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How to embed artificial intelligence (AI) in human resource management (HRM) is one of the core challenges of digital HRM. Despite regulations demanding humans in the loop to ensure human oversight of AI-based decisions, it is still unknown how much decision-makers rely on information provided by AI and how this affects (personnel) selection quality.

This paper presents an experimental study using vignettes of dashboard prototypes to investigate the effect of AI on decision-makers’ overreliance in personnel selection, particularly the impact of decision-makers’ information search behavior on selection quality.

Our study revealed decision-makers’ tendency towards status quo bias when using an AI-based ranking system, meaning that they paid more attention to applicants that were ranked higher than those ranked lower. We identified three information search strategies that have different effects on selection quality: (1) homogeneous search coverage, (2) heterogeneous search coverage, and (3) no information search. The more applicants were searched equally often (i.e. homogeneous) as when certain applicants received more search views than others (i.e. heterogeneous) the higher the search intensity was, resulting in higher selection quality. No information search is characterized by low search intensity and low selection quality. Priming decision-makers towards carrying responsibility for their decisions or explaining potential AI shortcomings had no moderating effect on the relationship between search coverage and selection quality.

Our study highlights the presence of status quo bias in personnel selection given AI-based applicant rankings, emphasizing the danger that decision-makers over-rely on AI-based recommendations.

The purpose of this paper is to evaluate Google question-answering (QA) quality.

Given the large variety and complexity of Google answer boxes in search result pages, existing evaluation criteria for both search engines and QA systems seemed unsuitable. This study developed an evaluation criteria system for the evaluation of Google QA quality by coding and analyzing search results of questions from a representative question set. The study then evaluated Google’s overall QA quality as well as QA quality across four target types and across six question types, using the newly developed criteria system. ANOVA and Tukey tests were used to compare QA quality among different target types and question types.

It was found that Google provided significantly higher-quality answers to person-related questions than to thing-related, event-related and organization-related questions. Google also provided significantly higher-quality answers to where- questions than to who-, what- and how-questions. The more specific a question is, the higher the QA quality would be.

Suggestions for both search engine users and designers are presented to help enhance user experience and QA quality.

Particularly suitable for search engine QA quality analysis, the newly developed evaluation criteria system expanded and enriched assessment metrics of both search engines and QA systems.

In online exchange platforms' sponsored search advertising, the array of product quality signals within a keyword search results list plays a crucial role in shaping buyers' purchasing decisions. This research seeks to explore the impact of various quality signals – namely, ranking position, seller reputation and product price – on ad clicks. Additionally, it examines the role of keyword attributes, such as specificity and popularity, in modulating the effects of these quality signals on advertising clicks.

A total of 5,763 effective data points were collected from a leading B2B electronic platform company, and we employed negative binomial regression with Heckman correction methods to test the hypotheses.

The results indicate that in online exchange platforms, search ad clicks are significantly and positively affected by displayed signals such as ranking position, seller reputation and product price information. Notably, a U-shaped relationship emerges between product price and ad clicks. Furthermore, keyword specificity and popularity distinctly moderate the impact of these displayed signals on ad clicks within online exchange platforms.

This paper addresses the gap in existing research on search advertising by methodically analyzing the impact of various signals displayed in search results and how keyword attributes moderate ad clicks, all through a signaling theory lens.

The purpose of this paper is to investigate returns to search (getting a better product and/or a lower price as a result of search) when consumers use/do not use recommendation agents (RAs). Specifically, it studies the effect of RAs/no RAs on decision quality, decision confidence and decision satisfaction taking into account subjective knowledge (SK) and involvement.

This paper employed two between-subjects factorial experimental designs with subjects searching for digital cameras in a simulated online digital camera store. The experiment was conducted with graduate students in Chennai, Bengaluru and Mysore in India.

Results of two online experiments showed that when consumers used RAs, low search led to better decision quality, whereas when consumers did not use RAs, medium search led to optimum decision quality. When consumers use RAs, SK had a U-shaped influence on the decision quality indicating that decision quality was the lowest for those with medium SK. When consumers did not use RAs, the effect of SK on decision quality was an inverted U-shape, indicating optimum decision quality for medium SK consumers. When consumers did not use RAs, subjects with high involvement made better choices, whereas when consumers used RAs, low involvement subjects made better choices. However, subjects who searched more had higher decision confidence and decision satisfaction even if their choices were not better.

The effect of RA vs no RA in conjunction with relevant consumer characteristics influencing decision quality of the consumer is demonstrated in this study. The findings have important managerial, consumer and theoretical contributions to make.

This paper explores the online customer experience (OCE) within business-to-business (B2B) websites. The purpose of this paper is to understand the influence of website credibility and information quality on the OCE during search for information and services. In addition, this paper acknowledges the role of customer support within the offline environment and thus explores the potential role of online customer support during a customer’s online experience.

An online experiment was conducted with 195 participants along with an associated questionnaire for data capture. Three tasks were developed for participants which they had to complete on three different business advisory websites. The online experiment gave all participants the experience of using a business advisory website. In order to analyse the hypothesised relationships, structural equation modelling was used.

