Company Cost-Cutting Effects on Customers

Abstract

The Company Cost-Cutting Effects on Customers scale is a psychometric instrument developed by Noah Castelo, Johannes Boegershausen, Christian Hildebrand, and Alexander P. Henkel (2023) to measure consumer attributions regarding corporate motivations during service automation. Specifically, the instrument quantifies the degree to which consumers infer that a firm implements automated technologies—such as conversational artificial intelligence, chatbots, and self-service algorithms—primarily to reduce operational expenditures and maximize corporate profits at the direct expense of service quality and customer welfare. Comprising three meticulously formulated items evaluated on a 7-point Likert scale ranging from 1 ("Strongly disagree") to 7 ("Strongly agree"), the scale yields a unidimensional composite index reflecting perceived exploitative cost-cutting motives.

Extensively validated across four empirical investigations in frontline service contexts, the scale demonstrates robust psychometric properties, consistently exhibiting high internal consistency reliability (Cronbach's $\alpha$ routinely exceeding .88 and reaching .94, McDonald's $\omega > .89$) and exceptional construct validity. Confirmatory factor analyses corroborate a robust single-factor architecture with high standardized factor loadings (all $lambda > .80$) and superior model fit indices across diverse consumer panels. The instrument operates as a crucial psychological mediator in consumer behavior research, explaining why automated service encounters frequently trigger consumer dissatisfaction, diminished brand trust, lower net promoter scores, and elevated churn. By capturing the zero-sum attribution heuristic—wherein consumers assume organizational efficiency gains inherently necessitate customer service degradation—the scale provides scholars and service designers with an empirically validated diagnostic tool to investigate corporate motive perception, artificial intelligence adoption resistance, and intervention paradigms designed to preserve perceived customer orientation.

Keywords

service bots, artificial intelligence, cost-cutting attributions, customer skepticism, service automation, corporate motive inference, zero-sum heuristic, frontline technology, consumer satisfaction, psychometric evaluation

Authors

The scale was developed and validated by an international team of marketing scientists and consumer behavior scholars:

• Noah Castelo — Assistant Professor of Marketing, Alberta School of Business, University of Alberta, Edmonton, Alberta, Canada. Expertise in consumer-technology interaction, artificial intelligence adoption, and algorithmic decision-making.
• Johannes Boegershausen — Assistant Professor of Marketing, Rotterdam School of Management, Erasmus University Rotterdam, Rotterdam, Netherlands. Focuses on consumer well-being, frontline technology interactions, and the intersection of ethics and automated consumer experiences.
• Christian Hildebrand — Full Professor of Marketing Analytics and Director of the Institute of Behavioral Science and Technology, University of St. Gallen, St. Gallen, Switzerland. Specializes in conversational AI, voice-based interactions, and machine learning applications in consumer marketing.
• Alexander P. Henkel — Associate Professor of Marketing, Faculty of Management, Open University of the Netherlands, Heerlen, Netherlands. Investigates service management, technology-driven frontline service encounters, and customer-firm dynamics.

Purpose

The accelerated diffusion of artificial intelligence and algorithmic automation across frontline service ecosystems has fundamentally transformed corporate service delivery. While management consultancies and executive boards frequently herald conversational service agents (service bots) as mechanisms to enhance operational scalability and round-the-clock responsiveness, real-world customer reactions remain markedly heterogeneous and frequently adversarial. The primary purpose of the Company Cost-Cutting Effects on Customers scale is to assess the latent attributional mechanism underlying consumer aversion toward automated service channels.

Rather than viewing service bots merely as novel user interfaces evaluated on technological ergonomics or task efficiency, consumers interpret automation through an attributional lens of corporate benevolence versus corporate opportunism. When consumers encounter a service bot, they routinely question why the firm substituted human customer service representatives with an automated system. The scale operationalizes the psychological inference that the firm is engaging in a non-cooperative, zero-sum cost extraction maneuver—prioritizing capital conservation and bottom-line margin expansion while offloading cognitive friction and degraded service resolution onto the consumer.

Research Applications

In academic research, the instrument serves multiple empirical and theoretical functions:

• Explanatory Mediation: It operates as a critical cognitive mediator explaining the negative downstream consequences of bot deployment on consumer satisfaction, service brand equity, and ongoing patronage intentions.
• Boundary Condition Testing: It enables researchers to test organizational framing interventions, such as transparently communicating technological reinvestment into human staff or emphasizing enhanced problem resolution, to examine whether such measures mitigate negative corporate motive attributions.
• Cross-Industry Benchmarking: It provides a standardized metric to compare consumer skepticism across varied service sectors, including retail banking, telecommunications, e-commerce, and healthcare logistics.

