Dr Sara Ahmed


Lecturer/assistant professor in Organizational Behaviors & HRM, Programme Director of BSc Business Management (HRM Pathway); Communications Manager of Future of Work Research Centre
PhD, MSc, BA, FHEA, MCIPD
+44 (0)1483 682018
54 MS 03
Tuesdays 11:30 am- 12:30 pm, Thursdays 1:30- 2:30 pm. Tel: +44 (0) 1483 682018. 54 MS 03

Academic and research departments

People and Organisations, Surrey Business School.

About

Biography

Dr Sara Ahmed is a lecturer on Organizational Behaviors and Human Resource Management (OB-HRM) at the University of Surrey. She is the Programme Director of BSc. Business Management (HRM Pathway) and the Communications Manager of Future of Work Research Centre. She received an MSc in HRM and a PhD degree in HRM-OB from Brunel University, Where she was working as a research assistant and won several awards, such as the Remarkable Brunel Women Award 2015 and the Best PhD Paper Award in the Brunel Business School (BBS) Doctoral Symposium in 2012 and 2014. Before joining University of Surrey, Sara held a position as a lecturer in OB-HRM at BBS, Brunel University in London.

Sara's Main research interest focus on how applicant justice/fairness perceptions influence their job attitudes and behaviors, well-being and self-perceptions as well as the determinants of their fairness reactions. She has a keen interest in organizational justice in the context of personnel selection and promotion, new technology in personnel selection (e.g., Internet-based techniques), and cross-country/cultural examination of applicant reactions. Her research has been published in refereed international journals such as Journal of Management and International Journal of Selection and Assessment.

Dr Ahmed has won a British Academy/Leverhulme research grant award for her proposed project "Longitudinal Assessment of the Effect of Promotions on Employees' Well-Being, Work Attitudes and Performance". The grant sum is approximately £10k, with the proposal garnering very positive reviews. She is also the programme Director for BSc. Business Management (HRM Pathway). 

Research interests

  • Applicant reactions in personnel selection
  • Organizational justice/fairness
  • New technology in selection and assessment
  • Cross-country/Cultural examinations of applicant reactions

Research collaborations

Prof Neil Anderson, Brunel University, UK

Dr Ana-Cristina Costa, Brunel University, UK

Prof Julie McCarthy, University of Toronto Scarborough, Canada

Dr Ioannis Nikolaou, Athens University of Economics and Business, Greece  

Prof Talya Bauer, Portland State University, USA

Prof Donald Truxillo, Portland State University, USA

Teaching

Human Resource Management (MAN2133) (UG)

Psychological Assessment in the Workplace (MANM360) (PG) 

 

Departmental duties

- Programme Director BSc. Business Management (HRM Pathway)

- Communications Manager of Future of Work Research Centre

 

Publication highlights

REFEREED JOURNAL ARTICLES

Woods, S., Ahmed, S., Nikolaou, I., Anderson, N., & Costa, A. C. (in press). Digital selection procedures: A critical review and agenda for future research. European Journal of Work and Organizational Psychology. https://doi.org/10.1080/1359432X.2019.1681401. Available at: https://www.researchgate.net/publication/337062756_Personnel_selection_in_the_digital_age_a_review_of_validity_and_applicant_reactions_and_future_research_challenges 

Truxillo, D., Bauer, T., McCarthy, J., Anderson, N., Ahmed, S. (2018), Applicant perspectives on employee selection systems. In Anderson, N., Ones, D. S., Sinangil, H. K., & Viswesvaran, C. (Eds.). Handbook of Industrial, Work & Organizational Psychology. London: Sage. https://www.researchgate.net/profile/Talya_Bauer/publication/277000722_Applicant_Perspectives_on_Employee_Selection_Systems_in_press/links/56b9fe9008ae9d9ac67f4439.pdf

McCarthy, J., Bauer, T., Truxillo, D., Anderson, N., Costa, A. C., Ahmed, S. (2017). Applicant perspectives during selection: A literature review addressing “So What?”, “What's New?”, and “Where to Next?” Journal of Management43(6), 1693–1725. https://doi.org/10.1177/0149206316681846

Anderson, N., Ahmed, S., & Costa, A. C. (2012). Applicant reactions in Saudi Arabia: Organizational attractiveness and core-self evaluation. International Journal of Selection and Assessment, 20, 197-208. https://doi.org/10.1111/j.1468-2389.2012.00592.x

University roles and responsibilities

  • Programme Director BSc. Business Management (HRM Pathway)
  • Communications Manager, Future of Work Research Centre.

