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The basic random effects meta-analytic model is overwhelmingly dominant in psychological research. Indeed, it is typically employed even when more complex multilevel multivariate meta-analytic models are warranted. In this paper, we aim to help overcome challenges so that multilevel multivariate meta-analytic models will be more often employed in practice. We do so by introducing MLMVmeta-an easy-to-use web application that implements multilevel multivariate meta-analytic methodology that is both specially tailored to contemporary psychological research and easily estimable, interpretable, and parsimonious-and illustrating it across three case studies. The three case studies demonstrate the more accurate and extensive results that can be obtained via multilevel multivariate meta-analytic models. Further, they sequentially build in complexity featuring increasing numbers of experimental factors and conditions, dependent variables, and levels; this in turn necessitates increasingly complex model specifications that also sequentially build upon one another., (© 2022. The Psychonomic Society, Inc.)

Ideally, survey respondents read and understand survey instructions, questions, and response scales, and provide answers that carefully reflect their beliefs, attitudes, or knowledge. However, respondents may also arrive at their responses using cues or heuristics that facilitate the production of a response, but diminish the targeted information content. We use eye-tracking data as covariates in a Bayesian switching-mixture model to identify different response behaviors at the item-respondent level. The model distinguishes response behaviors that are predominantly influenced either positively or negatively by the previous response, and responses that reflect respondents' preexisting knowledge and experiences of interest. We find that controlling for multiple types of adaptive response behaviors allows for a more informative analysis of survey data and respondents., (© 2021. The Psychometric Society.)

Recent applications of item response tree models demonstrate that this model class is well suited to detect midpoint and extremity response style effects in both attitudinal and personality measurements. This paper proposes an extension of this approach that goes beyond measuring response styles and allows us to examine item-feature effects. In a reanalysis of three published data sets, it is shown that the proposed extension captures item-feature effects across affirmative and reverse-worded items in a psychological test. These effects are found to affect directional responses but not midpoint and extremity preferences. Moreover, accounting for item-feature effects substantially improves model fit and interpretation of the construct measurement. The proposed extension can be implemented readily with current software programs that facilitate maximum likelihood estimation of item response models with missing data., (© 2019 The British Psychological Society.)

In high stakes assessments of personality and similar attributes, test takers may engage in impression management (aka faking). This article proposes to consider responses of every test taker as a potential mixture of "real" (or retrieved) answers to questions, and "ideal" answers intended to create a desired impression, with each type of response characterized by its own distribution and factor structure. Depending on the particular mix of response types in the test taker profile, grades of membership in the "real" and "ideal" profiles are defined. This approach overcomes the limitation of existing psychometric models that assume faking behavior to be consistent across test items. To estimate the proposed faking-as-grade-of-membership (F-GoM) model, two-level factor mixture analysis is used, with two latent classes at the response (within) level, allowing grade of membership in "real" and "ideal" profiles, each underpinned by its own factor structure, at the person (between) level. For collected data, units of analysis can be item or scale scores, with the latter enabling analysis of questionnaires with many measured scales. The performance of the F-GoM model is evaluated in a simulation study, and compared against existing methods for statistical control of faking in an empirical application using archival recruitment data, which supported the validity of latent factors and classes assumed by the model using multiple control variables. The proposed approach is particularly useful for high-stakes assessment data and can be implemented with standard software packages. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

In the age of information explosion, consumers may no longer want to be overwhelmed with more information. But marketers are forced to display multiple features of a product in advertisements to fully demonstrate its competitive advantage, especially for a multifunctional product. Based on this, the authors explore when more could be an effective marketing strategy. The results of five studies demonstrated that the more features of multifunctional products displayed in video ads, the lower the processing fluency of consumers when watching the ads, thus reducing their positive emotions, and finally reducing their purchase intention, supporting the negative effect of more features on consumers in the context of marketing and revealed its cognitive and emotional mechanism. More important, we introduce mindfulness, an effective marketing strategy that alleviates the negative effect of too many product features on consumers, into the field of advertising marketing to address this issue. A high level of mindfulness, whether measured by trait scales or manipulated by experimental priming, weakens the negative effects of the number of displayed features. Specifically, when the level of mindfulness is high, the negative effect of displaying more product features on purchase intention is weak. When the level of mindfulness is low, the number of displaying more features has a strong negative impact on purchase intention. [ABSTRACT FROM AUTHOR]

