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Article
Publication date: 12 October 2012

Evanthia Faliagka, Athanasios Tsakalidis and Giannis Tzimas

The purpose of this paper is to present a novel approach for recruiting and ranking job applicants in online recruitment systems, with the objective to automate applicant…

Abstract

Purpose

The purpose of this paper is to present a novel approach for recruiting and ranking job applicants in online recruitment systems, with the objective to automate applicant pre‐screening. An integrated, company‐oriented, e‐recruitment system was implemented based on the proposed scheme and its functionality was showcased and evaluated in a real‐world recruitment scenario.

Design/methodology/approach

The proposed system implements automated candidate ranking, based on objective criteria that can be extracted from the applicant's LinkedIn profile. What is more, candidate personality traits are automatically extracted from his/her social presence using linguistic analysis. The applicant's rank is derived from individual selection criteria using analytical hierarchy process (AHP), while their relative significance (weight) is controlled by the recruiter.

Findings

The proposed e‐recruitment system was deployed in a real‐world recruitment scenario, and its output was validated by expert recruiters. It was found that with the exception of senior positions that required domain experience and specific qualifications, automated pre‐screening performed consistently compared to human recruiters.

Research limitations/implications

It was found that companies can increase the efficiency of the recruitment process if they integrate an e‐recruitment system in their human resources management infrastructure that automates the candidate pre‐screening process. Interviewing and background investigation of applicants can then be limited to the top candidates identified from the system.

Originality/value

To the best of the authors’ knowledge, this is the first e‐recruitment system that supports automated extraction of candidate personality traits using linguistic analysis and ranks candidates with the AHP.

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Article
Publication date: 10 June 2020

Aureo Paiva Neto, Elaine Aparecida Lopes da Silva, Lissa Valéria Fernandes Ferreira and José Felipe Ribeiro Araújo

This paper aims to explore a hotel brand personality performance through electronic word-of-mouth. A complementary attribute is designed and tested in addition to the…

Abstract

Purpose

This paper aims to explore a hotel brand personality performance through electronic word-of-mouth. A complementary attribute is designed and tested in addition to the already existing five dimensions from the brand personality scale, denominated sustainability.

Design/methodology/approach

A sample of 16,175 reviews from the rating session of three hotel properties behind a brand was retrieved from TripAdvisor for a data mining procedure. A complementary list of associated words was considered in addition to the 42 personality traits of Aaker’s model, and a brief inventory was developed based on the 17 sustainable development goals (SDGs) to compose the sustainability dimension.

Findings

This study registered sincerity as the most representative dimension in its results, and ruggedness as the lowest. This is evidence that the latter is not suitable for representing a brand personality scale for hotels and could be replaced by sustainability.

Research limitations/implications

Despite the relevant findings, new surveys and tests are recommended to provide better support to the new proposed dimension.

Practical implications

This investigation enables hotel managers to work more effectively on their brand strategies based on sustainability-oriented brand personality, which could deliver economic, social and environmental benefits to the world by influencing consumption behavior in association with the SDGs.

Originality/value

This study differs from existing literature by attempting to fill a gap on the limitations of studies focused on linking brand personality to sustainability, and using data mining to reach this goal.

研究目的

本论文探索通过电子口碑形式的酒店品牌个性效用。本论文设计和检测了一个附加要素 (计价可持续性), 对现有的五项维度品牌个性量表进行补充

研究设计/方法/途径

本文样本为TripAdvisor同一品牌的三家酒店的16,175评论, 对其进行数据挖掘。本文扩充了Aaker模型的42项个性特点外的相关词汇, 并且建立了基于17项可持续发展战略目标(SDGs)的词汇库, 以确定可持续性维度

