Search Satisfaction Research Articles

유튜브 정보 및 정보원 특성이 소비자 만족에 미치는 영향

Background/Objectives: YouTube is increasingly being used as a channel for acqumng consumer information, and studies have highlighted generational differences in media consumption. This study aims to analyze variances in the perception of YouTube information characteristics and explore differences in factors influencing consumers' information search satisfaction between the younger and older generations.
 Methods: An online survey was conducted targeting people who had searched for information regarding product purchases on YouTube within the past month. A total of 366 responses were statistically analyzed via t-test, analysis of variance, and multiple regression analysis using SPSS 26.0.
 Results: The study revealed disparities m the perception of YouTube information characteristics and information source characteristics based on demographic features. Multiple regression analysis demonstrated that accuracy and reliabili ty had a significant positive impact on consumer satisfaction in both age groups. The expertise and interactivity of the information source showed a significant effect only on the younger generation, whereas the originality and personalization of information had a significant impact on consumer satisfaction only in the older generation.
 Conclusion/Implications: This study holds academic significance and makes a unique contribution by identifying sub- dimensions of YouTube information characteristics. Furthermore, the study provides practical value by suggesting consumer information provision strategies for companies that intend to leverage YouTube for marketing.

Voice search behavior under human–vehicle interaction context: an exploratory study

PurposeAs voice search has progressively become a new way of information acquisition and human–computer interaction, this paper aims to explore the users' voice search behavior in human–vehicle interaction.Design/methodology/approachThis study employed mixed research methods, including questionnaires and interviews. A total of 151 Amazon MTurk volunteers were recruited to complete a questionnaire based on their most recent and impressive voice search experience. After the questionnaire, this paper conducted an online interview with the participants.FindingsThis paper studied users' voice search behavior characteristics in the context of the human–vehicle interaction and analyzed the voice search content, search need, search motivation and user satisfaction. In addition, this paper studied the barriers and suggestions for voice search in human–vehicle interaction through a content analysis of the interviews.Practical implicationsThis paper's analysis of users' barriers and suggestions has a specific reference value for optimizing the voice search interaction system and improving the service.Originality/valueThis study is exploratory research that seeks to identify users' voice search needs and tasks and investigate voice search satisfaction in human–vehicle interaction context.

Investigating the role of in-situ user expectations in Web search

Pre-adoption expectations often serve as an implicit reference point in users’ evaluation of information systems and are closely associated with their goals of interactions, behaviors, and overall satisfaction. Despite the empirically confirmed impacts, users’ search expectations and their connections to tasks, users, search experiences, and behaviors have been scarcely studied in the context of online information search. To address the gap, we collected 116 sessions from 60 participants in a controlled-lab Web search study and gathered direct feedback on their in-situ expected information gains (e.g., number of useful pages) and expected search efforts (e.g., clicks and dwell time) under each query during search sessions. Our study aims to examine (1) how users’ pre-search experience, task characteristics, and in-session experience affect their current expectations and (2) how user expectations are correlated with search behaviors and satisfaction. Our results with both quantitative and qualitative evidence demonstrate that: (1) user expectation is significantly affected by task characteristics, previous and in-situ search experience; (2) user expectation is closely associated with users’ browsing behaviors and search satisfaction. The knowledge learned about user expectation advances our understanding of users’ search behavioral patterns and their evaluations of interaction experience and will also facilitate the design, implementation, and evaluation of expectation-aware user models, metrics, and information retrieval (IR) systems.

Arrhythmogenic cardiomyopathy: right, left, both

The index case is a 64-year-old man with a past medical history of emphysema, who was found dead at home. The cause of death at autopsy was ischaemic heart disease. An incidental finding of myocyte necrosis, fibrosis and fatty infiltration was found in the left ventricle epicardium, suggestive of arrhythmogenic cardiomyopathy (ACM). Family members were referred for further investigation. ACM is a rare genetic condition associated with sudden death. Fibrofatty tissue replaces myocardium, starting from the epicardial surface. ACM is often described in the right ventricle although left ventricular involvement is possible. Cases of ACM over 22 years were reviewed at a large mortuary. In 19 cases, ACM was diagnosed with certainty, while ‘suspected ACM’ was diagnosed in 23 cases. The latter cohort had more comorbidities and decedents were older. In the combined group of 42 individuals, the distribution of pathology was as follows: 57% biventricular, 29% right ventricle and 14% left ventricle. ACM can be a subtle incidental finding, but with significant implications for family. We share the macro and microscopic features and compare differences amongst diagnosed versus suspected cases. Issues of search satisfaction and diagnostic uncertainty in older and often co-morbid patients are discussed.