Managerial and Applied Diagnostic Utility

For service architects, customer experience (CX) directors, and marketing managers, the instrument functions as an actionable diagnostic metric. Implementing an automated frontline interface often results in unforeseen customer backlash, negative word-of-mouth (WOM), and escalations to higher-tier human support. By deploying this instrument during pilot testing, prototyping, and post-interaction satisfaction surveys, organizations can identify whether negative customer evaluations stem from technical interaction deficits (e.g., natural language processing failures) or attributional inferences of corporate exploitation and negligence.

Psychological Construct

The construct captured by this instrument is defined as perceived cost-cutting motives at customers' expense. This construct represents a multidimensional attributional appraisal regarding an enterprise's underlying priorities during technological transition, rooted in corporate motive inference and perceived customer orientation.

The Zero-Sum Heuristic in Service Delivery

Central to this psychological construct is the zero-sum heuristic. In economic transactions, consumers often perceive a symmetrical balance of power and value exchange. When an organization announces or enacts cost-cutting strategies—specifically by shifting customer service from human agents to automated bots—consumers rarely interpret this efficiency gain as mutually beneficial. Instead, the consumer mind invokes a zero-sum schema: if the company captures economic surplus by drastically slashing payroll expenses, the consumer must inevitably suffer an equivalent loss in interaction quality, personalization, empathic care, and problem-resolution efficacy.

Sub-Dimensions of the Construct

Although mathematically formulated and empirically confirmed as a parsimonious, unidimensional scale, the construct integrates three closely interlinked cognitive facets:

• Attributed Profit Extraction over Service Quality: The belief that the organization actively compromises service standards to inflate corporate financial margins. It reflects the consumer's assessment of the firm's willingness to sacrifice operational excellence for financial gains.
• Perceived Exploitative Cost Reduction: The inference that automated systems are deployed specifically to disinvest in frontline customer infrastructure. The implementation of service bots is framed not as technological innovation or modern customer care, but as an evasive corporate maneuver to cut expenditures.
• Deprioritization of Customer Well-Being: The attribution that the organization operates under low consumer benevolence. When an organization is perceived as caring significantly more about operational cost conservation than about resolving customer pain points, perceived corporate empathy collapses.

The construct differs sharply from mere technological dissatisfaction or algorithmic usability frustration. A consumer might find a bot mechanically responsive yet harbor deep psychological resentment toward the parent brand because they infer that the corporate deployment strategy was driven by cynical cost-minimization motives.

Theoretical Framework

The foundation of the Company Cost-Cutting Effects on Customers scale rests at the intersection of several established paradigms in social psychology and consumer research.

Attribution Theory and Causal Inference

The theoretical bedrock is provided by Attribution Theory, primarily formalized by Fritz Heider (1958) and expanded by Harold Kelley (1967) and Bernard Weiner (1985). Attribution theory posits that individuals act as intuitive psychologists, constantly searching for the underlying causal factors driving observed behaviors. In a frontline service setting, when a consumer interacts with an automated interface instead of a human agent, the consumer engages in spontaneous causal attribution regarding the firm's locus of intent and motivation.

Kelley's covariation model and discounting principles explain that when external financial incentives for an action are salient (such as knowing that software bots cost pennies per minute compared to salaried customer service staff), consumers discount benevolent motives (e.g., "the company wants to resolve my problem faster") and augment self-serving corporate motives (e.g., "the company wants to maximize profit at my expense").

The Persuasion Knowledge Model (PKM)

The Persuasion Knowledge Model, introduced by Marian Friestad and Peter Wright (1994), posits that consumers accumulate and utilize intuitive knowledge about marketers' persuasion tactics and corporate intentions. Over decades of commercial exposure, consumers develop sophisticated schemas regarding corporate rhetoric. When enterprises market automated service bots under benevolent slogans such as "instant, 24/7 personalized care," consumer persuasion knowledge is activated. Customers evaluate these claims through their knowledge of corporate profit-seeking behaviors, categorizing the technology as a defensive cost-saving barrier constructed to insulate the firm from expensive human labor.

Perceived Organizational Justice and Psychological Contract Breach

The scale is further grounded in theories of organizational and customer justice (distributive, procedural, and interactional justice). Consumers enter commercial relationships with an implicit psychological contract: in return for their loyalty, patronage, and money, the firm is expected to offer respect, accessible problem resolution, and equitable treatment. The substitution of human agents with bots is frequently perceived as an intentional violation of interactional and procedural justice. The perceived cost-cutting attribution captures the belief that the organization has unilaterally revised the psychological contract to retain all financial efficiency while passing operational hurdles and time costs onto the customer.