This paper presents a systematic investigation of how affirmative and polar-opposite items presented either jointly or separately affect yea-saying tendencies. We measure these yea-saying tendencies with item response models that estimate a respondent's tendency to give a "yea"-response that may be unrelated to the target trait. In a re-analysis of the Zhang et al. (PLoS ONE, 11:1-15, 2016) data, we find that yea-saying tendencies depend on whether items are presented as part of a scale that contains affirmative and/or polar-opposite items. Yea-saying tendencies are stronger for affirmative than for polar-opposite items. Moreover, presenting polar-opposite items together with affirmative items creates lower yea-saying tendencies for polar-opposite items than when presented in isolation. IRT models that do not account for these yea-saying effects arrive at a two-dimensional representation of the target trait. These findings demonstrate that the contextual information provided by an item scale can serve as a determinant of differential item functioning.

Item response tree (IRTree) models form a family of psychometric models that allow researchers to control for multiple response processes, such as different sorts of response styles, in the measurement of latent traits. While IRTree models can capture quantitative individual differences in both the latent traits of interest and the use of response categories, they maintain the basic assumption that the nature and weighting of latent response processes are homogeneous across the entire population of respondents. In the present research, we therefore propose a novel approach for detecting heterogeneity in the parameters of IRTree models across subgroups that engage in different response behavior. The approach uses score-based tests to reveal violations of parameter heterogeneity along extraneous person covariates, and it can be employed as a model-based partitioning algorithm to identify sources of differences in the strength of trait-based responding or other response processes. Simulation studies demonstrate generally accurate Type I error rates and sufficient power for metric, ordinal, and categorical person covariates and for different types of test statistics, with the potential to differentiate between different types of parameter heterogeneity. An empirical application illustrates the use of score-based partitioning in the analysis of latent response processes with real data., (© 2024 The Author(s). British Journal of Mathematical and Statistical Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.)

We develop a Bayesian method for aggregating partial ranking data using the Thurstone model. Our implementation is a JAGS graphical model that allows each individual to rank any subset of items, and provides an inference about the latent true ranking of the items and the relative expertise of each individual. We demonstrate the method by analyzing data from new experiments that collected partial ranking data. In one experiment, participants were assigned subsets of items to rank; in the other experiment, participants could choose how many and which items they ranked. We show that our method works effectively for both sorts of partial ranking in applications to US city populations and the chronology of US presidents. We discuss the potential of the method for studying the wisdom of the crowd and other research problems that require aggregating incomplete or partial rankings., (© 2024. The Psychonomic Society, Inc.)

Abstract: Nocebo hyperalgesia is a pervasive problem in which the treatment context triggers negative expectations that exacerbate pain. Thus, developing ethical strategies to mitigate nocebo hyperalgesia is crucial. Emerging research suggests that choice has the capacity to reduce nocebo side effects, but choice effects on nocebo hyperalgesia have not been explored. This study investigated the impact of choice on conditioned nocebo hyperalgesia using a well-established electrocutaneous pain paradigm where increases in noxious stimulation were surreptitiously paired with the activation of a sham device. In study 1, healthy volunteers (N = 104) were randomised to choice over (nocebo) treatment administration, nocebo administration without choice, or a natural history control group. Nocebo hyperalgesia was greater for those with choice than no choice, suggesting that choice increased rather than diminished nocebo hyperalgesia. Study 2 tested whether providing positive information about the benefits of choice in coping with pain could counteract heightened nocebo hyperalgesia caused by choice. A different sample of healthy adults (N = 137) were randomised to receive nocebo treatment with choice and positive choice information, choice only, or no choice. The positive choice information failed to attenuate the effect of choice on nocebo hyperalgesia. The current results suggest that, rather than decreasing nocebo hyperalgesia, treatment choice may exacerbate pain outcomes when a painful procedure is repeatedly administered. As such, using choice as a strategy to mitigate nocebo outcomes should be treated with caution., (Copyright © 2024 International Association for the Study of Pain.)