研究结果

本论文确立了真诚度为结果中最具代表性的维度, 坚固性为最低代表度。显而易见, 坚固性不适合代表酒店品牌个性, 需要被可持续性取代

研究理论限制/意义

尽管相关结果, 本文建议采用新问卷和测试来为新提出的维度做更好的理论支持

研究实际意义

hx672C;论文使得酒店经理能够更高效地运作, 基于可持续品牌个性的品牌战略, 这将带来结合SDGs的消费行为, 从而对世界带来经济、社会、和环境效益

研究原创性/价值

本论文区别于以往的文献, 连接品牌个性与可持续性, 使用数据挖掘的方法, 来实现研究目的, 对有限的相关文献做出贡献

Details

Journal of Hospitality and Tourism Technology, vol. 11 no. 2
Type: Research Article
ISSN: 1757-9880

Keywords

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Article
Publication date: 14 November 2016

Tsung-Yi Chen, Meng-Che Tsai and Yuh-Min Chen

For an enterprise, it is essential to win as many customers as possible. The key to successfully winning customers is often determined by understanding the personality

Abstract

Purpose

For an enterprise, it is essential to win as many customers as possible. The key to successfully winning customers is often determined by understanding the personality characteristics of the object of communication in order to employ an effective communication strategy. An enterprise needs to obtain the personality information of target or potential customers. However, the traditional method for personality evaluation is extremely costly in terms of time and labor, and it cannot acquire customer personality information without their awareness. Therefore, the manner in which to effectively conduct automated personality predictions for a large number of objects is an important issue. The paper aims to discuss these issues.

Design/methodology/approach

The diverse social media that have emerged in recent years represent a digital platform on which users can publicly deliver speeches and interact with others. Thus, social media may be able to serve the needs of automated personality predictions. Based on user data of Facebook, the main social media platform around the world, this research developed a method for predicting personality types based on interaction logs.

Findings

Experimental results show that the Naïve Bayes classification algorithm combined with a feature selection algorithm produces the best performance for predicting personality types, with 70-80 percent accuracy.

Research limitations/implications

In this research, the dominance, inducement, submission, and compliance (DISC) theory was used to determine personality types. Some specific limitations were encountered. As Facebook was used as the main data source, it was necessary to obtain related data via Facebook’s API (FB API). However, the data types accessible via FB API are very limited.

Practical implications

This research serves to build a universal model for social media interaction, and can be used to propose an efficient method for designing interaction features.

Originality/value

This research has developed an approach for automatically predicting the personality types of network users based on their Facebook interactions.

Details

Online Information Review, vol. 40 no. 7
Type: Research Article
ISSN: 1468-4527

Keywords

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Article
Publication date: 2 February 2021

Swati Garg, Shuchi Sinha, Arpan Kumar Kar and Mauricio Mani

This paper reviews 105 Scopus-indexed articles to identify the degree, scope and purposes of machine learning (ML) adoption in the core functions of human resource…

Abstract

Purpose

This paper reviews 105 Scopus-indexed articles to identify the degree, scope and purposes of machine learning (ML) adoption in the core functions of human resource management (HRM).

Design/methodology/approach

A semi-systematic approach has been used in this review. It allows for a more detailed analysis of the literature which emerges from multiple disciplines and uses different methods and theoretical frameworks. Since ML research comes from multiple disciplines and consists of several methods, a semi-systematic approach to literature review was considered appropriate.

Findings

The review suggests that HRM has embraced ML, albeit it is at a nascent stage and is receiving attention largely from technology-oriented researchers. ML applications are strongest in the areas of recruitment and performance management and the use of decision trees and text-mining algorithms for classification dominate all functions of HRM. For complex processes, ML applications are still at an early stage; requiring HR experts and ML specialists to work together.

Originality/value

Given the current focus of organizations on digitalization, this review contributes significantly to the understanding of the current state of ML integration in HRM. Along with increasing efficiency and effectiveness of HRM functions, ML applications improve employees' experience and facilitate performance in the organizations.