    Affiliations and memberships

    Member of the Chartered Institute of Personnel and Development (MCIPD)
    Fellow of the Higher Education Academy (FHEA)
    Member of the EUROPEAN NETWORK OF SELECTION RESEARCHERS (ENESER)
    Academic Members of CIPD (MCIPD)
    Sara is an Academic Member of The Chartered Institute of Personnel and Development (MCIPD)

    Publications

    Sara Ahmed, Steve Woods (2018)New Technology in Personnel Selection, In: Academy of Management Specialized Conference 2018. Global ProceedingsSurrey AOM

    Rapid changes in selection technologies have impacted inexorably the science and practice of personnel selection in recent years. Technological advances are allowing organizations to use new internet-based selection procedures (IBSPs) as well as big data and analytics to make empirical-based employment decisions by collecting and analyzing the digital footprints that job applicants leave behind them in social networks, social media and other internet platforms. The aim of the paper is to make a contribution to these important and emerging issues by reviewing the literature in this area and designing a study to examine applicant privacy and fairness reactions to 10 types of new selection procedures. It also aims to examine the application of these new selection and assessment practices in organizations across 10 countries.

    Hannah Mary Collis, Sara Ahmed H.Collis PhD Study 1 : COVID Daily Diary Data – Personality and Wellbeing, In: When the Job Changes You: Exploring Dynamic Relationships between Personality, Work, and the COVID-19 Pandemic University of Surrey

    This dataset contains data collected from April – June 2020 during the first national UK lockdown in response to COVID-19. The data was collected using a ‘daily diary’ format, with participants completing an initial baseline questionnaire, followed by short daily questionnaires for 14 consecutive days. The project was designed around the TESSERA framework of personality development (Wrzus, 2021). The sample consists of 192 participants, with 2,277 observations. Variables include demographics, the Big 5 personality traits, weekly expectancies/goals, personality state expressions, reactions/reflections, affective wellbeing, and stress. Items relating to job security and organisational support were only asked to participants who were employed during the data collection period. Only quantitative data is available. Access to this dataset may be granted upon reasonable request to Dr Hannah Collis (h.m.collis@exeter.ac.uk) or Prof Stephen A. Woods (s.a.woods@surrey.ac.uk).

    Hannah Mary Collis, Sara Ahmed H.Collis PhD Study 2 : 3 Month Longitudinal Teacher Work, Personality and Wellbeing Data, In: When the Job Changes You: Exploring Dynamic Relationships between Personality, Work, and the COVID-19 Pandemic University of Surrey

    This dataset contains multi-wave quantitative data collected between September – December 2020 from a sample of secondary level teachers (student ages 11-16). The dataset consists of an initial ‘baseline’ questionnaire, followed by 5 questionnaire waves issued in 2-week intervals from the end of September 2020. Variables include demographics, Big 5 personality traits, stressors, resources, burnout, mental wellbeing, work-related rumination, work-life balance, job satisfaction, commitment, and turnover intention. Sample consists of 127 participants and 441 observations. Only quantitative data is available. Access to this dataset may be granted upon reasonable request to Dr Hannah Collis (h.m.collis@exeter.ac.uk) or Prof Stephen A. Woods (s.a.woods@surrey.ac.uk).

    Sara Atito Ali Ahmed, Berrin Yanikoglu (2021)Relative Attribute Classification with Deep-RankSVM, In: Pattern Recognition. ICPR International Workshops and Challengespp. 659-671 Springer International Publishing

    Relative attributes indicate the strength of a particular attribute between image pairs. We introduce a deep Siamese network with rank SVM loss function, called Deep-RankSVM, that can decide which one of a pair of images has a stronger presence of a specific attribute. The network is trained in an end-to-end fashion to jointly learn the visual features and the ranking function. The trained network for an attribute can predict the relative strength of that attribute in novel images. We demonstrate the effectiveness of our approach against the state-of-the-art methods on four image benchmark datasets: LFW-10, PubFig, UTZap50K-2 and UTZap50K-lexi datasets. Deep-RankSVM surpasses state-of-art in terms of the average accuracy across attributes, on three of the four image benchmark datasets.

