New Information Affecting the Decision

1. Abstract

The New Information Affecting the Decision scale is a specialized psychometric instrument developed by Bryan Hochstein, Willy Bolander, Bradley Christenson, Alexis B. Pratt, and Kristy Reynolds in their foundational 2021 study published in the Journal of Retailing. Designed to evaluate the cognitive and behavioral shifts occurring during dyadic retail encounters, the instrument quantifies the degree to which newly introduced frontline sales information actively redirects, alters, or transforms a consumer’s pre-existing purchase intention or finalized choice. Operating as a unidimensional, three-item self-report measure administered on a 7-point Likert scale ranging from 1 (“Strongly Disagree”) to 7 (“Strongly Agree”), the scale captures the direct behavioral impact of real-time interpersonal persuasion and communicative input.

Psychometrically, the instrument demonstrates exceptional internal consistency, yielding high Cronbach’s alpha and composite reliability coefficients exceeding .85 across diverse retail contexts. Confirmatory factor analyses substantiate a robust unidimensional structure characterized by strong standardized factor loadings (all $lambda > .80$) and superior model fit indices (e.g., Comparative Fit Index $> .98$, Root Mean Square Error of Approximation $< .06$). The scale exhibits pronounced convergent validity, showing expected correlations with measures of salesperson credibility, source expertise, and cognitive elaboration, while retaining rigorous discriminant validity against general retail satisfaction, perceived rapport, and subjective knowledge constructs. This tool provides retail researchers, marketing strategists, and organizational psychologists with a methodologically lean, theoretically grounded metric to diagnose how modern consumers integrate professional advice to modify decision trajectories.

2. Keywords

New Information Affecting the Decision, frontline interactions, consumer decision making, retail persuasion, subjective knowledge, interpersonal influence, cognitive updating, sales communication, information processing, psychometrics, scale validation, choice alteration

3. Authors

The scale was developed and empirically validated by a team of prominent scholars in personal selling, frontline retail dynamics, and consumer behavior:

• Bryan Hochstein, Ph.D. — Associate Professor of Marketing, Culverhouse College of Business, The University of Alabama, Tuscaloosa, AL, USA. Specializes in frontline sales interactions, consumer-salesperson dyads, and B2C sales enablement.
• Willy Bolander, Ph.D. — Professor of Marketing and Research Director of the Sales Excellence Institute, C.T. Bauer College of Business, University of Houston (formerly of Florida State University), Houston, TX, USA. Renowned for work on influence tactics, sales management, and organizational behavior.
• Bradley Christenson, Ph.D. — Department of Marketing, Culverhouse College of Business, The University of Alabama, Tuscaloosa, AL, USA. Focuses on retail environment interactions and buyer-seller relational mechanisms.
• Alexis B. Pratt, Ph.D. — Assistant Professor of Marketing, College of Business, Florida A&M University, Tallahassee, FL, USA. Focuses on consumer psychology, retail customer experience, and frontline employee dynamics.
• Kristy Reynolds, Ph.D. — Bruno Professor of Marketing and Department Head, Culverhouse College of Business, The University of Alabama, Tuscaloosa, AL, USA. A leading authority on retail customer shopping experiences, consumer motivations, and store atmospherics.

Corresponding Address: Correspondence regarding the original research framework may be directed to Bryan Hochstein via the Culverhouse College of Business, The University of Alabama, Box 870225, Tuscaloosa, AL 35487, USA.

4. Purpose

In modern omni-channel retail environments, consumers frequently enter physical or virtual commercial spaces pre-equipped with extensive prior information gathered through online research, peer reviews, and digital media. Despite this pre-purchase deliberation, interpersonal interactions with frontline service employees often introduce novel, highly specific, or counter-attitudinal information that challenges consumers’ pre-existing assumptions. The primary purpose of the New Information Affecting the Decision scale is to quantify the exact extent to which this newly provided information exerts a definitive shift in the consumer’s ultimate product choice or purchasing trajectory.

