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- Dabirian, Amir, 1965-
(författare)
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Unpacking Employer Branding in the Information Technology Industry
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Attracting and retaining the best talent is a concern, particularly for knowledge-based firms in high-turnover industries, which rely on a limited supply of highly qualified individuals (Ewing, Pitt, De Bussy, & Berthon, 2002). In 2014, 36% of global employers criticized talent shortages, and in a 2015 study, 73% of CEOs reported being concerned about the availability of workers with key skills (Mosley, 2015). Employer branding is a key human resource and marketing strategy that contributes to the company’s brand, enhances the firm’s reputation as a great place for employees to work, and attracts a new workforce (Ahmad & Daud, 2016). An employer brand’s and its employer branding value propositions’ (EBV) ability to attract new employees and increase retention will provide benefits for the entire organization.EBV defines the employer’s attractiveness (Berthon et al., 2005), is a key aspect of the employer branding process, and provides differentiation for the firm (Alnıaçık & Alnıaçık, 2012; Backhaus & Tikoo, 2004; Berthon et al., 2005; Leekha Chhabra & Sharma, 2014; Moroko & Uncles, 2008) to attract and retain employees. Existing research viewed employer branding and its EBV from one or two views—employee or employer—and lacked multiview approaches to employer branding and employer attractiveness. This research focused on a holistic approach and addressed the question: “How do different EBVs affect the perceptions of employer attractiveness? To answer this question holistically, the following research subquestions emerged: RQ1: How do employees perceive the EBV of employer attractiveness?RQ2: How do current and former employees perceive the EBV of employer attractiveness?RQ3: How do potential employees perceive the EBV of employer attractiveness?RQ4: How do employers manage how employees perceive EBV? This research consisted of four empirical papers and focused on the information technology (IT) industry context. The first study focused on employee views from all industries, whereas the second study concentrated on the IT industry and compared current and former employees. Study 3 considered potential employees in the IT industry and operationalized the employee attractiveness construct and EBVs. The final study explored EBVs from the employer’s view in an IT firm and compared its employees’ views regarding the psychological contract. The design of this research is a mixed approach with descriptive and exploratory methodologies. IBM Watson’s artificial intelligence content analysis was used in Studies 1, 2, and 4.Contributions to the body of knowledge includes operationalizing the employee attractiveness construct as a set of EBVs. This research viewed EBVs holistically and extended the set of EBVs from five to eight value propositions for the IT industry. It also defined employer brand intelligence as a tool for practitioners to develop insights for their employer brand.The document is organized with an introductory chapter describing the overall research approach, followed by a literature review chapter, methodology chapter, and summary of findings and contributions. The four papers are included in the final chapter.
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| 2. |
- Lee, Linda W., et al.
(författare)
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Making sense of text : artificial intelligence-enabled content analysis
- 2020
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Ingår i: European Journal of Marketing. - : Emerald. - 0309-0566 .- 1758-7123. ; 54:3, s. 615-644
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Tidskriftsartikel (refereegranskat)abstract
- Purpose The purpose of this paper is to introduce, apply and compare how artificial intelligence (AI), and specifically the IBM Watson system, can be used for content analysis in marketing research relative to manual and computer-aided (non-AI) approaches to content analysis. Design/methodology/approach To illustrate the use of AI-enabled content analysis, this paper examines the text of leadership speeches, content related to organizational brand. The process and results of using AI are compared to manual and computer-aided approaches by using three performance factors for content analysis: reliability, validity and efficiency. Findings Relative to manual and computer-aided approaches, AI-enabled content analysis provides clear advantages with high reliability, high validity and moderate efficiency. Research limitations/implications - This paper offers three contributions. First, it highlights the continued importance of the content analysis research method, particularly with the explosive growth of natural language-based user-generated content. Second, it provides a road map of how to use AI-enabled content analysis. Third, it applies and compares AI-enabled content analysis to manual and computer-aided, using leadership speeches. Practical implications - For each of the three approaches, nine steps are outlined and described to allow for replicability of this study. The advantages and disadvantages of using AI for content analysis are discussed. Together these are intended to motivate and guide researchers to apply and develop AI-enabled content analysis for research in marketing and other disciplines. Originality/value To the best of the authors' knowledge, this paper is among the first to introduce, apply and compare how AI can be used for content analysis.
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| 3. |
- Whittaker, Lucas, et al.
(författare)
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"All Around Me Are Synthetic Faces" : The Mad World of AI-Generated Media
- 2020
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Ingår i: IT Professional Magazine. - : Institute of Electrical and Electronics Engineers (IEEE). - 1520-9202 .- 1941-045X. ; 22:5, s. 90-99
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Tidskriftsartikel (refereegranskat)abstract
- Advances in artificial intelligence and deep neural networks have led to a rise in synthetic media, i.e., automatically and artificially generated or manipulated photo, audio, and video content. Synthetic media today is highly believable and "true to life"; so much so that we will no longer be able to trust what we see or hear is unadulterated and genuine. Among the different forms of synthetic media, the most concerning forms are deepfakes and general adversarial networks (GANs). For IT professionals, it is important to understand what these new phenomena are. In this article, we explain what deepfakes and GANs are, how they work and discuss the threats and opportunities resulting from these forms of synthetic media.
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