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The purpose of this study is to highlight special characteristics of building services systems and investigate how practitioners view reliability and maintenance. These characteristics include energy‐hungry services systems, operating modes, maintenance types, the relationship between procurement costs and maintenance costs.

The practitioners' viewpoints on reliability and maintenance are explored through a workshop. The authors wish to draw the attention of researchers in the reliability and maintenance community and furthermore emphasise the difference between building services systems and systems in industries other than construction.

It is shown that a lack of failure data and maintenance data is the main problem from both academic researchers' and industrial practitioner's points of view. The paper suggests that there exists no fixed cost ratio available to apply to building services systems; the analysis of RAMS (Reliability, Availability, Maintainability and Safety) should include duty cycles and the environment; and clients of the construction industry would benefit from mandating a LCC to be applied to the build.

The gap between academia and practitioners should be bridged through better understanding each other's needs. Accurately estimating the ratio between procurement and maintenance costs is needed from a whole life costing perspective.

This paper is a good reference for building designers, facility managers and maintenance staff of building services systems. It also offers reliability researchers references on special characteristics of building services systems.

Opens with the observation that the would‐be interviewer is faced with a daunting task for which the existing literature is not always helpful. Presents a résumé of the considerations necessary to begin successful personal interviews. These include: training and preparation, credibility, confidentiality, interview technique, lack of co‐operation, interview accuracy, interviewer bias, reliability and validity and overload. Combines the received wisdom of the literature with sound practical advice.

This study aims to determine system quality (SQ) and information quality (IQ) characteristics of voice assistants (VA) on smartphones that are affecting users’ satisfaction and technology acceptance, and how these affect behavioral intention (BI) to use.

This study uses the integrated model of user satisfaction and technology acceptance to evaluate users’ behavioral intention to use VAs on smartphones. The model represents a causal chain from the key characteristics of SQ and IQ to beliefs and attitudes that ultimately affect use. An online survey was conducted with 75 university students, and the data was analyzed using multivariate analysis: Simple linear regression analysis and multiple regression analysis.

While SQ and IQ of VAs had stronger effects on perceived ease of use (PEU), information satisfaction and PEU showed significant influences on perceived usefulness (PU). The results supported the influence of PEU, PU, and attitude on BI to use but with lesser effect than what PU and attitude together had on BI.

The sample was drawn from a population of students at a single and small university. Although this study received 160 responses, only 75 were appropriate for analysis.

There is no research, which adopts technology acceptance and user satisfaction approaches to VAs. To measure the causal effects, this study determined system and information characteristics that could explain SQ and IQ of the current VAs on smartphones. This study tested the proposed framework within the scope of the integrated approach.

Recently, with a high volume of users and user’s content in Community Question Answering (CQA) sites, the quality of answers provided by users has raised a big concern. Finding the expert users can be a method to address this problem, which aims to find the suitable users (answerers) who can provide high-quality relevant answers. The purpose of this paper is to find the expert users for the newly posted questions of the CQA sites.

In this paper, a new algorithm, RANKuser, is proposed for identifying the expert users of CQA sites. The proposed RANKuser algorithm consists of three major stages. In the first stage, folksonomy relation between users, tags, and queries is established. User profile attributes, namely, reputation, tags, and badges, are also considered in folksonomy. In the second stage, expertise scores of the user are calculated based on reputation, badges, and tags. Finally, in the third stage, the expert users are identified by extracting top N users based on expertise score.

In this work, with the help of proposed ranking algorithm, expert users are identified for newly posted questions. In this paper, comparison of proposed user ranking algorithm (RANKuser) is also performed with other existing ranking algorithms, namely, ML-KNN, rankSVM, LDA, STM CQARank, and EV-based model using performance parameters such as hamming loss, accuracy, average precision, one error, F-measure, and normalized discounted cumulative gain. The proposed ranking method is also compared to the original ranking of CQA sites using the paired t-test. The experimental results demonstrate the effectiveness of the proposed RANKuser algorithm in comparison with the existing ranking algorithms.

This paper proposes and implements a new algorithm for expert user identification in CQA sites. By utilizing the folksonomy in CQA sites and information of user profile, this algorithm identifies the experts.

This article aims to shows how changing information routines might influence service quality perceptions. A secondary aim was to test an instrument's everyday feasibility for healthcare quality assessment.

Patients often show high‐grade satisfaction with general care although they display dissatisfaction with some information they receive. A questionnaire survey was used to establish patient satisfaction after introducing standardised guidelines for nursing performance and information provision. Patient satisfaction was assessed using “quality from the patient's perspective” (QPP) questionnaire. Patients from gynaecological and haematological wards (n=71) (the study group) and a comparison group (n=67) were surveyed. Patients were given the questionnaire when their diagnosis was confirmed, after six months and 12 months. Data were collected over 36 months.

The study group showed an increased satisfaction with information from nurses (p=0.001) but not physicians. However, patients tended to put greater emphasis on socio‐cultural issues than information and cooperation seemed to represent high quality from the patient's perspective.

Successively lower response rate, mainly owing to cancer patients' deteriorating medical conditions. The study verifies the concordance model's relative merits.

The study verifies that care's softer side appears to be more important to patients than information improvements.

Results confirm that patients' satisfaction with information had implications for overall quality; but social issues seemed more important and enhancing quality is best achieved through participation and cooperation.

The purpose of this study is to examine the predictors of high‐quality answers in a community‐driven question answering service (Yahoo! Answers).

The identified predictors were organised into two categories: social and content features. Social features refer to the community aspects of the users and are extracted from explicit user interaction and feedback. Content features refer to the intrinsic and extrinsic content quality of answers that could be used to select the high‐quality answers. In total the framework built in this study comprises 17 features from two categories. Based on a randomly selected dataset of 1,600 question‐answer pairs from Yahoo! Answers, high‐quality answer predictors were identified.

