Search results

The current study investigates the impact on perceived review helpfulness of the simultaneous processing of information from multiple cues with various central and peripheral cue combinations based on the elaboration likelihood model (ELM). Thus, the current study develops and tests hypotheses by analyzing real-world review data with a text mining approach in e-commerce to investigate how information consistency (rating inconsistency, review consistency and text similarity) influences perceived helpfulness. Moreover, the role of product type is examined in online consumer reviews of perceived helpfulness.

The current study collected 61,900 online reviews, including 600 products in six categories, from Amazon.com. Additionally, 51,927 reviews were filtered that received helpfulness votes, and then text mining and negative binomial regression were applied.

The current study found that rating inconsistency and text similarity negatively affect perceived helpfulness and that review consistency positively affects perceived helpfulness. Moreover, peripheral cues (rating inconsistency) positively affect perceived helpfulness in reviews of experience goods rather than search goods. However, there is a lack of evidence to demonstrate the hypothesis that product types moderate the effectiveness of central cues (review consistency and text similarity) on perceived helpfulness.

Previous studies have mainly focused on numerical and textual factors to investigate the effect on perceived helpfulness. Additionally, previous studies have independently confirmed the factors that affect perceived helpfulness. The current study investigated how information consistency affects perceived helpfulness and found that various combinations of cues significantly affect perceived helpfulness. This result contributes to the review helpfulness and ELM literature by identifying the impact on perceived helpfulness from a comprehensive perspective of consumer review and information consistency.

Contextual cues have become a hot research topic in the field of mobile consumer behavior, owing to the continuous rise of digital marketing. However, the complex online shopping scene makes it challenging to directly identify the association between the characteristics of contextual cues and consumer behavior. Presently, few studies have only systematically extracted and refined the types and characteristics of contextual cues. The purpose of this study is to explore the types and mechanisms of contextual cues in online shopping scenarios.

This study uses the word2vec algorithm, grounded theory and co-occurrence cluster method, along with online shopping word-of-mouth (WOM) text and consumer behavior theory, in order to explore different types of contextual cues and its efficiency from 5,619 comment corpus.

This study puts forward the following conclusions. (1) From the perspective of online shopping, contextual cues comprise aesthetic perception cues, value perception cues, trust-dependent cues, time perception cues, memory attention cues, spatial perception cues, attribute cues and relationship cues. (2) Based on the online shopping scenarios, contextual cues and their interaction effects exert an effect on consumer satisfaction, recommendation, purchase and return behavior.

The study conclusions are helpful to further reveal the deep association between contextual cues and consumer behavior in the process of online shopping, thus providing practical and theoretical enlightenment for enterprises to not only effectively reshape the scene but also promote the consumers' active purchase behavior.

This study aims to understand how consumers evaluate downscale vertical line extensions of a prestige/luxury original equipment manufacturer (OEM) in the European automotive market. The authors investigate the moderator effects of innovativeness and the need-for-status traits on the relationships between consumers' extension perceived fit (EPF), extension attitude (EA) and extension perceived value (EPV).

Experimental design with quantitative analyses based on a sample of 419 participants. Participants were randomly assigned to two treatments: low-fit and high-fit extension simulations.

The purchase intention of the downscale vertical extension of a luxury OEM brand is directly influenced by EPV and indirectly influenced by consumer EA and EPF with the parent brand. Findings also suggest that parent brand equity is transferable to extensions that present closeness and consistency with the brand’s heritage. Moreover, the need for status strengthens the relationship between the EPF and the extension perceived social value (EPSV).

The authors developed a realistic simulation of a downscale model of a well-known prestige/luxury car brand. The authors test the influence of innovativeness and need-for-status personal traits on consumer extension acceptance.