Details

International Journal of Productivity and Performance Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-0401

Keywords

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Article
Publication date: 10 November 2020

Samira Khodabandehlou, S. Alireza Hashemi Golpayegani and Mahmoud Zivari Rahman

Improving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start…

Abstract

Purpose

Improving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start, sparsity, scalability and interest drift that affect their performance. Despite the efforts made to solve these problems, there is still no RS that can solve or reduce all the problems simultaneously. Therefore, the purpose of this study is to provide an effective and comprehensive RS to solve or reduce all of the above issues, which uses a combination of basic customer information as well as big data techniques.

Design/methodology/approach

The most important steps in the proposed RS are: (1) collecting demographic and behavioral data of customers from an e-clothing store; (2) assessing customer personality traits; (3) creating a new user-item matrix based on customer/user interest; (4) calculating the similarity between customers with efficient k-nearest neighbor (EKNN) algorithm based on locality-sensitive hashing (LSH) approach and (5) defining a new similarity function based on a combination of personality traits, demographic characteristics and time-based purchasing behavior that are the key incentives for customers' purchases.

Findings

The proposed method was compared with different baselines (matrix factorization and ensemble). The results showed that the proposed method in terms of all evaluation measures led to a significant improvement in traditional collaborative filtering (CF) performance, and with a significant difference (more than 40%), performed better than all baselines. According to the results, we find that our proposed method, which uses a combination of personality information and demographics, as well as tracking the recent interests and needs of the customer with the LSH approach, helps to improve the effectiveness of the recommendations more than the baselines. This is due to the fact that this method, which uses the above information in conjunction with the LSH technique, is more effective and more accurate in solving problems of cold start, scalability, sparsity and interest drift.

Research limitations/implications

The research data were limited to only one e-clothing store.

Practical implications

In order to achieve an accurate and real-time RS in e-commerce, it is essential to use a combination of customer information with efficient techniques. In this regard, according to the results of the research, the use of personality traits and demographic characteristics lead to a more accurate knowledge of customers' interests and thus better identification of similar customers. Therefore, this information should be considered as a solution to reduce the problems of cold start and sparsity. Also, a better judgment can be made about customers' interests by considering their recent purchases; therefore, in order to solve the problems of interest drifts, different weights should be assigned to purchases and launch time of products/items at different times (the more recent, the more weight). Finally, the LSH technique is used to increase the RS scalability in e-commerce. In total, a combination of personality traits, demographics and customer purchasing behavior over time with the LSH technique should be used to achieve an ideal RS. Using the RS proposed in this research, it is possible to create a comfortable and enjoyable shopping experience for customers by providing real-time recommendations that match customers' preferences and can result in an increase in the profitability of e-shops.

Originality/value

In this study, by considering a combination of personality traits, demographic characteristics and time-based purchasing behavior of customers along with the LSH technique, we were able for the first time to simultaneously solve the basic problems of CF, namely cold start, scalability, sparsity and interest drift, which led to a decrease in significant errors of recommendations and an increase in the accuracy of CF. The average errors of the recommendations provided to users based on the proposed model is only about 13%, and the accuracy and compliance of these recommendations with the interests of customers is about 92%. In addition, a 40% difference between the accuracy of the proposed method and the traditional CF method has been observed. This level of accuracy in RSs is very significant and special, which is certainly welcomed by e-business owners. This is also a new scientific finding that is very useful for programmers, users and researchers. In general, the main contributions of this research are: 1) proposing an accurate RS using personality traits, demographic characteristics and time-based purchasing behavior; 2) proposing an effective and comprehensive RS for a “clothing” online store; 3) improving the RS performance by solving the cold start issue using personality traits and demographic characteristics; 4) improving the scalability issue in RS through efficient k-nearest neighbors; 5) Mitigating the sparsity issue by using personality traits and demographic characteristics and also by densifying the user-item matrix and 6) improving the RS accuracy by solving the interest drift issue through developing a time-based user-item matrix.