The results outlined that the credibility of the website and the quality of the information on the website have a significant effect on the OCE in a B2B context. The research highlights the need for online customer support with a service representative during search on a B2B website. The credibility of the website as well as the success of the search drives the need for online customer support. The lack of online customer support will result in customers becoming dissatisfied with their experience if they have an unsuccessful search.

Managers of B2B websites should acknowledge the importance of website credibility cues and information quality cues. Each of these variables drive the success of a customer’s search and in turn the customer’s impression of the experience. Additionally, managers ought to provide customers online support, through functions such as an online help desk or a live chat function, as those who have an unsuccessful search expect to be able to seek online support from a company representative, the same way as they would do in the offline environment. Online customer support can act as a service recovery tool for website providers.

Empirical research on the OCE within B2B websites is limited and somewhat out-dated. Due to technological advancements and changing customer expectations, this research has filled a knowledge gap on the OCE in a B2B perspective. Website credibility and information quality have been overlooked in previous research in relation to the OCE. In addition, this study outlines the need for online customer support resulting from the need to clarify information and the success of the search.

The increasing availability of eXtensible Business Reporting Language (XBRL) financial statements motivates additional investigation of whether XBRL’s search-facilitating technology (SFT) and enhanced viewing capabilities facilitate information search and improve financial analysis decision quality and efficiency. This experiment investigates how using XBRL technology to view financial statements influences novice investors’ decision quality by affecting decision processes such as search strategy and effort, as well as decision efficiency (accuracy/effort) in a financial statement analysis task. In the experiment, randomly assigned student participants (n = 102) invested in companies using either static PDF-formatted or XBRL-enabled financial statements. No differences in decision quality (i.e., accuracy) due to technology use were observed. However, participants in the XBRL condition examined less information, used more directed search processes, and evidenced greater efficiency than did participants assigned to the PDF condition. Hence, the results suggest that XBRL SFT affects the use of differing decision processes relative to PDF technology.

Accurate information is the basis for well-informed decision-making, which is particularly challenging in the dynamic reality of a pandemic. Search engines are a major gateway for obtaining information, yet little is known about the quality and scientific accuracy of information answering conspiracy-related queries about COVID-19, especially outside of English-speaking countries and languages.

The authors conducted an algorithmic audit of Google Search, emulating search queries about COVID-19 conspiracy theories in 10 different locations and four languages (English, Arabic, Russian, and Hebrew) and used content analysis by native language speakers to examine the quality of the available information.

Searching the same conspiracies in different languages led to fundamentally different results. English had the largest share of 52% high-quality scientific information. The average quality score of the English-language results was significantly higher than in Russian and Arabic. Non-English languages had a considerably higher percentage of conspiracy-supporting content. In Russian, nearly 40% of the results supported conspiracies compared to 18% in English.

This study’s findings highlight structural differences that significantly limit access to high-quality, balanced, and accurate information about the pandemic, despite its existence on the Internet in another language. Addressing these gaps has the potential to improve individual decision-making collective outcomes for non-English societies.

While the growth of emerging technologies like Blockchain has created significant market opportunities and economic incentives for firms, it is valuable for both researchers and practitioners to understand their creation mechanisms. This paper aims to discuss the aforementioned objective.

Based on the knowledge search perspective, this study examines the impact of search boundary on innovation novelty and quality. Additionally, innovation targets, namely R&D innovation and application innovation, are proposed as the moderator of the knowledge search effect. Using a combination of machine learning algorithms such as natural language processing and classification models, the authors propose new methods to measure the identified concepts.

The empirical results of 3,614 Blockchain patents indicate that search boundary enhances both innovation novelty and innovation quality. For R&D innovation, the positive impact of search boundary on innovation quality is enhanced, whereas for application innovation, the positive effect of search boundary on innovation novelty is improved.

This study mainly contributes to the growing literature on emerging technologies by describing their creation mechanisms. Specifically, the exploration of R&D and application taxonomy enriches researchers' understanding of knowledge search in the context of Blockchain invention.

The purpose of this paper is to explore the evolution of greenhouse gas (GHG) reporting quality and to determine whether the evolution of reporting quality is linked with the type of information reported based on the “search”, “experience”, and “credence” typology.

The method is based on the content analysis of GHG reporting in 245 sustainability reports by 45 oil and gas companies between 1998 and 2010. The content analysis disclosure index developed links GHG reporting requirements with seven quality dimensions. The information associated with each item on the content analysis index is classified as “search”, “experience” or “credence”. Statistical analysis is used to determine whether any significant change occurred in either overall GHG reporting quality or in the quality of reporting in any of the individual dimensions of quality over the period of the study.

GHG reporting quality has not improved significantly between 1998 and 2010. The quality of reporting is not the same in each of the seven dimensions of quality and this can be explained by information typology.

This paper provides the first longitudinal analysis of the quality of GHG reporting. The methodology developed advances current measures of reporting quality by linking reporting requirements with particular quality dimensions. The results show that the type of information is important in terms of quality evolution and that this can dictate the measures required to improve quality.