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This study aims to draw on cue utilization and irradiation theories to: determine the extent to which country-of-origin image and its sub-dimensions exert an aggregate and relative influence on consumer brand evaluations; and identify the contextual and methodological factors that account for between-study variance in the focal relationship.

A random-effects model was used to examine 166 empirical articles encompassing 499,563 observations, and 282 effect sizes from 1984 to 2020 using Comprehensive Meta-Analysis software.

Results show that country-of-origin image has a positive, moderate effect on consumer brand evaluations. Moreover, findings reveal that each dimension of country-of-origin image – general country image, general product country image, specific product country image and partitioned country image – significantly influences consumer brand evaluation, but the effect of general product country image is the largest. What’s more, the aggregate impacts of country-of-origin image on consumer brand evaluation – brand commitment, brand-specific associations and general brand impressions – show that the effect on brand commitment is the largest. Finally, findings show that contextual factors (brand source, product sector, culture [individualism vs collectivism], brand origin continents and respondents’ continent) and methodological factors (cues, sampling unit, publication year and sample size) significantly account for between-study variance.

This study provides the first meta-analytic review of the relationship between country-of-origin image and consumer brand evaluation to help clarify mixed findings and balance out the literature, which has only seen quantitative reviews on product evaluation and purchase decisions.

This study aims to examine the effects of perceived product–brand fit and brand type on consumer evaluations of wearable smart masks’ technological, aesthetic and social attributes and how these affect consumers’ attitudes and intentions to use.

Through an experimental approach, a total of 240 US consumers’ evaluations of smart masks are compared according to perceived product–brand fit (high vs low) and brand type (electronics vs fashion).

The results showed that high perceived product–brand fit increases consumers’ evaluations, while brand type did not significantly affect consumers’ evaluations. Among various attributes, social acceptability had the greatest influence on consumers’ attitude and intention to use. Perceived ease of use, however, positively influenced attitude but negatively influenced intention to use.

As consumers’ interest in smart health-care wearables increases and air pollution is a serious issue across countries, research on wearable smart masks is being facilitated. Smart masks refer to the digitalized, reusable wearable masks that provide protection and health-care functions. However, their market penetration is still limited. To close this gap between smart mask technology and the market, this study examines how perceived fit and brand type can be used to enhance consumer evaluations.

Capturing consumers’ notice by differentiating a product from competing brands in attaching an affixed label featuring product claims, as an alternative front-of-package (FOP) cue, has been widely used in fast-moving consumer goods retailing. This paper aims to apply perceived product newness as the basis for examining how affixed labeling, manipulated in terms of design features and message claims, can impact consumer evaluation.

Four between-subjects experiments examined the persuasive impact of the use of affixed labels. In particular, how product evaluation, in response to affixed labeling, varied as a function of its shape (Study 1a), location (Study 1b), the combination of shape and location cues (Study 1c) and the strength of message claims conveyed by such labels (Study 2). Perceived product newness is assessed as a mediator for all studies.

The results show the power of affixed labels in persuasion. Specifically, consumers tend to perceive the item as newer, achieving persuasion, when the affixed label has a distinctive shape or location. Yet, incorporating several unusual design components fails to trigger an elevated result if a singular visual stimulus serves as a cue for an item’s newness. Further, the strength of claims highlighted in an affixed label correlates to positive impact on evaluations.

This study offers an empirically based examination of consumers’ responses to affixed labeling and identifies perceived product newness as a mediator of the observed effect.

A salient, affixed label enables a credible cue for product newness, therefore, driving evaluation.

This paper contributes to understanding the influence on the persuasion of FOP labeling, with salience to retail promotional and sales messaging tactics.

Marketers often select unique color names for products to heighten shopper interest. The purpose of this study is to use self-referencing as the foundation for assessing how a product color name that involves the self impacts product evaluation. This paper also investigates the salient element that might moderate consumers’ responses, in particular for product categories where color serves mainly as decoration and is of secondary importance.

Two between-subjects experiments examined how self-referencing mediates the influence of generic versus identity color names on product evaluation (Study 1) and also tested whether the effect of naming product colors in identity terms is limited to the scenario where cognitive load is low rather than high (Study 2).