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Article
Publication date: 1 March 1974

Tom Schultheiss, Lorraine Hartline, Jean Mandeberg, Pam Petrich and Sue Stern

The following classified, annotated list of titles is intended to provide reference librarians with a current checklist of new reference books, and is designed to…

Abstract

The following classified, annotated list of titles is intended to provide reference librarians with a current checklist of new reference books, and is designed to supplement the RSR review column, “Recent Reference Books,” by Frances Neel Cheney. “Reference Books in Print” includes all additional books received prior to the inclusion deadline established for this issue. Appearance in this column does not preclude a later review in RSR. Publishers are urged to send a copy of all new reference books directly to RSR as soon as published, for immediate listing in “Reference Books in Print.” Reference books with imprints older than two years will not be included (with the exception of current reprints or older books newly acquired for distribution by another publisher). The column shall also occasionally include library science or other library related publications of other than a reference character.

Details

Reference Services Review, vol. 2 no. 3
Type: Research Article
ISSN: 0090-7324

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Article
Publication date: 5 June 2017

Atika Qazi, Ram Gopal Raj, Glenn Hardaker and Craig Standing

The purpose of this paper is to map the evidence provided on the review types, and explain the challenges faced by classification techniques in sentiment analysis (SA)…

Abstract

Purpose

The purpose of this paper is to map the evidence provided on the review types, and explain the challenges faced by classification techniques in sentiment analysis (SA). The aim is to understand how traditional classification technique issues can be addressed through the adoption of improved methods.

Design/methodology/approach

A systematic review of literature was used to search published articles between 2002 and 2014 and identified 24 papers that discuss regular, comparative, and suggestive reviews and the related SA techniques. The authors formulated and applied specific inclusion and exclusion criteria in two distinct rounds to determine the most relevant studies for the research goal.

Findings

The review identified nine practices of review types, eight standard machine learning classification techniques and seven practices of concept learning Sentic computing techniques. This paper offers insights on promising concept-based approaches to SA, which leverage commonsense knowledge and linguistics for tasks such as polarity detection. The practical implications are also explained in this review.

Research limitations/implications

The findings provide information for researchers and traders to consider in relation to a variety of techniques for SA such as Sentic computing and multiple opinion types such as suggestive opinions.

Originality/value

Previous literature review studies in the field of SA have used simple literature review to find the tasks and challenges in the field. In this study, a systematic literature review is conducted to find the more specific answers to the proposed research questions. This type of study has not been conducted in the field previously and so provides a novel contribution. Systematic reviews help to reduce implicit researcher bias. Through adoption of broad search strategies, predefined search strings and uniform inclusion and exclusion criteria, systematic reviews effectively force researchers to search for studies beyond their own subject areas and networks.

Details

Internet Research, vol. 27 no. 3
Type: Research Article
ISSN: 1066-2243

Keywords

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Book part
Publication date: 4 July 2019

Steve McDonald, Amanda K. Damarin, Jenelle Lawhorne and Annika Wilcox

The Internet and social media have fundamentally transformed the ways in which individuals find jobs. Relatively little is known about how demand-side market actors use…

Abstract

The Internet and social media have fundamentally transformed the ways in which individuals find jobs. Relatively little is known about how demand-side market actors use online information and the implications for social stratification and mobility. This study provides an in-depth exploration of the online recruitment strategies pursued by human resource (HR) professionals. Qualitative interviews with 61 HR recruiters in two southern US metro areas reveal two distinct patterns in how they use Internet resources to fill jobs. For low and general skill work, they post advertisements to online job boards (e.g., Monster and CareerBuilder) with massive audiences of job seekers. By contrast, for high-skill or supervisory positions, they use LinkedIn to target passive candidates – employed individuals who are not looking for work but might be willing to change jobs. Although there are some intermediate practices, the overall picture is one of an increasingly bifurcated “winner-take-all” labor market in which recruiters focus their efforts on poaching specialized superstar talent (“purple squirrels”) from the ranks of the currently employed, while active job seekers are relegated to the hyper-competitive and impersonal “black hole” of the online job boards.