Improving a startup’s ability to obtain funding is critical to the survival of the organization. Although existing studies have observed various biases in investment decisions, few have studied the neural mechanisms behind such behavioral observations. We propose to apply cutting-edge neuroscientific techniques to uncover the neural processes engaged during pitches by entrepreneurs to investors and to use this new knowledge to identify strategy artifacts promoting pitch success. We hypothesize that pitches are dynamically shaped by covert cognitive, emotional, and social processes, which are in turn influenced by tactical approach (story-telling vs dry facts), physical context (online vs in-person), and demographics (gender, ethnicity). The role of inter-brain synchrony (i.e., correlation of cortical activity between brains) – within the startup team or between the entrepreneurs and investors – in pitch outcomes remains unknown. By uncovering the covert processes that mediate pitch outcomes, we provide an evidence-based, scientific approach to improving pitch success.

This study aims to discover how domain familiarity and language affect the cognitive load and the strategies applied for the evaluation of search engine results pages (SERP).

This study used an experimental research design. The pattern of the experiment was based upon repeated measures design. Each student was given four SERPs varying in two dimensions: language and content. The criteria of students to decide on the three best links within the SERP, the reasoning behind their selection, and their perceived cognitive load of the given task were the repeated measures collected from each participant.

The evaluation criteria changed according to the language and task type. The cognitive load was reported higher when the content was presented in English or when the content was academic. Regarding the search strategies, a majority of students trusted familiar sources or relied on keywords they found in the short description of the links. A qualitative analysis showed that students can be grouped into different types according to the reasons they stated for their choices. Source seeker, keyword seeker and specific information seeker were the most common types observed.

This study has an international scope with regard to data collection. Moreover, the tasks and findings contribute to the literature on information literacy.

This paper aims to provide an elucidative and explanatory overview of decision-making theory that human resource management and development (HR) researchers and practitioners can use to explore the impact of heuristics and biases on organizational decisions, particularly within HR contexts.

This paper draws upon three theoretical resources anchored in decision-making research: the theory of bounded rationality, the heuristics and biases program, and cognitive-experiential self-theory (CEST). A selective narrative review approach was adopted to identify, translate, and contextualize research findings that provide immense applicability, connection, and significance to the field and study of HR.

The authors extract key insights from the theoretical resources surveyed and illustrate the linkages between HR and decision-making research, presenting a theoretical framework to guide future research endeavors.

Decades of decision-making research have been distilled into a digestible and accessible framework that offers both theoretical and practical implications.

Heuristics are mental shortcuts that facilitate quick decisions by simplifying complexity and reducing effort needed to solve problems. Heuristic strategies can yield favorable outcomes, especially amid time and information constraints. However, heuristics can also introduce systematic judgment errors known as biases. Biases are pervasive within organizational settings and can lead to disastrous decisions. This paper provides HR scholars and professionals with a balanced, nuanced, and integrative framework to better understand heuristics and biases and explore their organizational impact. To that end, a forward-looking and direction-setting research agenda is presented.

Low-fit brand extensions offer several potential benefits, yet their success is challenging. Building on construal level theory, this study aims to investigate how different advertising appeals can improve the evaluations of low-fit brand extensions through two different processes (cognitive and affective).

Two experiments were conducted with US consumers. Study 1 used a 2 (extension fit: high, low) × 2 (ad appeal: abstract, concrete) between-subjects design. Study 2 applied a 2 (brand associations: promotion, prevention) × 2 (ad appeal: promotion, prevention) between-subjects design. Multivariate analyses and follow-up means comparisons were used to analyse data.

Study 1 found that an abstract ad appeal is more effective for promoting low-fit brand extension because it improves the perception of fit. Study 2 showed promotion vs prevention ad appeals lead to better evaluation of low-fit brand extensions when matched with parent brand associations (promotion vs prevention) in terms of construal level. This matching effect is underpinned by processing fluency.

Ad appeals can influence low-fit brand extension evaluation by influencing the perception of fit (cognitive process) or processing fluency (affective process). Future research could consider different ad appeals and other construal related factors to generalise these findings.