The results of this study show that identity color names are more persuasive than generic color names. Self-referencing mediates the results, and the favorable effect of identity color names is diminished in the case of high cognitive load. Such evidence suggests that consumers might face impediments for self-referenced processing when factors exhaust their cognitive resources. The decreased elaboration results in less favorable evaluation.

This research enriches knowledge of how an element as modest as a creative product color name influences consumer evaluation, with suggestions for approaches to color naming.

This study aims to propose and demonstrate a practical application of a new three-part holistic sensory evaluation (HSE) method for textiles and apparel based on the senses of sight, touch, hearing and smell. HSE method development was carefully documented, described and successfully applied to evaluate sensory characteristics and consumer perceptions and acceptance of bacterial cellulose (BC), a novel sustainable material for apparel.

In Part One of the HSE method, research participants described the material in their own words based on the senses of sight, touch, hearing and smell. In Part Two, they rated the intensities and their linking for 25 predetermined attributes describing BC. Part Three measured participants’ overall liking of BC and its perceived suitability for apparel and accessories.

Application of the HSE method resulted in an in-depth understanding of BC material. Areas for material improvements and positive characteristics were identified, providing direction for further development. Consumers found BC suitable for accessories and outer-layer garments but not for apparel.

Sensory evaluation of textiles and apparel has traditionally focused on the senses of touch and sight. The new HSE method allows evaluating the full range of sensory characteristics of materials/products and holistically assessing consumer perceptions. The method is especially useful for novel materials and wearable technology. BC has gained increased interests as a novel sustainable material, yet consumer studies have been lacking. This study reports a comprehensive evaluation of BC material from consumer perspective.

Will consumers accept artificial intelligence (AI) products that evaluate them? New consumer products offer AI evaluations. However, previous research has never investigated how consumers feel about being evaluated by AI instead of by a human. Furthermore, why do consumers experience being evaluated by an AI algorithm or by a human differently? This research aims to offer answers to these questions.

Three laboratory experiments were conducted. Experiments 1 and 2 test the main effect of evaluator (AI and human) and evaluations received (positive, neutral and negative) on fairness perception of the evaluation. Experiment 3 replicates previous findings and tests the mediation effect.

Building on previous research on consumer biases and lack of transparency anxiety, the authors present converging evidence that consumers who got positive evaluations reported nonsignificant difference on the level of fairness perception on the evaluation regardless of the evaluator (human or AI). Contrarily, consumers who got negative evaluations reported lower fairness perception when the evaluation was given by AI. Further moderated mediation analysis showed that consumers who get a negative evaluation by AI experience higher levels of lack of transparency anxiety, which in turn is an underlying mechanism driving this effect.

To the best of the authors' knowledge, no previous research has investigated how consumers feel about being evaluated by AI instead of by a human. This consumer bias against AI evaluations is a phenomenon previously overlooked in the marketing literature, with many implications for the development and adoption of new AI products, as well as theoretical contributions to the nascent literature on consumer experience and AI.

Despite the growing importance of visual information, user-generated photos in product reviews have received relatively little attention. We investigate whether the contextual background of a product image can influence consumers' perceptions of review helpfulness and product evaluation.

Online experiments were conducted using a scenario technique. A single factor (contextual background: low vs. high) between-subjects design was conducted in Study 1. A 2 (contextual background: low vs. high) × 2 (mental simulation: outcome vs. process) between-subjects design was conducted in Study 2.

A photo with a high (vs. low) contextual background enhances mental imagery, increasing perceived helpfulness and product evaluation. Furthermore, mental simulation plays a significant moderating role in the relationship between contextual background and mental imagery.

Based on cue utilization theory, this study identifies how the contextual background of product images affects consumers' perception and product evaluation by uncovering the underlying mechanism of mental imagery. Furthermore, the research examines the moderating effect of mental simulation while reviewing user-generated photos.

A successful process of design concept evaluation has positive influence on subsequent processes. This study aims to consider the evaluation information at multiple stages and the interaction among evaluators and improve the credibility of evaluation results.

This paper proposes a multi-stage approach for design concept evaluation based on complex network and bounded confidence. First, a network is constructed according to the evaluation data. Depending on the consensus degree of evaluation opinions, the number of evaluation rounds is determined. Then, bounded confidence rules are applied for the modification of preference information. Last, a planning function is constructed to calculate the weight of each stage and aggregate information at multiple evaluation stages.

