This scoping review examines the integration of artificial intelligence (AI) within organization development (OD), a field dedicated to enhancing organizational effectiveness through planned, human-centered change interventions. As AI reshapes industries by optimizing decision making, processes, and workflows, its potential to transform OD practices remains underexplored. This paper synthesizes findings from 49 studies published between 2019 and 2024, identifying key themes in AI’s application to leadership development, organizational structure, performance management, human resources (HR), and organizational culture. Whereas AI offers promising avenues to enhance efficiency, flexibility, and data-driven insights, the review highlights critical concerns, including ethical implications, privacy issues, and the potential erosion of essential human qualities in organizational culture. The study underscores the need for comprehensive governance frameworks and human oversight to address these challenges. Additionally, it calls for empirical, longitudinal research to assess AI’s long-term impact on organizational outcomes and employee engagement. This review contributes to OD literature by mapping AI’s transformative potential and outlining a future research agenda aimed at bridging the gap between technology and the human-centered foundations of OD, offering valuable insights for practitioners and scholars alike.
Individual performance at work is fundamental to the effective functioning of any organization. Due to its importance, it is one of the most relevant and researched constructs in people management, and organizational behavior studies. However, few studies are dedicated to analyzing the state of the art on the subject, especially in the post COVID-19 pandemic scenario, an event that has generated significant changes in the contemporary work context. Therefore, this paper presents a systematic review with bibliometric analysis of the literature on individual performance at work between 2016 and 2023, based on journals with a high impact factor. 145 articles published in journals with a JCR above 8 were reviewed. The findings confirm that individual performance at work is a criterion variable that is predominantly investigated using quantitative methods, and suggest opportunities to advance the theoretical definition of the construct, as well as the development of measurement instruments consistent with its multidimensionality.
In almost half a century, competency modeling has become an important movement in Human Resource and talent management. This study aims to identify the characteristics of the research published on competency/capability modeling using bibliometric analysis. To answer the research questions, 367 journal articles were analyzed. The results showed an increasing number of publications in the most recent two decades, and there is a worldwide collaboration in advancing this field while countries such as the United States and Australia are leading. Moreover, there are two competency/capability camps. One camp focuses on individual job performance, while the other focuses on organizational core competencies and capabilities. Capabilities often co-occur with the keywords related to competitive advantage and innovation. Behavioral event interviews and Delphi are among the common methods used to design competency models. Researchers and practitioners can benefit from the findings by gaining insights into the latest trends in this field.
A major setback for parent companies in creating value is managing subsidiaries, particularly in human resources management (HRM). Therefore, this paper intends to design a framework for strategic control of HRM in holdings (SCHRH) to ensure that HRM in subsidiaries is aligned with the implementation of corporate strategy. This paper’s methodology is an exploratory case study carried out in a mining holding, and analysis of the obtained data used qualitative content analysis. SCHRH framework is a managerial tool that facilitates and standardizes implementation of the corporate strategy by HRM in subsidiaries. Since managers often do not have the opportunity to deal with all the complications, this framework can draw their attention to the key and practical tips for successful strategy implementation via HRM.
This study seeks to understand the role of rater–ratee personality configurations in relational and employee outcomes. Specifically, it examines the effect of the interplay between rater–ratee honesty–humility (H-factor) on ratee feedback reactions via leader–member exchange (LMX). Data collected from N = 310 matched dyads were analyzed using polynomial regression. The findings indicated that rater–ratee H-factor congruence was more accurate in predicting LMX and ratee feedback reactions compared with H-factor incongruence. Congruence at both high and low levels of H-factor was found to affect LMX and ratee feedback reactions positively. Different magnitudes of incongruences exhibited negative impacts on LMX and ratee feedback reactions. LMX also mediated the relationship between rater–ratee H-factor (in)congruence and ratee feedback reactions. Rater–ratee personality configurations contribute to extraneous variance, affecting their relationship and ratee reactions to performance appraisals. This study highlights how different dyadic personality interactions influence relationship quality and reactions to performance appraisal feedback.