The results of the analysis showed the importance of content appraisal features over social and textual content features. The features identified as strongly associated with high‐quality answers include positive votes, completeness, presentation, reliability and accuracy. Features weakly associated with high‐quality answers were high frequency words, answer length, and best answers answered. Features related to the asker's user history were found not to be associated with high‐quality answers.

This work could help in the reuse of answers for new questions. The study identified features that most influence the selection of high‐quality answers. Hence they could be used to select high‐quality answers for answering similar questions posed by users in the future. When a new question is posed, similar questions are first identified, and the answers for these questions are extracted and routed to the proposed quality framework for identifying high‐quality answers. Based on the overall quality index computed, the high‐quality answer could be returned to the asker.

Previous studies in identifying high‐quality answers were conducted using either of two approaches. First using social and textual content features found in community‐driven question answering services and second using content appraisal features by thorough assessment of answer quality provided by experts. However no study had integrated both approaches. Hence this study addresses this gap by developing an integrated generalisable framework to identify features that influence high‐quality answers.

The purpose of this paper is to examine the quality of answers on the Wikipedia Reference Desk, and to compare it with library reference services. It aims to examine whether Wikipedia volunteers outperform expert reference librarians and exemplify the paradox of expertise.

The study applied content analysis to a sample of 434 messages (77 questions and 357 responses) from the Wikipedia Reference Desk and focused on three SERVQUAL quality variables: reliability (accuracy, completeness, verifiability), responsiveness, and assurance.

The study reports that on all three SERVQUAL measures quality of answers produced by the Wikipedia Reference Desk is comparable with that of library reference services.

The collaborative social reference model matched or outperformed the dyadic reference interview and should be further examined theoretically and empirically. The generalizability of the findings to other similar sites is questionable.

Librarians and library science educators should examine the implications of the social reference on the future role of reference services.

The study is the first to: examine the quality of the Wikipedia Reference Desk; extend research on Wikipedia quality; use SERVQUAL measures in evaluating Q&A sites; and compare Q&A sites with traditional reference services.

Social question and answer (Q&A) systems have been rapidly developed on many e-commerce websites. The purpose of this paper is to explore how social Q&A systems influence consumers' information processing and purchase intention.

The authors design this research based on the information adoption model (IAM). First, the auhors consider the impacts of the central route (information factor) and peripheral route (social factor) on consumers' perception of information usefulness in Q&A systems. Then, the authors verify the influence of information and social aspects on purchase intention and empirically test the model with structural equation modelling (SEM) using 428 effective data samples.

On the whole, the authors prove that purchase intention is influenced by information and social aspects, which are two paths in Q&A systems. Specifically, both answer quality and social presence positively influence information usefulness. Interestingly, respondent credibility and answer consistency do not significantly impact information usefulness. Moreover, information usefulness positively affects information adoption, which positively affects consumer purchase intention.

This paper provides insights on social Q&A system mechanism design.

First, this paper is a useful complement to the research on social Q&A systems on e-commerce websites. Second, the authors provide a new theoretical lens through which the impacts of social Q&A systems on e-commerce websites are understood by extending the IAM. Third, the authors add answer consistency into original information process routes, which obtains a finding that is different from those of prior research.

This study aims to provide insights into US and Australian analysts' views regarding the relative importance of disclosing the direct method (DM) or indirect method (IM) statement of cash flows and forecasting firm performance.

Evidence is collected from responses to 104 surveys and 52 interviews completed by US and Australian analysts from 2017 to 2022. The survey and interview questions are developed with reference to the literature.

US and Australian analysts believe that the DM format provides incremental benefits compared to the IM for (1) confirming the reliability of earnings; (2) improving earnings confidence; (3) more accurate ex ante forecasts of operating cash flow and earnings; and (4) identifying opportunistic accruals manipulation. Analysts view that DM disclosure can lower firm-level cost of equity, although US interviewees more uniformly expect lower costs of equity under DM disclosure when firms yield low earnings quality. DM disclosure is also more important during unstable economic periods, as proxied by COVID-19.

Limited research currently exists regarding disclosure of the DM or IM and its impact on analysts' forecasting accuracy, earnings quality, economic uncertainty and cost of equity. Previous research has relied on archival research to examine differences between the DM and IM methods and are limited by data availability. Our findings are particularly relevant to the US market with few US firms reporting the DM format.

The importance of information systems (IS) professions is increasing. As personality–job fit theory claims, employees must have suitable personality traits for particular IS professions. However, candidates can try to fake-good on personality tests towards the desired personality type. Thus, the purpose of this study is to identify archetypal IS professions, their associated personality types and examine the reliability of the Myers–Briggs Type Indicator (MBTI) personality test in IS recruitment decisions.

The authors reviewed academic literature related to IS professions to identify job archetypes and personality traits for IS professions. Then, the authors conducted an experiment with 452 participants to investigate whether candidates can fake-good on personality tests when being tested for a particular IS profession.

The identified job archetypes were IS project manager, IS marketing specialist, IS consultant, IS security specialist, data scientist and business process analyst. The experimental results show that the participants were not able to fake-good considerably regarding their personality traits for a particular archetype.

The taxonomy of IS professions should be validated further. The experiment was executed in an educational organisation and not in a real-life environment. Actual work performance was not measured.

This study enables a better identification of suitable candidates for a particular IS profession. Personality tests are good indicators of the candidate's true personality type but must be properly interpreted.

This study enhances the existing body of knowledge on IS professions' archetypes, proposes suitable MBTI personality types for each profession and provides experimental support for the appropriateness of using personality tests to identify potentially suitable candidates.