The results indicate that the opinions of the evaluators tend to be consistent after multiple stages of interactive adjustment, and the ordering of design concept alternatives tends to be stable with the progress of the evaluation.

Updating preferences according to the bounded confidence rules, only the opinions within the trust threshold are considered. The attribute information of the node itself is inadequately considered.

This method addresses the need for considering the evaluation information at each stage and minimizes the impact of disagreements within the evaluation group on the evaluation results.

This study aimed to investigate the influence of brand-targeted animosity on consumers' boycott intentions for target fashion products via their cognitive and affective evaluations, in the context of the “Xinjiang cotton ban” incident. The moderating role of xenocentrism was also examined.

Data were collected through an online survey in China using convenience sampling, and 411 valid responses were obtained. The collected data were analyzed using SPSS 26.0 for the descriptive statistics, frequency analysis and reliability analysis. AMOS 24.0 was employed for the confirmatory factor and structural equation modeling analyses. Bootstrapping analysis using PROCESS Macro was employed to analyze the moderating effects.

This study found that consumers' brand animosity directly and positively affected boycott intentions and that this influence was sequentially mediated through cognitive-affective evaluations. However, cognitive product judgment did not directly affect boycott intentions. The results showed that xenocentrism had a moderating effect on the relationship between animosity and cognitive judgment. The higher the xenocentrism of consumers, the weaker the negative effect of animosity was on cognitive judgment.

This study bridges the gap in the literature on animosity and xenocentrism in a fashion-related context through examining the consequences of brand animosity.

The purpose of this study is to build a consensus model of social network group decision-making (SNGDM) based on improved PageRank algorithm. By objectively and fairly measuring the evaluation ability of participants in the decision-making process, the authors can improve the fairness and authenticity of the weight solution of decision-makers (DM) in the decision-making process. This ensures the reliability of the final group consensus results.

This study mainly includes six parts: preference expression, calculation of DM's weight, preference aggregation, consensus measurement, opinion adjustment and alternative selection. First, Pythagorean fuzzy expression is introduced to express the preference of DMs, which expands the scope of preference expression of DMs. Second, based on the social network structure among DMs, the process of “mutual judgment” among DMs is increased to measure the evaluation ability of DMs. On this basis, the PageRank algorithm is improved to calculate the weight of DMs. This makes the process of reaching consensus more objective and fair. Third, in order to minimize the evaluation difference between groups and individuals, a preference aggregation model based on plant growth simulation algorithm (PGSA) is proposed to aggregate group preferences. Fourth, the consensus index of DMs is calculated from three levels to judge whether the consensus degree reaches the preset value. Fifth, considering the interaction of DMs in the social network, the evaluation value to achieve the required consensus degree is adjusted according to the DeGroot model to obtain the overall consensus. Finally, taking the group preference as the reference, the ranking of alternatives is determined by using the Pythagorean fuzzy score function.

This paper proposes a consensus model of SNGDM based on improved PageRank algorithm to aggregate expert preference information. A numerical case of product evaluation is introduced, and the feasibility and effectiveness of the model are explained through sensitivity analysis and comparative analysis. The results show that this method can solve the problem of reaching consensus in SNGDM.

Different DMs may have different judgment criteria for the same decision-making problem, and the angle and depth of considering the problem will also be different. By increasing the process of mutual evaluation of DMs, the evaluation ability of each DM is judged only from the decision-making problem itself. In this way, the evaluation opinions recognized by most DMs will form the mainstream of opinions, and the influence of corresponding DMs will increase. Therefore, in order to improve the fairness and reliability of the consensus process, this study measures the real evaluation ability of DMs by increasing the “mutual judgment” process. On this basis, the defect of equal treatment of PageRank algorithm in calculating the weight of DMs is improved. This ensures the authenticity and objectivity of the weight of DMs. That is to improve the effectiveness of the whole evaluation mechanism. This method considers both the influence of DMs in the social network and their own evaluation level. The weight of DMs is calculated from two aspects: sociality and professionalism. It provides a new method and perspective for the calculation of DM’s weight in SNGDM.