Monotrait (HTMT) Ratio: Subsequent replications employing contemporary PLS-SEM guidelines have confirmed that all HTMT ratios remain below the conservative .85 threshold, ruling out multicollinearity among the subscales.

Criterion-Related, Predictive, and Nomological Validity

Seiders et al. (2007) and subsequent empirical investigations (e.g., Kaura et al., 2015; Benoit et al., 2017) demonstrated nomological and predictive validity by modeling SERVCON within established nomological networks:

• Antecedents: Time pressure, competitive provider alternatives, and prior service experience significantly predict consumer evaluations of service convenience. Consumers facing severe temporal constraints place significantly greater weight on Access and Transaction convenience.
• Consequences: Structural equation modeling confirms that the five convenience dimensions directly and indirectly drive overall customer satisfaction, perceived overall value, affective commitment, and behavioral intentions (repeat repurchase, willingness to recommend, and customer wallet-share allocation).
• Moderating Variations: Nomological validity is further corroborated by structural variations across service typologies. In utilitarian and low-contact services (such as retail banking or parcel delivery), Transaction and Access convenience exert the strongest path coefficients toward customer retention. In hedonic and high-contact environments (such as luxury hospitality or wellness clubs), Benefit and Post-benefit convenience emerge as primary drivers of customer brand attachment.

8. Reliability

The internal consistency and measurement stability of the SERVCON scale have been confirmed across numerous studies, showing high reliability across diverse demographic groups and service industries.

Internal Consistency Statistics

In the original scale validation studies by Seiders et al. (2007), the five subscales demonstrated internal consistency values that exceeded conventional psychometric standards (Cronbach's $\alpha > .70$; Composite Reliability $CR > .70$):

• Decision Convenience (3 items): Cronbach's $\alpha = .86$ to $.89$; Composite Reliability ($CR$) $= .88$;
• Access Convenience (4 items): Cronbach's $\alpha = .84$ to $.88$; Composite Reliability ($CR$) $= .87$;
• Transaction Convenience (3 items): Cronbach's $\alpha = .85$ to $.91$; Composite Reliability ($CR$) $= .89$;
• Benefit Convenience (4 items): Cronbach's $\alpha = .88$ to $.92$; Composite Reliability ($CR$) $= .91$;
• Post-benefit Convenience (3 items): Cronbach's $\alpha = .87$ to $.93$; Composite Reliability ($CR$) $= .90$.

Total scale reliability, computed as an overall 17-item convenience index, regularly yields Cronbach's alpha coefficients exceeding .93. Item-total correlations for all individual items remain consistently above .60, with no individual item removal resulting in an increase in subscale reliability.

Test-Retest Stability and Cross-Sample Invariance

Subsequent longitudinal studies evaluating test-retest reliability over two-to-four-week intervals in stable service environments have reported stability coefficients ranging between $r_{tt} = .78$ and $.86$, confirming that the scale captures enduring cognitive assessments rather than transient mood fluctuations. Furthermore, multi-group confirmatory factor analyses have substantiated full metric and scalar measurement invariance across gender, age cohorts, and service delivery channels (physical storefronts versus digital/mobile channels), verifying that the underlying measurement properties remain invariant across administrative contexts.

9. Factor Analysis

The structural dimensionality of the SERVCON scale was established through sequential exploratory factor analyses (EFA) during the initial development phase, followed by rigorous confirmatory factor analyses (CFA) across independent cross-validation samples.

Exploratory Factor Analysis (EFA)

During pilot testing, the purified item pool was subjected to principal axis factoring with oblique (Promax) rotation, reflecting the theoretical expectation that dimensions of convenience correlate in natural settings. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy consistently exceeded .91, and Bartlett's Test of Sphericity was highly significant ($p < .001$). Factor extraction using the Kaiser criterion (eigenvalues $> 1.0$) and scree plot inspections confirmed a clear five-factor solution accounting for over 72% of the total variance. All retained items exhibited primary factor pattern coefficients $ge .68$ on their designated dimension, with negligible cross-loadings ($< .22$) on alternative dimensions.

Confirmatory Factor Analysis (CFA) and Model Fit

To assess construct dimensionality and structural integrity, Seiders et al. (2007) estimated multiple competing CFA models using maximum likelihood estimation in AMOS and LISREL. The theoretical first-order, correlated five-factor model demonstrated exceptional fit across all empirical validation samples. Representative goodness-of-fit indices include:

• Chi-Square / Degrees of Freedom: $\chi^2/df = 1.84$ to $2.26$ (well below the conservative $3.0$ threshold);
• Comparative Fit Index (CFI): $.96$ to $.98$ (exceeding the standard $.95$ cutoff for exemplary fit);
• Tucker-Lewis Index (TLI / NNFI): $.95$ to $.97$;
• Root Mean Square Error of Approximation (RMSEA): $.042$ to $.055$ (with 90% confidence intervals bounded between $.034$ and $.063$);
• Standardized Root Mean Square Residual (SRMR): $.032$ to $.041$.