Responding to rating scale items is a multidimensional process, since not only the substantive trait being measured but also additional personal characteristics can affect the respondents' category choices. A flexible model class for analyzing such multidimensional responses are IRTree models, in which rating responses are decomposed into a sequence of sub-decisions. Different response processes can be involved in item responding both sequentially across those sub-decisions and as co-occurring processes within sub-decisions. In the previous literature, modeling co-occurring processes has been exclusively limited to dominance models, where higher trait levels are associated with higher expected scores. However, some response processes may rather follow an ideal point rationale, where the expected score depends on the proximity of a person's trait level to the item's location. Therefore, we propose a new multidimensional IRT model of co-occurring dominance and ideal point processes (DI-MIRT model) as a flexible framework for parameterizing IRTree sub-decisions with multiple dominance processes, multiple ideal point processes, and combinations of both. The DI-MIRT parameterization opens up new application areas for the IRTree model class and allows the specification of a wide range of theoretical assumptions regarding the cognitive processing of item responding. A simulation study shows that IRTree models with DI-MIRT parameterization provide excellent parameter recovery and accurately reflect co-occurring dominance and ideal point processes. In addition, a clear advantage over traditional IRTree models with purely sequential processes is demonstrated. Two application examples from the field of response style analysis highlight the benefits of the general IRTree framework under real-world conditions., (© 2024. The Author(s).)

Rating scales are susceptible to response styles that undermine the scale quality. Optimizing a rating scale can tailor it to individuals' cognitive abilities, thereby preventing the occurrence of response styles related to a suboptimal response format. However, the discrimination ability of individuals in a sample may vary, suggesting that different rating scales may be appropriate for different individuals. This study aims to examine (1) whether response styles can be avoided when individuals are allowed to choose a rating scale and (2) whether the psychometric properties of self-chosen rating scales improve compared to given rating scales. To address these objectives, data from the flourishing scale were used as an illustrative example. MTurk workers from Amazon's Mechanical Turk platform (N = 7042) completed an eight-item flourishing scale twice: (1) using a randomly assigned four-, six-, or 11-point rating scale, and (2) using a self-chosen rating scale. Applying the restrictive mixed generalized partial credit model (rmGPCM) allowed examination of category use across the conditions. Correlations with external variables were calculated to assess the effects of the rating scales on criterion validity. The results revealed consistent use of self-chosen rating scales, with approximately equal proportions of the three response styles. Ordinary response behavior was observed in 55-58% of individuals, which was an increase of 12-15% compared to assigned rating scales. The self-chosen rating scales also exhibited superior psychometric properties. The implications of these findings are discussed., (© 2024. The Author(s).)

Platforms are a dominant model for coordinating complex economic interactions. Platforms are distinguished by growth through network effects: an increase in the number (N) of platform actors (or complements produced by them) on one side makes the platform more valuable for an actor on the same or other side (and vice versa). This paper unbundles the network effect—the feedback loop—into directional and distinct types of network externalities that define the value received by each platform side. For example, value from a network externality may relate to variety (e.g., Google Play apps) or availability (e.g., Uber rides). Furthermore, at times, network externalities can have a negative impact. Although the existing literature recognizes some of these constituents, we lack an integrative framework. To address this gap, we first distinguish the heterogeneity and persistence of platform value units to identify a 2 × 2 typology of four positive and negative network externalities: quantity versus rivalry, accumulation versus fragmentation, variety versus choice overload, and utility versus degradation. Second, we generalize network connectivity and horizontal complementarity as two types of network externality amplification. Third, we demonstrate how all these dynamics play together in interactions that ultimately underscore the platformvalue and growth. [ABSTRACT FROM AUTHOR]

We introduce multilevel multivariate meta-analysis methodology designed to account for the complexity of contemporary psychological research data. Our methodology directly models the observations from a set of studies in a manner that accounts for the variation and covariation induced by the facts that observations differ in their dependent measures and moderators and are nested within, for example, papers, studies, groups of subjects, and study conditions. Our methodology is motivated by data from papers and studies of the choice overload hypothesis. It more fully accounts for the complexity of choice overload data relative to two prior meta-analyses and thus provides richer insight. In particular, it shows that choice overload varies substantially as a function of the six dependent measures and four moderators examined in the domain and that there are potentially interesting and theoretically important interactions among them. It also shows that the various dependent measures have differing levels of variation and that levels up to and including the highest (i.e., the fifth, or paper, level) are necessary to capture the variation and covariation induced by the nesting structure. Our results have substantial implications for future studies of choice overload.