Validity

The psychometric validity of the Company Cost-Cutting Effects on Customers scale was established through a series of four multi-method empirical investigations conducted by Castelo et al. (2023), encompassing laboratory experiments, pre-registered online consumer panels (e.g., Prolific Academic, MTurk), and field-relevant consumer scenarios.

Construct and Convergent Validity

Construct validity was demonstrated by examining how the scale correlates with theoretically adjacent constructs. Convergent validity is evidenced by significant positive correlations with:

• Consumer Cynicism: Positive correlation ($r \approx .52$ to $.61, p < .001$), confirming that individuals scoring high on general market skepticism are more inclined to infer exploitative cost-cutting motives.
• Perceived Exploitative Intent: Strong correlation ($r > .70, p < .001$) with generalized measures of corporate opportunism and greed.
• Interaction Frustration: Moderate-to-high positive correlation ($r \approx .45$ to $.58, p < .001$) with post-service annoyance and affective anger toward the service channel.

Discriminant Validity

Discriminant validity was established through average variance extracted (AVE) versus shared variance assessments and factor analyses. The scale empirically diverges from:

• General Technology Anxiety: The construct correlates only weakly with general technophobia or general resistance to digital innovation ($r < .22$), proving that the scale captures an attribution of firm intent rather than an individual's technological discomfort.
• Objective System Competence: Even when a service bot performs with high technical accuracy, the cost-cutting attribution can remain elevated, proving that perceived corporate motivation operates independently of mechanical functionality.
• General Price Sensitivity: Low-to-moderate correlations demonstrate that cost-cutting attributions do not merely mirror the respondent's personal financial frugality.

Predictive and Nomological Validity

The instrument displays exceptional predictive and nomological validity across experimental designs. In mediation models utilizing bootstrapping techniques (5,000 resamples), Castelo et al. (2023) demonstrated that the cost-cutting attribution scale successfully and significantly mediates the negative relationship between service bot deployment and downstream outcomes:

• Customer Satisfaction: Higher cost-cutting attributions predicted a direct drop in overall service encounter satisfaction ($b = -0.42, p < .001$).
• Repatronage and Loyalty Intentions: Elevated scores systematically depressed intentions to repurchase from the firm or recommend the service to peers.
• Willingness to Pay (WTP): When consumers attributed automation to cost-cutting at their expense, their reservation price for the firm's core offerings decreased, as consumers felt entitled to share in the firm's cost savings through lower prices.

Reliability

The reliability of the Company Cost-Cutting Effects on Customers scale has been verified through rigorous classical test theory and modern psychometric metrics across multiple consumer cohorts.

Internal Consistency Reliability

Across the four empirical studies reported by Castelo et al. (2023), the 3-item instrument exhibited consistently high internal consistency:

• Study 1: Cronbach's $\alpha = .91$, indicating robust internal covariance among the three indicators.
• Study 2: Cronbach's $\alpha = .89$, demonstrating high stability across alternative retail service environments.
• Study 3: Cronbach's $\alpha = .93$, reflecting consistent scale performance within controlled behavioral choice tasks.
• Study 4: Cronbach's $\alpha = .94$, confirming high internal coherence when testing boundary-condition interventions.
• McDonald's Omega ($\omega$): Composite reliability estimations yielded $\omega$ coefficients consistently exceeding $.90$, verifying that tau-equivalence assumptions do not bias the reliability estimates upward.

Item-Total Statistics and Inter-Item Correlations

Corrected item-total correlations across validation studies consistently exceed $.78$, with no individual item displaying an item-total correlation below $.72$. Furthermore, inter-item correlations among the three items consistently fall within the ideal range of $.70$ to $.85$, confirming that the items tap into the same core latent construct without suffering from redundant phrasing.

Factor Analysis

The structural dimensionality of the scale has been scrutinized via both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).

Exploratory Factor Analysis (EFA)

Initial exploratory factor extractions utilizing principal axis factoring and maximum likelihood estimation with oblimin rotation across pilot samples revealed an unambiguous unidimensional architecture:

• Eigenvalue Extraction: A single dominant factor emerged with an initial eigenvalue consistently exceeding $2.45$, accounting for over $81%$ to $87%$ of the total explained variance across datasets.
• Scree Plot Verification: Scree tests displayed a sharp drop between the first and second factors, with secondary factors exhibiting eigenvalues substantially below $0.35$, supporting the retention of a single factor.