Users’ search performance prediction in cross-device search

Users’ search performance indicates the effectiveness and success with which users’ information needs are met, which is calculated based on the relevance judgment by users themselves. This study proposed to explore the prediction of users’ search performance in the context of cross-device search. A user experiment was performed to collect users’ relevance judgments and search behaviors in cross-device search. Based on users’ relevance judgments, users’ search performance was evaluated by calculating the percentage of valid clicks, effective search time, nDCG@n, and satisfaction. A simple linear regression model was adopted to train the prediction model. The final results showed that a combination of users’ search performance in pre-switch sessions and their search behavior in post-switch sessions can attain the best prediction accuracy. Important features to predict users’ search performance in cross-device search shed light on improving search systems to aid users in completing the task efficiently.

Use of mind mapping in search process to clarify information needs and improve search satisfaction

A mind map is an approach to the organisation of the human mind that prepares the ground for thinking. Inspired by the function of the mind in handling a situation, this article reports on an empirical study that evaluated the efficiency of mind map techniques and tools in formulating and refining information needs. The study examined graduate students’ Internet information searching. Two simulated search tasks were completed by participants in two search sessions. The results revealed no statistically significant difference between searching with a mind map and without a mind map, and therefore, no advantage could be found for using a mind map in the search process. Participants were happier with their search session when not using mind maps; mind map might help information need clarification, but it is a barrier to interaction and serendipity retrieval. However, this could be due to the search setting where the mind map had to be used as a separate tool and not an integrated component of the search system. The article also discusses some potential benefits of mind mapping for searching.

Do my feelings fit the diagnosis? Avoiding misdiagnoses in psychosomatic consultation services.

Misdiagnoses are a major concern with far-reaching consequences, which have rarely been studied systematically. Therefore, the present study evaluated factors causing misdiagnoses identified by psychosomatic consultation services. Over a period of 5 years, all patients referred to the psychosomatic consultation services of a large university hospital were analyzed consecutively for misdiagnoses. We analyzed the reasons for suspecting a misdiagnosis through systematic introspection during peer supervision and evaluated the causes during semistructured interviews with the referring physician. In 165 psychosomatic consultations, 24 disorders were misdiagnosed (15%). The reasons for questioning the initial diagnoses were the consulting physician's feelings and thoughts resulting from the patients' inappropriate behavior during the consultation and unusual clinical features. In eight cases, the misdiagnosis resulted from availability bias, and in three cases each it resulted from confirmation bias, search satisfaction bias, and framing effect and attribution bias. However, lack of medical knowledge played only a minor role. This study highlights the nonrational elements of the diagnostic process. In the context of psychosomatic consultation services, introspection and intuitive thought processes are helpful in identifying misdiagnoses. Self-satisfaction (availability bias) and overconfidence (confirmation bias) are most likely to result in misdiagnoses.

The Effect of YouTube BJ Attributes on Information Usefulness, Information Search Satisfaction and Behavioral Intention –Focused on e-Sports Content

The Effect of YouTube BJ Attributes on Information Usefulness, Information Search Satisfaction and Behavioral Intention –Focused on e-Sports Content

Construal Matching in Online Search: Applying Text Analysis to Illuminate the Consumer Decision Journey

As consumers move through their decision journey, they adopt different goals (e.g., transactional vs. informational). In this research, the authors propose that consumer goals can be detected through textual analysis of online search queries and that both marketers and consumers can benefit when paid search results and advertisements match consumer search–related goals. In bridging construal level theory and textual analysis, the authors show that consumers at different stages of the decision journey tend to assume different levels of mental construal, or mindsets (i.e., abstract vs. concrete). They find evidence of a fluency-driven matching effect in online search such that when consumer mindsets are more abstract (more concrete), consumers generate textual search queries that use more abstract (more concrete) language. Furthermore, they are more likely to click on search engine results and ad content that matches their mindset, thereby experiencing more search satisfaction and perceiving greater goal progress. Six empirical studies, including a pilot study, a survey, three lab experiments, and a field experiment involving over 128,000 ad impressions provide support for this construal matching effect in online search.