Marketers can design different ad appeals to effectively advertise low-fit brand extensions. These findings can guide managers in the development of effective advertising strategies.

This research offers a new perspective on how ad appeals can enhance low-fit brand extension evaluation.

This paper aims to explore the effectiveness of ethnically targeted ads in developing consumer–brand relationships through an application of social identification theory and elaboration likelihood model among African American consumers in the service sector.

Study 1 used a 2 (service type: hedonic vs utilitarian) × 2 (strength of ethnic identification: high vs low) between-subjects experimental design. Study 2 used the same experimental design, procedure and ads as Study 1.

Taken together, the findings from these two studies demonstrate how ethnically targeted advertising, in conjunction with service type, can influence consumer–brand relationships. The results showed that those with high strength of ethnic identification had more brand love, as well as higher intentions to spread positive word-of-mouth, purchase intention and brand loyalty for hedonic services, but that this effect was not significant for utilitarian services.

The environments in which advertisements appear (e.g. in an in-store display or a magazine advertisement) is important, and consumers’ reactions to targeted ads in various environments should be considered in future research. Future studies should also examine the role of individuals’ personality traits and level of acculturation in determining their relationships with brands.

Across two studies, the authors demonstrated that ethnic ads are more effective for African American individuals with high level of ethnic identification especially for hedonic services.

AI-driven product recommendation chatbots have markedly reduced operating costs and increased sales for marketers. However, previous literature has paid little attention to the effects of the personality of e-commerce chatbots. This study aimed to examine the ways that the interplay between the chatbot's and the user's personality can increase favorable product attitudes and future intentions to use the chatbot. Based on prior literature, we specifically focused on the degree of extroversion of both chatbot and user.

A total of 291 individuals participated in this study. Two different versions of chatbot were created for this study (i.e. extroversion: high vs. low). Participants self-reported their degree of extroversion. The PROCESS macro Model 1 and Model 7 with the Johnson–Neyman technique were employed to test the hypotheses.

The results showed that the high extroversion chatbot elicited greater user satisfactions and perceptions of chatbot friendliness among users with a high level of extroversion. On the contrary, the low extroversion chatbot resulted in greater user satisfactions and perceived chatbot friendliness among users with a low level of extroversion. This study further found that user satisfactions and perceived chatbot friendliness mediated the effects of the chatbot on greater intentions to use the chatbot and more favorable product attitudes.

By showing the effects of matching the personality of the chatbot and user, this study revealed that similarity-attraction effects also apply to human–chatbot interaction in e-commerce. Future studies would benefit by investigating the similarity-attraction effects in different characteristics, such as appearance, opinion and preference. This study also provides useful information for e-commerce marketers and chatbot UX/UI designers.

Despite being an authentic carrier of various cultural practices, the human body is often underutilised to access the knowledge of human body. Digital inventions today have created new avenues to open up cultural data resources, yet mainly as apparatuses for well-annotated and object-based collections. Hence, there is a pressing need for empowering the representation of intangible expressions, particularly embodied knowledge within its cultural context. To address this issue, the authors propose to inspect the potential of machine learning methods to enhance archival knowledge interaction with intangible cultural heritage (ICH) materials.

This research adopts a novel approach by combining movement computing with knowledge-specific modelling to support retrieving through embodied cues, which is applied to a multimodal archive documenting the cultural heritage (CH) of Southern Chinese martial arts.

Through experimenting with a retrieval engine implemented using the Hong Kong Martial Arts Living Archive (HKMALA) datasets, this work validated the effectiveness of the developed approach in multimodal content retrieval and highlighted the potential for the multimodal's application in facilitating archival exploration and knowledge discoverability.

This work takes a knowledge-specific approach to invent an intelligent encoding approach through a deep-learning workflow. This article underlines that the convergence of algorithmic reckoning and content-centred design holds promise for transforming the paradigm of archival interaction, thereby augmenting knowledge transmission via more accessible CH materials.