Alternative Model Comparisons

To test whether service convenience is better conceptualized as a unidimensional construct or a higher-order construct, researchers compared the five-factor correlated model against competing specifications:

1. Unidimensional Model: A single-factor model forcing all 17 items onto a lone latent convenience factor exhibited unacceptable fit ($\chi^2/df > 8.5$, $\text{CFI} < .72$, $\text{RMSEA} > .135$), confirming that convenience cannot be reduced to a single global attribute.
2. Orthogonal Five-Factor Model: Constraining inter-factor correlations to zero yielded poor model fit, verifying that the dimensions share variance.
3. Second-Order Factor Model: A hierarchical model with the five first-order factors loading onto a single higher-order “Overall Service Convenience” factor yielded acceptable fit ($ ext{CFI} = .95$,$ ext{RMSEA} = .058$), though marginally inferior to the correlated first-order model. The target coefficient ($T = .92$) indicates that the second-order construct accounts for a substantial proportion of the variation among the first-order factors, providing statistical justification for researchers who wish to evaluate both dimensional profiles and global convenience indices.

10. Instrument / Measurement Tool

The SERVCON instrument is a structured, standardized survey protocol administered to adult consumers. Below are the structural and administration specifications:

• Instrument Designation: Service Convenience Scale (SERVCON)
• Author Origin: Kathleen Seiders, Glenn B. Voss, Dhruv Grewal, and Andrea L. Godfrey (2007)
• Measurement Type: Self-report multi-item psychometric rating scale
• Target Population: Consumers, service patrons, and users of commercial, institutional, or digital services
• Total Item Inventory: 17 reflective declarative statements
• Dimensional Architecture: Five distinct first-order subscales:
  • Decision Convenience: 3 items
  • Access Convenience: 4 items
  • Transaction Convenience: 3 items
  • Benefit Convenience: 4 items
  • Post-benefit Convenience: 3 items
• Response Scale: 7-point Likert response continuum:
  • 1 = Strongly Disagree
  • 2 = Disagree
  • 3 = Somewhat Disagree
  • 4 = Neither Agree nor Disagree (Neutral)
  • 5 = Somewhat Agree
  • 6 = Agree
  • 7 = Strongly Agree
• Scoring Protocol:
  • All items are positively keyed (no reverse scoring is necessary).
  • Subscale Scores: Computed by calculating the arithmetic mean of the items comprising each subscale. Scores range from 1.0 to 7.0, where higher values indicate greater perceived convenience (minimized time and effort).
  • Composite Overall Index: May be calculated as the grand mean across all 17 items (or via a higher-order latent construct score in structural equation modeling) when a summary measure of general service convenience is required.
  • Profile Interpretation: Analysts are encouraged to examine subscale profiles independently to identify operational bottlenecks across specific stages of the service delivery pipeline.
• Administration Time: Approximately 4 to 6 minutes for full completion.

11. Permissions & Fee and Test Year

The Service Convenience Scale was finalized and formally published in 2007 by the Academy of Marketing Science in the Journal of the Academy of Marketing Science (Springer Nature). The theoretical foundations were established in 2002 in the Journal of Marketing.

• Academic Research Use: The scale items are published in scholarly journals for academic research, non-commercial scientific inquiry, theses, and university teaching. Academic researchers may use and adapt the scale without paying licensing fees, provided that appropriate scholarly attribution is made to Seiders et al. (2007) and Berry et al. (2002).
• Commercial and Proprietary Application: Commercial enterprises, management consulting firms, market research agencies, and proprietary technology developers intending to embed SERVCON into commercial software, SaaS platforms, or for-profit consumer monitoring audits should consult the publishers (Springer Nature / Academy of Marketing Science) and the copyright-holding authors to determine whether formal reproduction permissions or commercial licensing agreements are required.
• Contextual Adaptation: The authors encourage researchers to replace bracketed referents (e.g., “[service provider]” or “[firm]”) with the specific focal brand, institution, store, or service setting being investigated.

12. References

Adams, J. S. (1965). Inequity in social exchange. In L. Berkowitz (Ed.), Advances in Experimental Social Psychology (Vol. 2, pp. 267–299). Academic Press. https://doi.org/10.1016/S0065-2601(08)60108-2

Becker, G. S. (1965). A theory of the allocation of time. The Economic Journal, 75(299), 493–517. https://doi.org/10.2307/2228949

Benoit, S., Klose, S., & Ettinger, A. (2017). Linking service convenience to satisfaction: Dimensions and moderators. Journal of Services Marketing, 31(6), 527–538. https://doi.org/10.1108/JSM-10-2016-0353

Berry, L. L., Seiders, K., & Grewal, D. (2002). Understanding service convenience. Journal of Marketing, 66(3), 1–17. https://doi.org/10.1509/jmkg.66.3.1.18505

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

Kaura, V., Prasad, C. S. D., & Sharma, S. (2015). Service quality, service convenience, price and fairness, customer loyalty, and the mediating role of customer satisfaction. International Journal of Bank Marketing, 33(4), 404–422. https://doi.org/10.1108/IJBM-04-2014-0048

Seiders, K., Voss, G. B., Grewal, D., & Godfrey, A. L. (2007). SERVCON: Development and validation of a multidimensional service convenience scale. Journal of the Academy of Marketing Science, 35(1), 144–156. https://doi.org/10.1007/s11747-006-0001-5

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4

Zipf, G. K. (1949). Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology. Addison-Wesley.

13. Items of the Scale