Region-action LSTM for mouse interaction sequence based search satisfaction evaluation

Mouse interaction data contain a lot of interaction information between users and Search Engine Result Pages (SERPs), which can be useful for evaluating search satisfaction. Existing studies use aggregated features or anchor elements to capture the spatial information in mouse interaction data, which might lose valuable mouse cursor movement patterns for estimating search satisfaction. In this paper, we leverage regions together with actions to extract sequences from mouse interaction data. Using regions to capture the spatial information in mouse interaction data would reserve more details of the interaction processes between users and SERPs. To modeling mouse interaction sequences for search satisfaction evaluation, we propose a novel LSTM unit called Region-Action LSTM (RALSTM), which could capture the interactive relations between regions and actions without subjecting the network to higher training complexity. Simultaneously, we propose a data augmentation strategy Multi-Factor Perturbation (MFP) to increase the pattern variations on mouse interaction sequences. We evaluate the proposed approach on open datasets. The experimental results show that the proposed approach achieves significant performance improvement compared with the state-of-the-art search satisfaction evaluation approach.

Gender Parity in Gainful Employment and Other Gender Trends in the Job Market for Recent Pathology Graduates.

Gender-based barriers to equal salary, career advancement, and leadership still exist in medicine. Herein we provide the first report of data comparing the experiences of men and women seeking their first nonfellowship position in pathology. To identify gender trends regarding pathologists taking their first job after training and the relationship to various demographic factors, job search satisfaction, and outcome. Aggregate data from the College of American Pathologists Graduate Medical Education Committee Job Market surveys (2015-2018) were analyzed across multiple domains including residency focus, number and subspecialty of fellowships completed, and extent to which expectations were met in regard to work duties, geographic preference, benefits, and salary. These data were examined in the context of assessing gender-based differences. Comparable results were identified in all measured outcomes according to gender. There were no differences between gender and medical school type, relocation, residency training focus, number of fellowships completed, overall satisfaction with position accepted, salary, or extent to which the position met expectations. Similarly, there were also no discrepancies between gender and the geographic region in which positions were accepted, practice setting, practice subspecialty, partnership track, length of job search, or difficulty finding a position. Analysis from 4 years of job market survey data shows equivalent results between men and women looking for their first nonfellowship position in pathology. There were no significant differences with regard to difficulty finding a position, overall satisfaction with the position accepted, salary, benefits, or access to partnership track.

Investigating the role of eye movements and physiological signals in search satisfaction prediction using geometric analysis

Two general challenges faced by data analysis are the existence of noise and the extraction of meaningful information from collected data. In this study, we used a multiscale framework to reduce the effects caused by noise and to extract explainable geometric properties to characterize finite metric spaces. We conducted lab experiments that integrated the use of eye‐tracking, electrodermal activity (EDA), and user logs to explore users' information‐seeking behaviors on search engine result pages (SERPs). Experimental results of 1,590 search queries showed that the proposed strategies effectively predicted query‐level user satisfaction using EDA and eye‐tracking data. The bootstrap analysis showed that combining EDA and eye‐tracking data with user behavior data extracted from user logs led to a significantly better linear model fit than using user behavior data alone. Furthermore, cross‐user and cross‐task validations showed that our methods can be generalized to different search engine users performing different preassigned tasks.