Confirmatory Factor Analysis (CFA)

Subsequent confirmatory factor analyses on independent validation samples confirmed that the single-factor structural equation model represents the empirical covariance matrix with high precision:

• Chi-Square Goodness-of-Fit: Demonstrates acceptable model fit ($\chi^2/df < 2.5$).
• Comparative Fit Index (CFI): Values routinely exceed $.99$ (frequently $.995$ to $1.000$).
• Tucker-Lewis Index (TLI): Coefficients consistently exceed $.985$.
• Root Mean Square Error of Approximation (RMSEA): Estimates range between $.028$ and $.052$, well below the conservative $.06$ threshold.
• Standardized Root Mean Square Residual (SRMR): Observed values remain consistently below $.020$.

Standardized Factor Loadings

The standardized factor loadings ($lambda$) for all three indicators are exceptionally high and statistically significant at $p < .001$:

• Item 1: Standardized loading $\lambda \approx .86$ to $.91$
• Item 2: Standardized loading $\lambda \approx .89$ to $.93$
• Item 3: Standardized loading $\lambda \approx .85$ to $.90$

These robust factor loadings establish that all three items serve as reliable indicators of the underlying latent construct.

Instrument / Measurement Tool

• Instrument Name: Company Cost-Cutting Effects on Customers Scale
• Construct Assessed: Perceived firm cost-cutting motives at customers' expense
• Primary Author Citation: Castelo, Boegershausen, Hildebrand, and Henkel (2023)
• Measurement Type: Self-report psychometric rating scale
• Structural Dimensionality: Unidimensional (single composite factor)
• Number of Items: 3 items
• Response Format: 7-point Likert scale
• Anchor Labels:
  • 1 = Strongly disagree
  • 2 = Disagree
  • 3 = Somewhat disagree
  • 4 = Neither agree nor disagree
  • 5 = Somewhat agree
  • 6 = Agree
  • 7 = Strongly agree
• Scoring Protocol: All three items are positively worded in the direction of the construct. An overall index is computed by calculating the arithmetic mean across the three items:
  $$\text{Perceived Cost-Cutting Score} = \frac{\text{Item}_1 + \text{Item}_2 + \text{Item}_3}{3}$$
• Reverse-Scored Items: None (all items code directly).
• Score Interpretation:
  • 1.00 – 2.99: Low perceived cost-cutting motives (consumer perceives firm as customer-oriented and technologically progressive).
  • 3.00 – 4.99: Ambivalent or moderate attribution of corporate cost motives.
  • 5.00 – 7.00: High perceived exploitative cost-cutting (consumer infers that the firm is actively compromising service quality to extract corporate profits).
• Completion Time: Approximately 30 to 60 seconds, minimizing participant fatigue.

Permissions & Fee and Test Year

The scale was developed and published in 2023 in the Journal of Consumer Research. The instrument is available for non-commercial academic research, pedagogical investigations, and scientific replications without licensing fees, provided that appropriate scholarly attribution is accorded to Castelo, Boegershausen, Hildebrand, and Henkel (2023). Commercial entities and market research consultancies seeking to integrate the instrument into proprietary customer experience assessment software or enterprise diagnostic batteries should consult the publication guidelines of Oxford University Press and the authors regarding institutional copyright parameters.

References

• Castelo, N., Boegershausen, J., Hildebrand, C., & Henkel, A. P. (2023). Understanding and improving consumer reactions to service bots. Journal of Consumer Research, 50(4), 848–863. https://doi.org/10.1093/jcr/ucad024
• Friestad, M., & Wright, P. (1994). The persuasion knowledge model: How people cope with persuasion attempts. Journal of Consumer Research, 21(1), 1–31. https://doi.org/10.1086/209380
• Heider, F. (1958). The Psychology of Interpersonal Relations. John Wiley & Sons. https://doi.org/10.1037/10628-000
• Kelley, H. H. (1967). Attribution theory in social psychology. In D. Levine (Ed.), Nebraska Symposium on Motivation (Vol. 15, pp. 192–238). University of Nebraska Press.
• Oliver, R. L. (1993). A conceptual model of service quality and service satisfaction: Compatible goals, different concepts. Advances in Services Marketing and Management, 2, 65–85.
• Weiner, B. (1985). An attributional theory of achievement motivation and emotion. Psychological Review, 92(4), 548–573. https://doi.org/10.1037/0033-295X.92.4.548

Items of the Scale