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The view that successful memory performance depends importantly on the extent to which there is a match between the encoding and retrieval conditions is commonplace in memory research. However, Nairne ( Memory , 10 , 389–395, 2002 ) proposed that this idea about trace–cue compatibility being the driving force behind memory retention is a myth, because one cannot make unequivocal predictions about performance by appealing to the encoding–retrieval match. What matters instead is the relative diagnostic value of the match, and not the absolute match. Three experiments were carried out in which participants memorised word pairs and tried to recall target words when given retrieval cues. The diagnostic value of the cue was varied by manipulating the extent to which the cues subsumed other memorised words and the level of the encoding–retrieval match. The results supported Nairne’s ( Memory , 10 , 389–395, 2002 ) assertion that the diagnostic value of retrieval cues is a better predictor of memory performance than the absolute encoding–retrieval match.

Recent evidence suggests that proactive interference (PI) does not hurt event-based prospective memory (ProM) the way it does retrospective memory (RetroM) (Oates, Peynircioğlu, & Bates, 2015). We investigated this apparent resistance further. Introduction of a distractor task to ensure we were testing ProM rather than vigilance in Experiment 1 and tripling the number of lists to provide more opportunity for PI buildup in Experiment 2 still did not produce performance decrements. However, when the ProM task was combined with a RetroM task in Experiment 3, a comparable buildup and release was observed also in the ProM task. It appears that event-based ProM is indeed somewhat resistant to PI, but this resistance can break down when the ProM task comprises the same stimuli as in an embedded RetroM task. We discuss the results using the ideas of cue overload and distinctiveness as well as shared attentional and working memory resources.

With the rapid growth of e-commerce platforms, consumers increasingly make purchase decisions without direct physical interaction, particularly for tactile-dependent categories such as furniture and home décor. Drawing on embodied cognition, this study investigates how visual-based tactile cues influence consumers’ purchase intention on e-commerce platforms. Using experimental methods, two studies manipulate the level of visual-based tactile cues (high vs. low) and examine their effects on purchase intention. The results show that richer visual-based tactile cues significantly increase purchase intention. Contrary to traditional information overload assumptions that additional visual detail may hinder decision-making, this effect occurs through enhanced immersion rather than increased cognitive burden, suggesting that visual-based tactile cues operate as embodied sensory triggers instead of purely informational inputs. Furthermore, cross-modal mental imagery moderates this process in a counterintuitive way: the indirect effect of visual-based tactile cues on purchase intention via immersion is stronger when consumers’ imagery ability is lower, indicating a compensatory role of external sensory cues. By conceptualizing visual-based tactile cues as an innovation in interactive marketing within new media environments, this research offers actionable design implications for e-commerce platforms in enhancing tactile perception through visual presentation and improving conversion effectiveness.

Consumers need external information to support their product evaluation, especially with regard to experiencing the product during online shopping. Review keywords provide aggregated information of online reviews for consumers. However, whether and how review keywords affect consumers’ product consideration is still unelucidated. Considering clothing as the research target, we built a model to depict the impacts of website-generated quality- and fit-related review keywords on consumers’ consideration of clothing by bridging cue diagnosticity frameworks and product uncertainty theory. The hypotheses were verified by analyzing the objective data collected from e-commerce platforms and experiments. Results indicate that quality-related review keywords have a more salient positive impact on clothing consideration compared with fit-related keywords. Meanwhile, further complementary analysis based on self-generated review keywords suggests that presenting consumers with social-related keywords and consumer buyback keywords can improve clothing consideration significantly. The research contributes to the literature of product consideration and online review keywords, and related findings can help the platforms and e-merchants to better leverage the advantages of online review keywords.

PurposePrevious research about online recommendation systems has focused largely on their impact on customers' purchase decisions regarding the products being recommended, but it has mostly ignored how they may affect focal product evaluation. This research aimed to examine the influence of recommendation type (i.e. substitute-based vs complement-based) on focal product evaluation dependent on the brand image (i.e. warm vs competent).Design/methodology/approachFour laboratory experiments were conducted. Study 1 adopted an implicit association task. Studies 2 and 3 used a 2 (image: warmth vs competence) × 2 (product display: complements vs substitutes) between-subjects experimental design. Study 4 used a 2 (decision stage) × 2 (image) × 2 (product display) × continuous (need for cognition) between-subjects design.FindingsStudy 1 demonstrated a general “complementation (competition)—warmth (competence)” association. Studies 2 and 3 found that when a focal product had a warm (competent) image, complement-based (substitute-based) recommendations led customers to evaluate it more favorably than substitute-based (complement-based) recommendations. Study 3 further demonstrated that processing fluency mediates the above effect. Study 4 showed that this effect relies on heuristic processing and disappears for those who are in the screening stage or have a high need for cognition.Originality/valueTheoretically, this research extends the understanding of the stereotype content model of focal product brand image, the feelings-as-information process, and moderating roles of processing stage and need for cognition in e-commerce contexts. Practically, the findings provide online retailers a guideline for customizing their recommendation systems.