The recently proposed class of item response tree models provides a flexible framework for modeling multiple response processes. This feature is particularly attractive for understanding how response styles may affect answers to attitudinal questions. Facilitating the disassociation of response styles and attitudinal traits, item response tree models can provide powerful process tests of how different response formats may affect the measurement of substantive traits. In an empirical study, 3 response formats were used to measure the 2-dimensional Personal Need for Structure traits. Different item response tree models are proposed to capture the response styles for each of the response formats. These models show that the response formats give rise to similar trait measures but different response-style effects. (PsycINFO Database Record, ((c) 2017 APA, all rights reserved).)

Two different item response theory model frameworks have been proposed for the assessment and control of response styles in rating data. According to one framework, response styles can be assessed by analysing threshold parameters in Rasch models for ordinal data and in mixture-distribution extensions of such models. A different framework is provided by multi-process item response tree models, which can be used to disentangle response processes that are related to the substantive traits and response tendencies elicited by the response scale. In this tutorial, the two approaches are reviewed, illustrated with an empirical data set of the two-dimensional 'Personal Need for Structure' construct, and compared in terms of multiple criteria. Mplus is used as a software framework for (mixed) polytomous Rasch models and item response tree models as well as for demonstrating how parsimonious model variants can be specified to test assumptions on the structure of response styles and attitude strength. Although both frameworks are shown to account for response styles, they differ on the quantitative criteria of model selection, practical aspects of model estimation, and conceptual issues of representing response styles as continuous and multidimensional sources of individual differences in psychological assessment., (© 2017 The British Psychological Society.)

We review and evaluate selection methods, a prominent class of techniques first proposed by Hedges (1984) that assess and adjust for publication bias in meta-analysis, via an extensive simulation study. Our simulation covers both restrictive settings as well as more realistic settings and proceeds across multiple metrics that assess different aspects of model performance. This evaluation is timely in light of two recently proposed approaches, the so-called p-curve and p-uniform approaches, that can be viewed as alternative implementations of the original Hedges selection method approach. We find that the p-curve and p-uniform approaches perform reasonably well but not as well as the original Hedges approach in the restrictive setting for which all three were designed. We also find they perform poorly in more realistic settings, whereas variants of the Hedges approach perform well. We conclude by urging caution in the application of selection methods: Given the idealistic model assumptions underlying selection methods and the sensitivity of population average effect size estimates to them, we advocate that selection methods should be used less for obtaining a single estimate that purports to adjust for publication bias ex post and more for sensitivity analysis-that is, exploring the range of estimates that result from assuming different forms of and severity of publication bias., (© The Author(s) 2016.)

Statistical power and thus the sample size required to achieve some desired level of power depend on the size of the effect of interest. However, effect sizes are seldom known exactly in psychological research. Instead, researchers often possess an estimate of an effect size as well as a measure of its uncertainty (e.g., a standard error or confidence interval). Previous proposals for planning sample sizes either ignore this uncertainty thereby resulting in sample sizes that are too small and thus power that is lower than the desired level or overstate the impact of this uncertainty thereby resulting in sample sizes that are too large and thus power that is higher than the desired level. We propose a power-calibrated effect size (PCES) approach to sample size planning that accounts for the uncertainty associated with an effect size estimate in a properly calibrated manner: sample sizes determined on the basis of the PCES are neither too small nor too large and thus provide the desired level of power. We derive the PCES for comparisons of independent and dependent means, comparisons of independent and dependent proportions, and tests of correlation coefficients. We also provide a tutorial on setting sample sizes for a replication study using data from prior studies and discuss an easy-to-use website and code that implement our PCES approach to sample size planning., ((c) 2016 APA, all rights reserved).)