Details

Work and Labor in the Digital Age
Type: Book
ISBN: 978-1-78973-585-7

Keywords

Content available
Article
Publication date: 8 September 2020

Alisha Ali, S. Mostafa Rasoolimanesh and Cihan Cobanoglu

Abstract

Details

Journal of Hospitality and Tourism Technology, vol. 11 no. 2
Type: Research Article
ISSN: 1757-9880

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Article
Publication date: 22 May 2020

Francisco Peco-Torres, Ana I. Polo-Peña and Dolores M. Frías-Jamilena

This study aims to analyze the effect of the use of social media on the perception of brand personality and to identify its effect on customer brand engagement.

Abstract

Purpose

This study aims to analyze the effect of the use of social media on the perception of brand personality and to identify its effect on customer brand engagement.

Design/methodology/approach

The study adopted an exploratory approach, adapting Aaker's brand personality scale (1997) to the context of cultural tourism before carrying out a quantitative study resorting to a structural equation modeling to obtain empirical evidence to identify these relationships.

Findings

The findings reveal that the use of social media has a positive effect on the perception of brand personality and that brand personality, likewise, has a positive effect on customer brand engagement.

Research limitations/implications

This study indicates that transmission of an attractive brand personality according to the desires of the public, combined with dissemination through social media, is a valid strategy to improve customer brand engagement.

Originality/value

This study represents an advance in the specialized literature on the value that consumers place on information transmitted through social media. Specifically, it sheds light on how the transmission of brand personality through social media affects customer brand engagement.

通过社交媒体在文化旅游中的品牌个性目的

本研究旨在分析使用社交媒体对品牌个性感知的影响, 并确定其对客户品牌参与度的影响。

设计/方法/方法

该研究采用探索性方法, 将Aaker(1997)的品牌人格量表适应文化旅游的背景, 然后使用结构方程模型(SEM)进行定量研究, 以获取关于拟议关系的经验证据

结果

研究结果表明, 社交媒体的使用对品牌个性的感知具有积极影响, 而品牌个性同样对客户品牌参与度也具有积极影响。

研究意义

这项研究表明, 根据公众的需求来传递有吸引力的品牌个性, 并通过社交媒体进行传播, 是提高客户品牌参与度的有效策略。

原创性/价值

这项研究代表了有关消费者对通过社交媒体传播的信息所具有的价值的专业文献的进步。 具体而言, 它揭示了通过社交媒体传播品牌个性如何影响客户品牌参与度。

La personalidad de marca en los recursos turísticos culturales a través de los social mediaPropósito

este estudio tiene como objetivo analizar el efecto del uso de los social media en la percepción de la personalidad de marca e identificar su efecto en el customer brand engagement.

Diseño/metodología/enfoque

el estudio adoptó un enfoque exploratorio, adaptando la Brand Personality Scale de Aaker (1997) al contexto del turismo cultural antes de llevar a cabo un estudio cuantitativo mediante un modelo de ecuaciones estructurales (SEM) para obtener evidencia empírica sobre las relaciones propuestas.

Hallazgos

los resultados revelan que el uso de los social media tiene un efecto positivo en la percepción de la personalidad de marca y que la personalidad de marca tiene un efecto positivo en el customer brand engagement.

Implicaciones de la investigación

este estudio demuestra que la transmisión de una personalidad de marca atractiva de acuerdo con los deseos del público, combinada con su difusión a través de los social media es una estrategia válida para mejorar el customer brand engagement.

Originalidad/valor

este estudio representa un avance en la literatura especializada sobre el valor que los consumidores otorgan a la información transmitida a través de los social media. Específicamente, arroja luz sobre cómo la transmisión de la personalidad de la marca a través de los social media influye en el customer brand engagement.

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