This paper analyzes notions and models of optimized cognition emerging at the intersections of psychology, neuroscience, and computing. What I somewhat polemically call the algorithms of mindfulness describes an ideal that determines algorithmic techniques of the self, geared at emotional resilience and creative cognition. A reframing of rest, exemplified in corporate mindfulness programs and the design of experimental artificial neural networks sits at the heart of this process. Mindfulness trainings provide cues as to this reframing, for they detail each in their own way how intermittent periods of rest are to be recruited to augment our cognitive capacities and combat the effects of stress and information overload. They typically rely on and co-opt neuroscience knowledge about what the brains of North Americans and Europeans do when we rest. Current designs for artificial neural networks draw on the same neuroscience research and incorporate coarse principles of cognition in brains to make machine learning systems more resilient and creative. These algorithmic techniques are primarily conceived to prevent psychopathologies where stress is considered the driving force of success. Against this backdrop, I ask how machine learning systems could be employed to unsettle the concept of pathological cognition itself.

PurposeThe helpfulness vote is a type of aggregate user representation that, by measuring the quality of an online review based on certain criteria, can allow readers to find helpful reviews more quickly. Although widely applied in practice, the effectiveness of the voting mechanism is unsatisfactory. This paper uses the heuristic–systematic model and the theory of dynamics of reviews to shed light on the effect of various information cues (product ratings, word count and product attributes in the textual content of reviews) on online reviews’ aggregative voting process. It proposes a conceptual model of seven empirically tested hypotheses.Design/methodology/approachA dataset of user-generated online hotel reviews (n = 6,099) was automatically extracted from Ctrip.com. In order to measure the variable of product attributes as a systematic cue, the paper uses Chinese word segmentation, a part-of-speech tag and word frequency statistics to analyze online textual content. To verify the seven hypotheses, SPSS 17.0 was used to perform multiple linear regression.FindingsThe results show that the aggregative process of helpfulness voting can be divided into two stages, initial and cumulative voting, depending on whether voting is affected by the previous votes. Heuristic (product ratings, word count) and systematic cues (product attributes in the textual content) respectively exert a greater impact on the two stages. Furthermore, the interaction of heuristic and systematic cues plays an important role in both stages, with a stronger impact on the cumulative voting stage and a weaker one on the initial stage.Practical implicationsThis paper’s findings can be used to explore improvements to helpfulness voting by aligning it with an individual’s information process strategy, such as by providing more explicating heuristic cues, developing different methods of presenting relevant cues to promote the voting decision at different stages, and specifying the cognitive mechanisms when designing the functions and features of helpfulness voting.Originality/valueThis study explores the aggregative process of helpfulness votes, drawing on the study of the dynamics of online reviews for the first time. It also contributes to the understanding of the influence of various information cues on the process from an information process perspective.

Authors may believe that having more information available about the research can help reviewers make better recommendations. However, too much information in a manuscript may create problems to the reviewers and may lead them to poorer recommendations. An information overload on the part of the reviewer might be a state in which she faces an amount of information comprising the accumulation of manuscript informational cues that inhibit the reviewer’s ability to optimally determine the best possible recommendation about the acceptance or rejection of the manuscript. Therefore the author wants to determine the amount of manuscript attributes to provide to reviewers. With this goal in mind we show that there is an intermediate number of manuscript attributes that maximizes the probability of acceptance. If too much research information is provided, some of it is not as useful for recommending acceptance, the average informativeness per research attribute evaluation is too low, and reviewers end up recommending rejection. If too little information is provided about the research, reviewers may end up not having sufficient details to recommend its acceptance. We also show that authors should provide more information to reviewers with more favorable initial valuation toward the research. For those reviewers with a less favorable prior attitude, the author should provide only the most important manuscript attributes. Given that expert reviewers face less load than potential readers, it follows that with respect to the target audience the optimal author’s strategy is also to trade off the amount of research information provided in the manuscript with the average informativeness of these items by selecting an intermediate number of attributes.

PurposeThe 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.Design/methodology/approachThe 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.FindingsThe 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.Originality/valuePrevious 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.

PurposeThe purpose of this paper is to draw on social exchange theory and heuristic–systematic model to examine how peer-to-peer (P2P) lending firms can enhance their customer acquisition by achieving mobile social media popularity.Design/methodology/approachContent data collected from multiple sources (websites and mobile applications) were employed to validate the research model.FindingsThe mobile social media popularity of P2P lending firms positively influences their customer acquisition. Furthermore, the heuristic cues (i.e. source credibility and content freshness) and the systematic cue (i.e. transaction relevance) potentially affect the firms’ mobile social media popularity.Research limitations/implicationsMobile social media is not only a platform for firms’ image-building but a critical means of acquiring actual customers. The appropriate use of heuristic–systematic cues in a mobile interface is useful for firms to achieve high user popularity despite the challenges derived from the mobile context.Practical implicationsTo achieve higher user popularity in the competitive online world, firms should dedicate greater effort in determining the adequate heuristic–systematic cues designed for the interface of their mobile social media account. The effect of popularity can then help the firms acquire more customers.Originality/valueThis study extends the understanding of social exchange in the context of mobile social media accounts and enriches the knowledge on business value of mobile social media popularity. This paper also contributes to the literature by relating heuristic–systematic cues to firms’ mobile social media popularity.