The conventional randomized response design is unidimensional in the sense that it measures a single dimension of a sensitive attribute, like its prevalence, frequency, magnitude, or duration. This paper introduces a multidimensional design characterized by categorical questions that each measure a different aspect of the same sensitive attribute. The benefits of the multidimensional design are (i) a substantial gain in power and efficiency, and the potential to (ii) evaluate the goodness-of-fit of the model, and (iii) test hypotheses about evasive response biases in case of a misfit. The method is illustrated for a two-dimensional design measuring both the prevalence and the magnitude of social security fraud.

Parental tradition transfer to children is pivotal for their socialization, identity formation, and culture perpetuation. But what motivates parents to transfer traditions to their children? We hypothesized that nostalgia, an emotion strengthening interpersonal bonds, would promote tradition transfer through parent-child relationship closeness. We tested these hypotheses using cross-sectional (Studies 1 and 4), cross-lagged (Study 2 and preregistered Study 5), and experimental (Studies 3 and 6) designs. In Studies 1 to 3, nostalgia was associated with, had lagged effect on, and promoted tradition transfer. In Studies 4–6, parent-child relationship closeness mediated the link between nostalgia and tradition transfer. The findings enrich our understanding of the vertical transmission of knowledge, customs, and values, offering insight into how intergenerational bonds are reinforced and cultural heritage is maintained. [ABSTRACT FROM AUTHOR]

Purpose: Cuteness has grown to be a global phenomenon fueled by the explosive usage of social media. Cute stimuli are ubiquitous, but few have explored their effects on consumer decision-making; direct evidence is particularly lacking in the area of risky choices. In this research, the authors theorize and demonstrate the unintended effects of cuteness exposure on people's risk preference. Design/methodology/approach: Across five experimental studies situated in various risk contexts, including health, financial and safety, the authors demonstrate that exposure to cuteness makes consumers more risk-seeking due to the reduction of situational conscientiousness. Study 1 used an experimental lab study with a real circumstance to test the effect on the risk associated with food consumption. Study 2 used a classical gambling experiment to test the effect on financial risk. Studies 3a and 3b used a mass shooting news article to test the effect on safety risk. Lastly, study 4 tested the mediating role of low conscientiousness with the classical gambling experiment. Findings: The findings show that exposure to cuteness makes people more likely to take risks in various domains (e.g. food consumption, safety and financial decisions). Research limitations/implications: This study tested the effect of cuteness on risk-seeking with a limited number of domains of risk. In addition, the authors tested the effect with visual cuteness stimuli, while individuals may perceive cuteness through other senses, such as sound. Practical implications: The findings have implications for business owners and marketers when deciding whether and how to use cuteness to promote their products and brands, as well as to avoid potential repercussions. For example, a marketer for a new extreme sports company could use videos or images of cute animals participating in sports on the company's social media channels to expand its market share. In addition, findings from this research would make consumers more attentive when facing cute appeals as they gain a better understanding of how exposure to cuteness could impact their own decision-making. Originality/value: To the best of the authors' knowledge, this research is the first to demonstrate that exposure to subtle, cute environmental cues has a robust effect on consumers' risk preferences across various domains, regardless of age and gender. [ABSTRACT FROM AUTHOR]

How do large-magnitude proportional systems affect invalid voting? We evaluate a Chilean electoral system reform that introduced proportionality. Voting is voluntary, and legislative and presidential elections are held concurrently. We compare the invalid votes between different types of elections before and after the reform using various difference-in-differences strategies. We find that invalid voting increased in legislative compared to presidential elections (pre-trends were parallel). The increase in invalid voting is greater in post-reform districts with higher magnitude and is not due to pre-reform district characteristics. The results of heterogeneity analyses and a survey experiment suggest the mechanism behind these findings is the cognitive burden associated with a longer ballot. This research highlights an understudied aspect of electoral systems: higher district magnitudes may be demobilizing in terms of valid voting for citizens who are more vulnerable to cognitive burden, even among those motivated enough to vote in the first-order election. [ABSTRACT FROM AUTHOR]