Whereas it is a valuable tool for instructional designers and performance improvement practitioners, needs assessment is often avoided due to perceived burdens associated with the process. Given the lack of study of perceived burden within the literature, there was no known existing scale to measure perceived burden. This article describes the process of conceptualizing perceived burden in needs assessment and developing the first scale to measure that construct: the Perceived Burden in Needs Assessment Participants Survey (PBNAPS). Through examining the performance of a pilot instrument, the authors explored the validity and reliability of the PBNAPS. The instrument was found to be reliable (α = .86) across four factors: (a) duties, obligations, and responsibilities; (b) cost; (c) needs assessment facilitator skills; and (d) needs assessment facilitator systemic sensitivities. Ultimately, the final revised PBNAPS instrument demonstrated both internal consistency and applicability across organizational contexts, constituent types, and lengths of affiliation.
This systematic literature review (SLR) examined the influence of ethical and reliable AI teammates on improving team performance in Human-AI teams (HAITs). The review synthesized 37 peer-reviewed papers to investigate how transparency, explainable AI (XAI), and ethics cultivate trust, an essential component for effective human-AI collaboration. Ethical AI teammates enhance team dynamics by mitigating uncertainty, guaranteeing equity, and fostering transparency in decision-making. Nonetheless, significant challenges exist in trusting AI teammates due to obstacles such as the “black box” nature of AI teammate representing the lack of transparency and trust violations. Trust restoration methods, such as explanations and trusting AI teammates with caution, are crucial for reinstating trust following breaches. The study concluded by highlighting the implications for enhancing team performance through ethical and trustworthy AI teammates, adding to the existing literature on human-AI collaboration.
This study aims to investigate whether individual interest and achievement-goal orientations facilitate learning and task performance. The effects of individual interest, achievement-goal orientation, and their interactions on rote learning, meaningful learning, and worthy performance were investigated. A hundred eighty-seven participants were grouped based on their individual interest levels and achievement-goal type toward the Critical Information Seeking and Reporting course. Participants’ initial goal orientations were fostered via experimental manipulations to create more distinctive achievement-goal groups. To obtain data regarding rote learning and meaningful learning, an achievement test and performance task were designed and developed respectively. Participants’ task performance score was divided by their cognitive effort score to calculate their worthy performance. The results indicate that a significant main effect of goal-orientation exists on rote learning, a significant interaction effect between individual interest and achievement goal-orientation exists on meaningful learning, significant main effects of both independent variables exist on worthy performance.
Social media platforms are extensively used in the present era and are now considered as an essential part of communication. Social media usage (SMU) within an organization distracts employees and may adversely affect their performance. However, SMU also significantly influences employees to collaborate and share information about their jobs as well as professional knowledge. This study examines the role of SMU on job performance, taking into consideration the influences of social capital and knowledge sharing. The proposed model has been empirically tested through a survey of 608 faculty from Indian public universities. The results highlight that social media usage has a significant influence on social capital, which further influence employees’ knowledge sharing and enhances job performance. Social media usage among employees plays an important role in the development of their social capital, which enables them to learn and build knowledge about jobs and, consequently, to perform tasks more effectively.
Several “Performance Improvement Quarterly” (PIQ) submissions have been rejected because of how survey instruments were developed, analyzed, or validated. In this Editorial, I hope to provide a quick guide for what is and is not accepted with survey instruments. This guide will focus on the central issues we have experienced with submissions to PIQ. However, these guidelines will not be a complete guide for conducting research using surveys or validating instruments.One of the main issues we have experienced with submissions to PIQ is that the constructs combined into a comprehensive survey are not supported by a theoretical foundation. I often ask, “Why are these constructs used and combined?” and “What theory puts these specific constructs together to justify a combined survey instrument?”. In several studies that we have reviewed, not only in PIQ but also in other peer-reviewed journals, constructs are measured without a theoretical foundation highlighting why these constructs are relevant to be measured. This is problematic and gives the perception that researchers are trying to apply a survey they developed to fit their narrative or bias.Without a theoretical foundation supporting a set of constructs being combined into a more extensive survey instrument, the study will be rejected. This importance of providing theoretical support for the constructs in a survey is highlighted by Bandalos and Finney (2010): when discussing rarer cases in which there is little theoretical support available (e.g., exploratory research, new fields of inquiry), “Some theory, however rudimentary, must have guided the selection of the variables and this theory should be explicated to the extent possible” (p. 95).Several submissions identified that the researchers developed a survey but provided no information on how the survey was developed. If a survey is developed as part of a research study, the development of the individual items (questions) and the factors representing the theoretical foundation must be provided.At a fundamental level, developing a scale must follow the following steps:When a new survey has been developed, the researchers should include the final survey items in the study’s appendix.Many submissions analyze surveys by