    Soran Badawi, Ari M. Saeed, Sara A. Ahmed, Peshraw Ahmed Abdalla, Diyari A. Hassan (2023)Kurdish News Dataset Headlines (KNDH) through multiclass classification, In: Data in brief48109120 Elsevier Inc

    The rapid growth of technology has massively increased the amount of text data. The data can be mined and utilized for numerous natural language processing (NLP) tasks, particularly text classification. The core part of text classification is collecting the data for predicting a good model. This paper collects Kurdish News Dataset Headlines (KNDH) for text classification. The dataset consists of 50000 news headlines which are equally distributed among five classes, with 10000 headlines for each class (Social, Sport, Health, Economic, and Technology). The percentage ratio of getting the channels of headlines is distinct, while the numbers of samples are equal for each category. There are 34 distinct channels that are used to collect the different headlines for each class, such as 8 channels for economics, 14 channels for health, 18 channels for science, 15 channels for social, and 5 channels for sport. The dataset is preprocessed using the Kurdish Language Processing Toolkit (KLPT) for tokenizing, spell-checking, stemming, and preprocessing.

    Sara Atito Ali Ahmed, Berrin Yanikoglu, Ozgu Goksu, Erchan Aptoula, Sara Ahmed (2020)Skin Lesion Classification With Deep CNN Ensembles, In: 2020 28TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU)pp. 1-4 IEEE

    Early detection of skin cancer is vital when treatment is most likely to be successful. However, diagnosis of skin lesions is a very challenging task due to the similarities between lesions in terms of appearance, location, color, and size. We present a deep learning method for skin lesion classification by fusing and fine-tuning three pre-trained deep learning architectures (Xception, Inception-ResNet-V2, and NasNetLarge) using training images provided by ISIC2019 organizers. Additionally, the outliers and the heavy class imbalance are addressed to further enhance the classification of the lesion. The experimental results show that the proposed framework obtained promising results that are comparable with the ISIC2019 challenge leader board.

    Mehmet Can Yavuz, Sara Atito Ali Ahmed, Mehmet Efe Kisaaga, Hasan Ocak, Berrin Yanikoglu, Sara Ahmed (2021)YFCC-CelebA Face Attributes Datasets, In: 2021 29th Signal Processing and Communications Applications Conference (SIU)pp. 1-4 IEEE

    The scales of the data accessible through internet search engines can reach hundreds of millions, or even billions. The existence of such large weak-labeled databases has gained importance in the training of face recognition algorithms. Starting with the publicly available YFCC100M, we propose a weaklylabeled subset for multi-label face recognition for self-supervised methods. A 392K image subset of YFCC100M of 128x128 images was obtained by querying for the 40 facial attributes. We made this dataset publicly available for other face recognition studies, by sharing the IDs, the links and the bounding boxes1. To reduce outliers with respect to CelebA, we apply the Elliptic Envelope algorithm, in the the latent feature space learned over CelebA, obtaining 353K face images. MixMatch algorithm is applied to this last set, to obtain pseudo labels. Pretraining with these pseudo-labels and final fine-tuning with CelebA brings an improvement of 0.4% points in the Area Under the ROC Curve (AUC) score over the system trained only with CelebA.

    N Anderson, Sara Ahmed, AC Costa (2012)Applicant Reactions in Saudi Arabia: Organizational attractiveness and core-self evaluation, In: International Journal of Selection and Assessment20(2)pp. 197-208 Springer

    This paper reports findings from a survey into applicant reactions of working adults in Saudi Arabia. A sample of 193 participants from four job functions was obtained, with measures of organizational attractiveness, core-self evaluation, and applicant reactions to four popular selection methods in the country – interviews, résumés, work sample tests, and references – being included. Findings indicate a notably similar pattern of preference reactions to previous studies in other (Western) countries, affirming arguments for so-called reaction generalizability. Work sample tests were rated the most favorably followed by interviews, résumés, and references. For specific procedural dimensions, résumés were perceived as the most favorable, followed by work sample tests, interviews, and references. Several significant differences were found across job functions, mostly for interviews and résumés. Significant effects were found between reactions and organizational attractiveness, and between reactions and core-self evaluation, including some interaction effects. Implications for future research and for practice in employee selection are considered in the conclusion.