Decisions about what we consume have environmental repercussions and, therefore, implications for future generations. Consumers' ability to pay more for sustainable food stimulates production strategies, like circular agriculture, aiding the European Green Deal's sustainable food system. With the aim of analysing the preferences and willingness to pay of European consumers for food labelled as obtained by more sustainable systems in terms of reduced greenhouse gas emissions, and the optimisation of soil nutrients, a survey with 5591 participants from Spain, Poland, Italy, Hungary, Croatia, and Belgium was conducted. The survey was designed to analyse three food products (pork meat, milk, and bread) obtained through different production systems (circular, conventional, and organic) using the discrete choice experiment methodology. The survey included questions about consumers' environmental attitudes and consumption behaviour, to identify their influence over preferences regarding sustainable food products. Results revealed that over 27% of consumers preferred circular food, unveiling a potential market. This preference highlighted the effect of consumers' environmental attitudes. Those who actively engaged in recycling were more prone to choose circular food and shown a tendency to go for less conventional options. Consumers' WTP was consistently higher for circular milk compared to conventional across all the studied countries. However, for circular pork and bread, this greater WTP was reported exclusively in Spain and Croatia. It was suggested that sectors involved in a sustainable food production should standardise labels for circular food, create educational programmes about problems generated by unsustainable consumption, and promote consumption of circular products. [ABSTRACT FROM AUTHOR]

Stress management during examinations is an issue perpetually concerning students at all levels of education. This research endeavor focuses not merely on the plethora of factors associated with stress during examinations but rather focuses primarily on specific emotions developed during written and oral exams, introducing an original method, based on electroencephalograph (EEG) data. In an attempt to address the role of emotions in stress management, we organized an experiment on a sample of 30 postgraduate students, administering the Trier research protocol. The validated Emotiv Epoc+ device was used, while acquired the data were processed using a combined methodology of network and time series analysis applied in three (3) steps. Initially a descriptive analysis was performed based on the extracted frequencies, while in the second phase a network analysis was conducted to understand the extend of coordination between measured emotions (stress, engagement, interest, relaxation, excitement, and focus), using the tools of centrality. Finally, time series analysis was employed to indicate the level of auto- and cross-correlation between stress and other emotions. Our findings show that (eu)stress plays an imperative role in examination success, as long as it normally fluctuates within middle-range values (on a scale 0–100), and is being restrained by other feelings, such as interest, excitement, and focus [ABSTRACT FROM AUTHOR]

Building on previous research demonstrating that demographic growth of racial minorities increases realistic threat and prejudice among majority group members, we examined whether demographic increases of groups associated with symbolic threat (lesbian, gay, bisexual, and transgender [LGBT] and nonreligious groups) increased realistic threat, symbolic threat, and/or prejudice. In a single-paper meta-analysis across four studies, participants who read that LGBT groups were becoming more prevalent in the United States exhibited heightened perceptions of realistic threat and (especially) symbolic threat from these groups, which in turn predicted anti-LGBT prejudice. Two similar studies examining the growth of nonreligious groups demonstrated weaker effects. Implications for America's growing diversity and future directions for studying these effects are discussed. [ABSTRACT FROM AUTHOR]

ChatGPT is expected to have significant implications for trip planning from a traveler's perspective. ChatGPT has the potential to be a revolutionary search tool, enabling travelers to bypass an often complicated and disturbing process with a simple conversation with ChatGPT. This research aimed to explain the impact of ChatGPT on travelers' trip planning behavior; specifically, it examined how travelers perceive ChatGPT when they narrow down multiple travel options. Based on the choice overload effect, we conducted five experimental studies to determine the negative effect of reducing options from the initial recommendations using ChatGPT. The results showed the importance of a hybrid decision-making process involving both humans and ChatGPT. Using our empirical results, we provide significant theoretical and practical implications for travelers' trip planning behavior incorporating ChatGPT. [ABSTRACT FROM AUTHOR]