calculating correlations and Cronbach’s alpha values between individual items (survey questions are instrument items). Basing the reliability of an instrument on correlations between items or on Cronbach’s alpha is incomplete. While these measures provide researchers with initial insight into an instrument, they represent only a precursor to the whole picture. An instrument cannot be reliable based only on the correlation matrix, variance matrix, or Cronbach’s alpha scores. Criticisms concerning Cronbach’s alpha as a measure of internal consistency are provided by DeVellis (2017):Unless a mixed-methods research study is being conducted, in which surveys and observations utilize triangulation techniques to support the different analyses (quantitative, qualitative), all measured surveys need to be subjected to either an exploratory factor analysis (EFA) or a confirmatory factor analysis (CFA).The main difference Between EFA and CFA is the instrument’s structure. When the instrument has not been tested in previous studies or when there is little evidence supporting the instrument, then an EFA is in order.An EFA is also called for in exploratory studies. In contrast, a CFA is in order when an instrument has plenty of support from several research studies using different samples: “CFA should only be used if the structure of the variables has been previously studied using EFA with an independent source of data” (Bandalos & Finney, 2010, p. 96). The instrument is ill-structured in the former case, requiring an EFA, whereas the instrument is structured in the latter case, calling for a CFA.One issue of concern is when the same data used for an EFA are used for the CFA. This is concerning and indicates that the researchers clearly do not understand what each is designed to perform. Two separate and independent samples should be used for an EFA and a CFA. One exception is if a large sample is collected. In this case, half of the data could be used for the EFA, and the second half could be used for the CFA. This requirement of independent samples is duplicated by Bandalos and Finney (2010) in the following: “Researchers should not conduct a CFA to ‘confirm’ the EFA solution using the same sample; this practice results in capitalization on chance due to fitting the idiosyncrasies of the sample data” (p. 106).Because EFA is more exploratory, inferences cannot be made. However, generalizations to the sample can be made. Additionally, it is possible to extend this generalization to other, similarly matching samples but not to different samples: “Results can only be generalized to samples similar to that on which the analyses have been conducted” (Bandalos & Finney, 2010, p. 97). This highlights the importance of reporting completely of whom your sample is comprised and how it is representative of the target population. Without presenting a representative sample, one that is representative of some larger population, generalizations cannot be made beyond the study sample.For CFA studies, the same guidelines for EFA still apply. However, one difference is that CFA is an inferential method in which the study’s power must be evaluated. Power must be “computed for both individual parameter estimates and for the model as a whole” (Bandalos & Finney, 2010, p. 107).When conducting a study involving a survey instrument, report how the instrument was generated. If developed as part of the study, the study should concentrate on following and reporting on EFA techniques, and this should be followed by appropriate CFA techniques, using a separate independent sample. While a CFA can be a complete study of its own to validate an instrument, an EFA typically is not comprehensive enough to be a standalone study. The exploratory portion with an EFA must be validated using CFA techniques. Although EFA and CFA are different in analysis and purpose, they often work hand-in-hand when validating survey instruments. Be transparent in reporting all steps to ensure a positive review when submitting your study, and follow previously published guidelines in doing so. For additional support, the references provided in this Editorial could provide a starting point for newer emerging scholars.One of the editors’ goals is the continued growth and advancement of the journal’s reach to various disciplines, industries, and markets. However, to accomplish this goal, the journal needs continued support from existing reviewers and the addition of new reviewers to the peer review team. If you are interested in participating in peer review for PIQ submissions, please create an account and sign up as a reviewer at https://mc.manuscriptcentral.com/piq.The submission of new research from the performance improvement communities that meet the minimum requirements, as highlighted in previous editorials in this journal (Turner, 2018a, 2018b, 2018c, 2019a, 2019b), are encouraged. If you are interested in having your manuscript considered for publication in PIQ, present your research study after reviewing the minimal requirements highlighted in the previously mentioned editorials as well as review the author guidelines at https://ispi.org/page/PIQuarterly.The editor and associate editors are here to help you with your publication. Do you have an idea for a research article and wonder if it is suitable for PIQ? Contact the editorial team for feedback. The editorial staff at PIQ works with submitting authors to move their articles toward publication. The editorial staff is active in the review process and continues to work with authors through rounds of revisions, if needed, to prepare their manuscripts for publication. If you have a performance improvement-related research article you would like to submit, please do so at https://mc.manuscriptcentral.com/piq. Be sure that the manuscript is related to performance improvement and meets the minimal guidelines presented in this and other editorials at PIQ.Peer review is necessary for a journal’s success and reputation. We thank our current reviewers for their time and dedication to PIQ. We need continual support from our reviewers to grow the number of active reviewers for the journal. As mentioned in previous editorials, additional reviewers must provide critical and informative reviews for manuscripts in the publication pipeline and future submissions. If you are interested in becoming a reviewer, please contact any member of the editorial team: John Turner (john.turner@unt.edu), Rose Baker (rose.baker@unt.edu), or Hamett Brown (hamett.brown@usm.edu).