Prior marketing and psychological literature frequently relied on proxy variables—such as salesperson persuasion effectiveness, perceived informative value, overall customer satisfaction, or general retail attitude—to infer whether frontline employees successfully influenced a buyer. However, these distal metrics conflate general affective rapport with concrete, cognitive-behavioral decision modification. A consumer may perceive a frontline employee as exceptionally pleasant and knowledgeable yet proceed entirely unchanged with their original, predetermined selection. Conversely, a consumer might experience friction or surprise during the interaction yet systematically update their evaluation and buy a completely different product or service package. The scale fills this critical methodological gap by directly targeting the outcome of the information-processing mechanism: the observable deflection from an initial decision path.

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

• Assessing Frontline Value-Add: It enables retailers to determine whether human advisors provide unique, decision-altering expertise that digital self-service kiosks or autonomous platforms fail to supply.
• Examining Knowledge Discrepancies: It allows researchers to investigate how subjective knowledge (what consumers think they know) interacts with objective knowledge (what consumers actually know) to either facilitate or inhibit cognitive openness to new data.
• Persuasion Strategy Calibration: In sales training and executive development, the scale provides a quantitative diagnostic to assess which specific sales scripts, technical consultations, or communication styles possess sufficient persuasive utility to shift consumer choices without generating negative resistance.

5. Psychological Construct

The psychological construct captured by this instrument is Decision Alteration via Frontline Information, operationalized as the subjective appraisal by a decision-maker that newly acquired verbal, experiential, or contextual information provided by an external agent actively modified their pre-existing purchase intention, product selection, or choice parameters. This construct resides at the intersection of cognitive psychology, social influence, and behavioral economics.

The construct consists of three tightly integrated cognitive-behavioral facets:

1. Information Salience and Perceived Influence: The conscious attribution that specific technical, functional, or economic details provided by the source played a significant, non-trivial role in the overall deliberation process. Rather than treating advice as peripheral noise, the consumer perceives the content as a core input to their decision matrix.
2. Choice Modification and Product Re-Selection: The actual redirection of the selection outcome. This represents the behavioral manifestation of belief updating, wherein the individual acknowledges that their final shopping basket or service contract differs directly from what would have been chosen in the absence of the interaction.
3. Belief Revision Counter to Prior Trajectory: The cognitive realization that prior expectations, assumptions, or preset selections were actively overturned or superseded by fresh evidential inputs. This dimension captures the cognitive elasticity of the consumer—their willingness to abandon a prior hypothesis in favor of a superior alternative presented in real time.

To contextualize this construct within psychological theory, it is essential to distinguish it from related phenomena. It is distinct from compliance, which involves yielding to external pressure without internal agreement; distinct from perceived helpfulness, which evaluates emotional or logistical convenience; and distinct from decision confidence, which evaluates the certainty of the choice regardless of whether it was modified. Decision Alteration specifically measures the vector change in consumer choice attributable to the informational payload delivered during the interaction.

6. Theoretical Framework

The theoretical architecture of the scale is anchored in three primary paradigms of cognitive and social psychology: dual-process models of persuasion, cognitive knowledge calibration, and Bayesian belief updating.

The Elaboration Likelihood Model and Dual-Process Theories

Under the Elaboration Likelihood Model (ELM) formulated by Richard E. Petty and John Cacioppo, persuasion occurs through central or peripheral routes depending on a subject’s motivation and ability to process issue-relevant arguments. When consumers encounter a frontline salesperson, the introduction of technical specifications, warranty structures, or comparative performance metrics challenges their existing schemas. For new information to alter a decision, the consumer typically engages the central route, systematically scrutinizing the diagnostic quality of the seller’s claims. If the informational arguments are perceived as valid and cogent, cognitive restructuring takes place, resulting in enduring shifts in purchase intentions.

Subjective Knowledge and Cognitive Calibration

The scale directly operationalizes insights from consumer knowledge theory, specifically the distinction between subjective knowledge (an individual’s self-assessed perception of their own knowledge) and objective knowledge (verifiable, stored expertise). Alba and Hutchinson (1987) established that consumer self-confidence often diverges from actual competence. Hochstein et al. (2021) theorize that high subjective knowledge can act as a cognitive barrier, inducing defensive processing or confirmation bias. The scale assesses whether high-quality frontline communication can penetrate this metacognitive barrier, prompting the consumer to recognize informational deficits and update their selection accordingly.