    J McCarthy, T Bauer, D Truxillo, N Anderson, A Costa, Sara Ahmed (2017)Applicant perspectives during selection: A literature review addressing “So What?”, “What’s New?”, and “Where to Next?”, In: Journal of Management43(6)pp. 1693-1725 SAGE Publications

    We provide a comprehensive but critical review of research on applicant reactions to selection procedures published since 2000 (n = 145), when the last major review article on applicant reactions appeared in the Journal of Management. We start by addressing the main criticisms levied against the field to determine whether applicant reactions matter to individuals and employers (“So what?”). This is followed by a consideration of “What’s new?” by conducting a comprehensive and detailed review of applicant reaction research centered upon four areas of growth: expansion of the theoretical lens, incorporation of new technology in the selection arena, internationalization of applicant reactions research, and emerging boundary conditions. Our final section focuses on “Where to next?” and offers an updated and integrated conceptual model of applicant reactions, four key challenges, and eight specific future research questions. Our conclusion is that the field demonstrates stronger research designs, with studies incorporating greater control, broader constructs, and multiple time points. There is also solid evidence that applicant reactions have significant and meaningful effects on attitudes, intentions, and behaviors. At the same time, we identify some remaining gaps in the literature and a number of critical questions that remain to be explored, particularly in light of technological and societal changes.

    JM McCarthy, TN Bauer, DM Truxillo, NR Anderson, AC Costa, Sara Ahmed (2017)Applicant Perspectives During Selection A Review Addressing “So What?,” “What’s New?,” and “Where to Next?”, In: Journal of Management46(3)pp. 1693-1725 Sage Publications

    We provide a comprehensive but critical review of research on applicant reactions to selection procedures published since 2000 (n = 145), when the last major review article on applicant reactions appeared in the Journal of Management. We start by addressing the main criticisms levied against the field to determine whether applicant reactions matter to individuals and employers (“So what?”). This is followed by a consideration of “What’s new?” by conducting a comprehensive and detailed review of applicant reaction research centered upon four areas of growth: expansion of the theoretical lens, incorporation of new technology in the selection arena, internationalization of applicant reactions research, and emerging boundary conditions. Our final section focuses on “Where to next?” and offers an updated and integrated conceptual model of applicant reactions, four key challenges, and eight specific future research questions. Our conclusion is that the field demonstrates stronger research designs, with studies incorporating greater control, broader constructs, and multiple time points. There is also solid evidence that applicant reactions have significant and meaningful effects on attitudes, intentions, and behaviors. At the same time, we identify some remaining gaps in the literature and a number of critical questions that remain to be explored, particularly in light of technological and societal changes.

    Stephen A. Woods, Sara Ahmed, Ioannis Nikolaou, Ana Cristina Costa, Neil R. Anderson (2019)Personnel selection in the digital age: a review of validity and applicant reactions, and future research challenges, In: European Journal of Work and Organizational Psychologypp. 1-14 Taylor & Francis (Routledge)

    We present a targeted review of recent developments and advances in digital selection procedures (DSPs) with particular attention to advances in internet-based techniques. By reviewing the emergence of DSPs in selection research and practice, we highlight five main categories of methods (online applications, online psychometric testing, digital interviews, gamified assessment and social media). We discuss the evidence base for each of these DSP groups, focusing on construct and criterion validity, and applicant reactions to their use in organizations. Based on the findings of our review, we present a critique of the evidence base for DSPs in industrial, work and organizational psychology and set out an agenda for advancing research. We identify pressing gaps in our understanding of DSPs, and ten key questions to be answered. Given that DSPs are likely to depart further from traditional non-digital selection procedures in the future, a theme in this agenda is the need to establish a distinct and specific literature on DSPs, and to do so at a pace that reflects the speed of the underlying technological advancement. In concluding, we, therefore, issue a call to action for selection researchers in work and organizational psychology to commence a new and rigorous multidisciplinary programme of scientific study of DSPs.

    D Truxillo, T Bauer, J McCarthy, N Anderson, Sara Ahmed (2017)Applicant perspectives on employee selection systems, In: N Anderson, DS Ones, H K Sinangil, C Viswesvaran (eds.), The SAGE Handbook of Industrial, Work & Organizational Psychology, V1: Personnel Psychology and Employee Performance, Pt 4: Staffing, Decision Making and Training1pp. 508-532 SAGE Publications Ltd

    Additional publications