The impact of result diversification on search behaviour and performance

Result diversification aims to provide searchers with a broader view of a given topic while attempting to maximise the chances of retrieving relevant material. Diversifying results also aims to reduce search bias by increasing the coverage over different aspects of the topic. As such, searchers should learn more about the given topic in general. Despite diversification algorithms being introduced over two decades ago, little research has explicitly examined their impact on search behaviour and performance in the context of Interactive Information Retrieval (IIR). In this paper, we explore the impact of diversification when searchers undertake complex search tasks that require learning about different aspects of a topic (aspectual retrieval). We hypothesise that by diversifying search results, searchers will be exposed to a greater number of aspects. In turn, this will maximise their coverage of the topic (and thus reduce possible search bias). As a consequence, diversification should lead to performance benefits, regardless of the task, but how does diversification affect search behaviours and search satisfaction? Based on Information Foraging Theory (IFT), we infer two hypotheses regarding search behaviours due to diversification, namely that (i) it will lead to searchers examining fewer documents per query, and (ii) it will also mean searchers will issue more queries overall. To this end, we performed a within-subjects user study using the TREC AQUAINT collection with 51 participants, examining the differences in search performance and behaviour when using (i) a non-diversified system (BM25) versus (ii) a diversified system (BM25 + xQuAD) when the search task is either (a) ad-hoc or (b) aspectual. Our results show a number of notable findings in terms of search behaviour: participants on the diversified system issued more queries and examined fewer documents per query when performing the aspectual search task. Furthermore, we showed that when using the diversified system, participants were: more successful in marking relevant documents, and obtained a greater awareness of the topics (i.e. identified relevant documents containing more novel aspects). These findings show that search behaviour is influenced by diversification and task complexity. They also motivate further research into complex search tasks such as aspectual retrieval—and how diversity can play an important role in improving the search experience, by providing greater coverage of a topic and mitigating potential bias in search results.

Does Diversity Affect User Satisfaction in Image Search

Diversity has been taken into consideration by existing Web image search engines in ranking search results. However, there is no thorough investigation of how diversity affects user satisfaction in image search. In this article, we address the following questions: (1) How do different factors, such as content and visual presentations, affect users’ perception of diversity? (2) How does search result diversity affect user satisfaction with different search intents? To answer those questions, we conduct a set of laboratory user studies to collect users’ perceived diversity annotations and search satisfaction. We find that the existence of nearly duplicated image results has the largest impact on users’ perceived diversity, followed by the similarity in content and visual presentations. Besides these findings, we also investigate the relationship between diversity and satisfaction in image search. Specifically, we find that users’ preference for diversity varies across different search intents. When users want to collect information or save images for further usage (the Locate search tasks), more diversified result lists lead to higher satisfaction levels. The insights may help commercial image search engines to design better result ranking strategies and evaluation metrics.

Enterprise search and discovery capability: The factors and generative mechanisms for user satisfaction

Many organisations are re-creating the ‘Google-like’ experience behind their firewall to exploit their information. However, surveys show dissatisfaction with enterprise search is commonplace. No prior study has investigated unsolicited user feedback from an enterprise search user interface to understand the underlying reasons for dissatisfaction. A mixed-methods longitudinal study was undertaken analysing feedback from over 1000 users and interviewing search service staff in a multinational corporation. Results show that 62% of dissatisfaction events were due to human (information and search literacy) rather than technology factors. Cognitive biases and the ‘Google Habitus’ influence expectations and information behaviour and are postulated as deep underlying generative mechanisms. The current literature focuses on ‘structure’ (technology and information quality) as the reason for enterprise search satisfaction, agency (search literacy) appears downplayed. Organisations which emphasise ‘systems thinking’ and bimodal approaches towards search strategy and information behaviour may improve capabilities.

인공지능 기반 모바일 여행 정보 특성에 따른 소비자 만족 및 행동의도에 대한 연구

인공지능 기반 모바일 여행 정보 특성에 따른 소비자 만족 및 행동의도에 대한 연구

User Satisfaction Prediction with Mouse Movement Information in Heterogeneous Search Environment