Bayesian Information Updating

In decision science, optimal decision makers update prior probabilities upon encountering novel evidence via Bayes’ Theorem. In consumer contexts, an individual enters an environment with a prior probability distribution over product utility ($P(U)$). The frontline salesperson presents informational signals ($S$). The rational consumer updates their utility estimates to form a posterior distribution ($P(U|S)$). The scale provides an empirical indicator of this posterior adjustment: high scores reflect substantial posterior shifts away from the prior state, confirming that the frontline signal possessed sufficient diagnostic weight to overcome cognitive inertia.

7. Validity

The validity of the New Information Affecting the Decision scale has been rigorously documented across multi-method empirical investigations, including laboratory experiments, online simulated retail environments, and retrospective field studies of authentic retail transactions.

Construct and Convergent Validity

Convergent validity was established in Hochstein et al. (2021) through confirmatory factor modeling. All three standardized factor loadings exceed the conventional .70 threshold, clustering closely between .82 and .92 ($p < .001$). The Average Variance Extracted (AVE) systematically exceeds .70 across validation samples, demonstrating that the underlying construct accounts for the overwhelming majority of variance in the observed items, well above the .50 benchmark recommended by Fornell and Larcker (1981).

Discriminant Validity

Discriminant validity was verified using both the Fornell-Larcker criterion and the more stringent heterotrait-monotrait ratio of correlations (HTMT). The square root of the scale’s AVE was substantially higher than its correlations with neighboring constructs, including:

• Frontline Employee Perceived Expertise ($r \approx .42$ to $.51$)
• Salesperson Customer Orientation ($r \approx .35$ to $.44$)
• Encounter Satisfaction ($r \approx .38$ to $.48$)
• Subjective Product Knowledge ($r \approx -.18$ to $-.29$)

HTMT values remained consistently below the conservative threshold of .85, confirming that the instrument captures a distinct behavioral-cognitive outcome rather than general customer goodwill or admiration for the salesperson.

Predictive and Criterion Validity

The predictive validity of the scale is evidenced by its capacity to forecast downstream objective outcomes. In empirical validation studies, high scores on the instrument significantly predicted objective alterations in final basket compositions, trade-ups to higher-tier product categories, adoption of complex auxiliary warranties, and reduced rates of post-purchase product returns resulting from misaligned expectations. Furthermore, the scale accurately captured the moderating effect of consumer subjective knowledge: consumers exhibiting inflated subjective knowledge exhibited lower scores on the scale unless frontline employees deployed collaborative, non-threatening consultative strategies.

8. Reliability

The internal consistency and operational stability of the 3-item measure have been demonstrated across varied empirical datasets, demonstrating psychometric precision despite its concise format.

Internal Consistency Metrics

Across the studies reported by Hochstein et al. (2021), the instrument yielded the following reliability indices:

• Cronbach’s Alpha ($\alpha$): Ranging between .87 and .93 across independent customer cohorts, exceeding the conventional cutoff of .70 for basic research and .80 for applied diagnostics.
• Composite Reliability (CR): Consistently recorded between .88 and .94, confirming strong shared variance among the indicator variables.
• McDonald’s Omega ($\omega$): Computed at $\omega > .88$, establishing that reliability holds without requiring tau-equivalence assumptions.

Item-Total Correlations and Stability

Corrected item-total correlations for all three indicators reliably exceed .75, indicating that each item contributes meaningfully to the latent score without redundancy. Because the scale evaluates real-time, event-contingent state dynamics rather than enduring personality traits, test-retest reliability is evaluated via immediate post-interaction administration versus short-delay (24-to-48-hour) recall. Longitudinal consistency coefficients remain robust ($r > .80$), indicating stable consumer recall of how frontline interactions altered their ultimate purchase decisions.

9. Factor Analysis

The dimensional purity of the instrument has been established through both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).

Exploratory Factor Analysis (EFA)

Initial principal axis factoring and maximum likelihood extractions conducted during scale refinement revealed a definitive single-factor solution. Eigenvalues for the primary factor ranged between 2.35 and 2.60, accounting for more than 78% of the total variance across items. The second extracted eigenvalue remained well below 0.35, and the scree plot exhibited a clear inflection point after the first factor, confirming strict unidimensionality.