The use of discrete categorical formats to assess psychological traits has a long-standing tradition that is deeply embedded in item response theory models. The increasing prevalence and endorsement of computer- or web-based testing has led to greater focus on continuous response formats, which offer numerous advantages in both respondent experience and methodological considerations. Response styles, which are frequently observed in self-reported data, reflect a propensity to answer questionnaire items in a consistent manner, regardless of the item content. These response styles have been identified as causes of skewed scale scores and biased trait inferences. In this study, we investigate the impact of response styles on individuals' responses within a continuous scale context, with a specific emphasis on extreme response style (ERS) and acquiescence response style (ARS). Building upon the established continuous response model (CRM), we propose extensions known as the CRM-ERS and CRM-ARS. These extensions are employed to quantitatively capture individual variations in these distinct response styles. The effectiveness of the proposed models was evaluated through a series of simulation studies. Bayesian methods were employed to effectively calibrate the model parameters. The results demonstrate that both models achieve satisfactory parameter recovery. Neglecting the effects of response styles led to biased estimation, underscoring the importance of accounting for these effects. Moreover, the estimation accuracy improved with increasing test length and sample size. An empirical analysis is presented to elucidate the practical applications and implications of the proposed models. [ABSTRACT FROM AUTHOR]

Search platforms are contemporary digital services that create value on multi-sided markets through providing services to each side of their platforms—indexing contents for all website operators on the internet, placing pages and tracking performance for paid advertisers or sellers, and searching market offerings to match inquiries for web users. Search platforms are interested in developing strategies at the sponsored ad set level to optimize consumer clicks. Drawing literature supports from hospitality, search engine advertising, and product assortment areas, our study examines the relationships between search platforms' paid ad strategies (in paid ad set size and set attractiveness) and consumer's single or multiple clicks on the sponsored ad section. We empirically test the relationships in a multinomial logit modeling approach using a large-scale disaggregated, hotel-search dataset collected from a leading Chinese search engine. Our results suggest a positive but diminishing impact of paid ad set size on a consumer's set selection and multiple click-through behavior. Our study also spotlights the distinct and often divergent moderating effects of two types of ad attractiveness, preference-based and social-based, on the relationship between paid ad set size and consumer responses. We discuss the theoretical and managerial implications of this research to search platforms and advertisers in hospitality alike. [ABSTRACT FROM AUTHOR]

In today's digitally driven landscape, people face an ever-expanding array of choices, potentially leading to choice overload or "The choice paradox." This phenomenon arises when the abundance of options overwhelms decision-making processes, causing dissatisfaction and increased difficulty in reaching a decision. To address this challenge, recommender systems, or recommendation agents, have emerged as tools designed to assist users in navigating the complex world of choices. Understanding the effectiveness of these systems in alleviating choice overload is crucial given their widespread use across various domains. This review aims to explore the extent to which recommender systems effectively mitigate choice overload and the factors contributing to their efficacy by consolidating available evidence. A systematic search on Scopus and Google Scholar was conducted, analyzing relevant texts that met specific eligibility criteria to comprehensively explore the topic. Eleven studies were selected from the pool to create a comprehensive overview of the existing literature. The findings underscore the complexities of assessing the impact of recommendation systems on choice difficulty. This complexity arises from the recognition that various variables, extending beyond system design to include user personal traits and decision contexts, can influence the effectiveness of recommendation agents. This scoping review emphasizes the need for further studies to truly understand the influence of recommender systems on users' decision-making processes. As a result, this review aims to provide a pivotal overview of current domain knowledge, paving the way for future research and insights to enhance decision-making in the face of the challenge of choice overload. [ABSTRACT FROM AUTHOR]

The number of candidates running in U.S. primary elections is increasing. Contested primary elections force voters to decide between candidates that share party labels and are ideologically similar. Similar to contexts of non-partisan elections, an increase in the number of candidates should exacerbate the cognitive demand of voting in primary elections. We expect large candidate fields in primaries make voters (1) spend less time looking at each candidate profile, (2) more likely to abstain, (3) more likely to vote based on ballot position, and (4) more likely to feel overwhelmed with the decision. All four of these expectations were preregistered. We test the effect of large candidate fields on voter behavior using a survey experiment (n = 2000) fielded by YouGov. We find large candidate fields cause respondents to spend less time looking at candidate profiles, to select candidates in the first half of the ballot, and to feel overwhelmed by the decision-making process. In contrast to our expectations, we find large candidate fields make respondents less likely to abstain. [ABSTRACT FROM AUTHOR]