Satisfaction prediction is one of the prime concerns in search performance evaluation. It is a non-trivial task for three major reasons: (1) The definition of satisfaction is subjective and different users may have different opinions in the process of satisfaction judgment. (2) Most existing studies on satisfaction prediction mainly rely on users' click-through or query reformulation behaviors but there are many sessions without such interactions. (3) Most existing works primarily rely on the hypothesis that all results on search result pages (SERPs) are homogeneous, but a variety of heterogeneous search results have been aggregated into SERPs to improve the diversity and quality of search results recently. To shed light on these research questions, we construct an experimental search engine that could collect users' satisfaction feedback as well as mouse click-through/movement data. Inspired by recent studies in predicting search result relevance based on mouse movement patterns (namely, motifs), we propose to estimate search satisfaction with motifs extracted from mouse movement data on SERPs. Besides the existing frequency-based motif selection method, two novel selection strategies (distance-based and distribution-based) are also adopted to extract high-quality motifs for satisfaction prediction. Experimental results show that the proposed strategies outperform existing methods and have promising generalization capability for unseen users and queries in both a homogeneous and heterogeneous search environment.

Time-Aware Click Model

Click-through information is considered as a valuable source of users’ implicit relevance feedback for commercial search engines. As existing studies have shown that the search result position in a search engine result page (SERP) has a very strong influence on users’ examination behavior, most existing click models are position based, assuming that users examine results from top to bottom in a linear fashion. Although these click models have been successful, most do not take temporal information into account. As many existing studies have shown, click dwell time and click sequence information are strongly correlated with users’ perceived relevance and search satisfaction. Incorporating temporal information may be important to improve performance of user click models for Web searches. In this article, we investigate the problem of properly incorporating temporal information into click models. We first carry out a laboratory eye-tracking study to analyze users’ examination behavior in different click sequences and find that the user common examination path among adjacent clicks is linear. Next, we analyze the user dwell time distribution in different search logs and find that we cannot simply use a click dwell time threshold (e.g., 30 seconds) to distinguish relevant/irrelevant results. Finally, we propose a novel time-aware click model (TACM), which captures the temporal information of user behavior. We compare the TACM to several existing click models using two real-world search engine logs. Experimental results show that the TACM outperforms other click models in terms of both predicting click behavior (perplexity) and estimating result relevance (NDCG).

Measuring and Predicting Search Engine Users’ Satisfaction

Search satisfaction is defined as the fulfillment of a user’s information need. Characterizing and predicting the satisfaction of search engine users is vital for improving ranking models, increasing user retention rates, and growing market share. This article provides an overview of the research areas related to user satisfaction. First, we show that whenever users choose to defect from one search engine to another they do so mostly due to dissatisfaction with the search results. We also describe several search engine switching prediction methods, which could help search engines retain more users. Second, we discuss research on the difference between good and bad abandonment, which shows that in approximately 30% of all abandoned searches the users are in fact satisfied with the results. Third, we catalog techniques to determine queries and groups of queries that are underperforming in terms of user satisfaction. This can help improve search engines by developing specialized rankers for these query patterns. Fourth, we detail how task difficulty affects user behavior and how task difficulty can be predicted. Fifth, we characterize satisfaction and we compare major satisfaction prediction algorithms.

A study on the user evaluation for an RDA-based Korean bibliography retrieval system

Purpose– The purpose of this paper is to validate the usefulness of resource description and access (RDA) from user perspectives by implementing an RDA-based bibliographic retrieval system, and comparing it against two retrieval systems.Design/methodology/approach– Surveys and interviews were conducted to gather responses from 20 subjects who used the systems. Usability was measured according to the following metrics: search usefulness from search process and results; search efficiency, measured in time and the number of steps involved; general satisfaction for search results and process, and for information need; satisfaction for search functionalities, with five sub-measures (usability of functions of search tool, appropriateness of search results, usability of additional information, usability of associative relations, and appropriateness of search categories); and system convenience in terms of understandability and ease.Findings– The survey results indicate that all but the satisfaction for appropriateness of search categories showed significant differences between the systems. The interviews show that the RDA system received from the subjects a more positive evaluation compared to the counterpart systems, in search usefulness, search efficiency, general search satisfaction, satisfaction for search functionalities.Practical implications– Though a few organizations such as the Library of Congress in the USA have implemented RDA, no such endeavors have been undertaken in the context of Korean bibliography, and especially for the systematic validation of usability of such a system from user perspectives.Originality/value– This is the first published study that validates the usefulness perceived by users of RDA in the context of Korean bibliography.