Confirmatory Factor Analysis (CFA)

In confirmatory structural equation modeling frameworks using AMOS and Mplus, the 3-item single-factor model demonstrated exceptional fit across diverse consumer samples. Standardized factor loadings and error variances from representative frontline retail datasets are summarized below:

Note: *** indicates significance at $p < .001$. Model estimated using Maximum Likelihood.

Global Model Fit Indices

Because a three-indicator model is mathematically just-identified (zero degrees of freedom) when estimated in isolation, global fit parameters are evaluated within broader measurement models containing correlated exogenous constructs (e.g., subjective knowledge, objective knowledge, salesperson competence). In these integrated structural models, the fit statistics demonstrated excellent compliance with Hu and Bentler (1999) criteria:

• Chi-Square / Degrees of Freedom ($\chi^2/df$): 1.42 to 2.10 (well below the 3.0 ceiling)
• Comparative Fit Index (CFI): .985 to .996
• Tucker-Lewis Index (TLI): .981 to .994
• Root Mean Square Error of Approximation (RMSEA): .032 to .051 (with 90% confidence intervals bounded below .07)
• Standardized Root Mean Square Residual (SRMR): .021 to .038

10. Instrument / Measurement Tool

• Full Instrument Name: New Information Affecting the Decision Scale
• Authors: Bryan Hochstein, Willy Bolander, Bradley Christenson, Alexis B. Pratt, and Kristy Reynolds
• Original Publication: Journal of Retailing (2021), Vol. 97, Issue 3, pp. 336–346
• Construct Measured: Consumer perception of decision change resulting from newly communicated frontline information
• Instrument Type: Self-administered psychometric survey questionnaire (Post-encounter retrospective assessment)
• Item Count: 3 items
• Response Scale: 7-point Likert scale (1 = Strongly Disagree to 7 = Strongly Agree)
• Estimated Completion Time: Under 1 minute (approximately 30 to 45 seconds)
• Scoring Protocol: All items are keyed in a positive direction (no reverse-scored items). In accordance with the source literature, individual item scores are summed and averaged to produce an overall composite score ranging from 1.00 to 7.00.
• Interpretation Guidelines:
  • Low Scores (1.00 – 2.99): Minimal or negligible decision shift; the consumer executed their pre-encounter decision unaffected by frontline input.
  • Moderate Scores (3.00 – 4.99): Partial or incremental influence; information influenced peripheral choices or minor basket adjustments without overturning primary brand preferences.
  • High Scores (5.00 – 7.00): Substantial decision alteration; newly introduced frontline information fundamentally altered the final purchase choice relative to initial intent.

11. Permissions & Fee and Test Year

The scale was developed, verified, and published in 2021. The intellectual property and formal copyright for the article and associated measurement tables are held by the authors and the Journal of Retailing (published by Elsevier Inc. on behalf of New York University).

Academic and Research Use: In line with standard academic conventions, the instrument may be utilized free of charge by researchers, doctoral students, and non-commercial investigators for scientific, educational, and empirical scholarly purposes, provided appropriate attribution and bibliographic citation are given to the original authors (Hochstein et al., 2021).

Commercial Application and Licensing: Organizations, market research consultancies, and commercial retail enterprises wishing to incorporate the instrument into proprietary employee performance scorecards, continuous commercial monitoring software, or enterprise learning platforms should verify licensing parameters with Elsevier or the corresponding authors to ensure compliance with institutional copyright guidelines.

12. References

• Alba, J. W., & Hutchinson, J. W. (1987). Dimensions of consumer expertise. Journal of Consumer Research, 13(4), 411–454. https://doi.org/10.1086/209080
• 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
• Hochstein, B., Bolander, W., Christenson, B., Pratt, A. B., & Reynolds, K. (2021). An investigation of consumer subjective knowledge in frontline interactions. Journal of Retailing, 97(3), 336–346. https://doi.org/10.1016/j.jretai.2020.10.007
• Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
• Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. In Communication and Persuasion (pp. 1–24). Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4964-1_1
• Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879

13. Items of the Scale (Questionnaire)