Psychological abuse as a form of domestic violence against working women is prevalent but underreported almost all over the world. The present study was conducted to examine the relationship between domestic psychological abuse and burnout, and how psychological resilience mediates between them. One thousand married teachers from private secondary schools were selected through purposive sampling. Study results demonstrated that there is a relationship between domestic psychological abuse against working women and burnout, and that psychological resilience has a positive supportive effect in overcoming depersonalization among these women, yet the study also showed a lack of significant intervention in the relationship between psychological abuse and depersonalization of working women. This study confirms the absence of total or partial mediation to address psychological abuse and depersonalization of working women.
This study explored (a) the design of a public social system fulfilling its federally mandated purposes; and (b) the utility of visualization in the strategic planning of that system. First, a modified Delphi survey of 18 executive directors and board members of local workforce development areas was conducted. Second, from qualitative survey data collected, a textual definition of the system was developed and a visual model (systemigram) of an ideal local workforce development area was designed. Third, 12 participants viewed the systemigram and were asked about the utility of such a model for strategic planning purposes. Eighteen participants completed 3 rounds of the modified Delphi. Round 1 of the modified Delphi included a Kendall's W of 0.37. Rounds 2 and 3 resulted in a Kendall's W of 0.41. Results from interviews with participants indicated that overall, a visual model would be useful for strategic planning in the workforce development system.
Through their diverse composition and perspectives, interprofessional teams are able to deal with complex healthcare demands; however, effective collaboration remains a challenge. Feedback has been identified as a promising strategy by which to support effective team functioning through adjusting practice. To better understand the factors that optimize the impact of feedback on collaborative practice, a mixed-methods instrumental case study was conducted among an interprofessional primary healthcare team. Data were collected from 22 semi-structured interviews and participant observations of 26 team members during team meetings and individual activities. Through the lens of delivery, specificity, source, and timing, a constant comparison method was used to analyze transcripts and field notes. This study identified 33 themes influencing feedback acceptance or rejection while working toward shared goals. Also, 32 feedback characteristics emerged across these themes. The results provide insights into effective feedback strategies, thereby helping to yield the desired outcomes of successful interprofessional teamwork.
This study identifies how change management practitioners promote organizational change. It expands upon current research to describe contemporary approaches that practitioners use in the field. We interviewed 20 participants from change management organizations and academic departments to identify how they used different strategies to promote organizational change. We developed 14 themes consisting of actionable strategies. The themes focus on how organizational culture affects change in terms of communicating with employees about change, promoting change through leadership, using change management strategies contextually, measuring change through milestones, and implementing informal assessments. The results of this study suggest that (a) there are common strategies that practitioners often use, (b) practitioners use different strategies based on the context of the change, and (c) change managers use informal assessments to determine whether a change transitioned into organizational culture.
The role and competencies of the chief learning officer (CLO) have been evolving, demonstrating a need to identify them from research-based evidence. In this qualitative study based on the interviews of 12 chief learning officers, we address the different roles performed by CLOs and the essential professional competencies needed for them to perform the role of a CLO, as contextualized in organizations in the United States. We developed a Chief Learning Officer Competency Model that includes 6 competency domains, 13 roles, and 31 competencies for CLOs. The findings have implications for current CLOs and professionals aspiring to serve as CLOs. The results also have implications for education and leadership graduate and professional development programs that support learning and development and business professionals with the knowledge and skills needed to grow and function as a CLO.
