Performance Expectancy Research Articles

Nurses' Perspectives and Experiences of Using a Bed-Exit Information System in an Acute Hospital Setting: Mixed Methods Study.

Technology that detects early when a patient at risk of falling leaves the bed can support nurses in acute care hospitals. To develop a better understanding of nurses' perspectives and experiences with a bed-exit information system (BES) in an acute care hospital setting. BES was implemented on 3 wards of a university medical center. Nurses completed 2 online surveys at each time point (P0 and P1) and participated in focus groups before (P0) and after (P1) implementation. Additional patient data were collected. Descriptive statistics summarized the survey results, while content analysis was applied to focus group data. Patient rates and adverse events in both phases were compared using negative binomial models. Reporting of this study adhered to the GRAMMS checklist. A total of 30 questionnaires were completed at P0 (30/72, 42%) and 24 at P1 (24/71, 33%). Of the participants, 15 completed both questionnaires (complete cases). At P1, 64% (9/14) of participants agreed that their perceived workload and strain in caring for patients with cognitive impairment was reduced by the use of the BES. The adverse event rate per patient per day was reduced by a factor of 0.61 (95% CI 0.393-0.955; P=.03). In addition, 11 nurses participated in 4 focus groups before and after the intervention. Participants found it challenging to operationalize the use of the BES due to the heterogeneity of care settings, but certain behaviors of patients with cognitive impairment were recognized as indicating a need for intervention. Negative experiences included information overload and alarm fatigue, leading to occasional removal of the system. While BES provides some support in managing patients with cognitive impairment, its impact remains limited to specific scenarios and does not significantly reduce nurses' workload or strain. Our findings highlight the need to manage expectations of BES performance to ensure alignment between expected and actual benefits. To improve BES effectiveness and long-term implementation, future research should consider both objective measures of patient care and subjective factors such as nurse experience, structural conditions, and technical specifications. Improving information mechanisms within call systems could help reduce alarm fatigue and increase perceived usefulness. Overall, successful integration of BES in acute care settings will require close collaboration with nursing staff to drive meaningful healthcare innovation and ensure that the technology meets the needs of both patients and nurses. German Register for Clinical Studies DRKS00021720; https://drks.de/search/de/trial/DRKS00021720.

Industry 5.0: Are We Going to Accept Robots as Co-Workers in Office Environments? An Empirical Analysis

This research aims to assess the readiness of professionals working in offices to accept robots as co-workers, and to provide insight for robot developers and organizations in promoting robot acceptance. This study investigates the acceptance of robots in office environments using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, extended with a specific focus on perceived sociability. A two-country comparative approach was employed. The research involved participants from the United Kingdom and Turkey to explore differences on robot acceptance. Data were collected via a structured questionnaire with demographics, robot usage or intention to use, and robot appearance preferences, targeting working professionals in office environments. The findings highlight key factors influencing behavioral intentions to use robots, including performance expectancy, effort expectancy, social influence, and perceived sociability. Our research results indicate that robots will likely to be accepted in our future office work environments. The results provide actionable insights for designing socially interactive robots and utilizing them in diverse workplace environments. Future research directions include expanding the cultural scope and utilizing qualitative methods for the additional investigation of factors that may enhance our understanding of robot acceptance.

Factors influencing academic staff satisfaction and continuous usage of generative artificial intelligence (GenAI) in higher education

Generative Artificial Intelligence (GenAI) tools hold significant promises for enhancing teaching and learning outcomes in higher education. However, continues usage behavior and satisfaction of educators with GenAI systems are still less explored. Therefore, this study aims to identify factors influencing academic staff satisfaction and continuous GenAI usage in higher education, employing a survey method and analyzing data using Partial Least Squares Structural Equation Modeling (PLS-SEM). This research utilized the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Expectation Confirmation Model (ECM) as its theoretical foundations, while also integrating ethical concerns as a significant factor. Data was collected from a sample of 127 university academic staff through an online survey questionnaire. The study found a positive correlation between effort expectancy, ethical consideration, expectation confirmation, and academic staff satisfaction. However, performance expectancy did not show a positive correlation with satisfaction. Performance expectancy was positively related to the intention to use GenAI tools, while academic staff satisfaction positively influenced the intention to use GenAI. The social influence did not correlate positively with the use of GenAI. Security and privacy were positively associated with staff satisfaction. Facilitation conditions also positively influenced the intention to use GenAI. The findings of this study provide valuable insights for academia and policymakers, guiding the responsible integration of GenAI tools in education while emphasizing factors for policy considerations and developers of GenAI tools.

Use intention and behavior in e-government among rural women: An extension of the UTAUT-2 model

The promotion of e-government is a vital initiative for enhancing public service delivery, improving administrative efficiency, and advancing the modernization of governance. However, the factors influencing rural women's intention and behavior in using e-government services remain insufficiently understood. This study extends the UTAUT-2 by incorporating perceived security and digital literacy as additional variables. A mixed-methods approach, combining structural equation modeling (SEM) and artificial neural networks (ANN), was employed to identify the key determinants of rural women's intention to engage with e-government services. The results indicate that performance expectancy, effort expectancy, social influence, facilitating conditions, habit, perceived security, and digital literacy significantly affect rural women's intention to use e-government services. The extended UTAUT-2 model explains 78.6% of the variance in their behavioral intention. Furthermore, a strong positive relationship was found between e-government usage intention and actual behavior, with education level also showing a significant positive impact on e-government usage behavior.

K-12 Teachers’ Acceptance and Resistance Perceptions of Learning Analytics Adoption: A Mixed-Methods Approach

Abstract Learning analytics (LA) is an educational innovation that enhances teaching practices and facilitates student learning. However, the degree of LA adoption across schools remains limited, and teachers who adopt LA do not engage with it consistently. Based on the unified theory of acceptance and use of technology (UTAUT) as a framework, we conducted a perception survey and five focus group interviews with K-12 teachers, aiming to identify their expectations, needs, and perceptions of LA adoption. This study focuses on aspects related to LA’s technology acceptance in school education. The research question is, “Using the UTAUT framework as a theoretical perspective, which factors explain the adoption of LA in schools? How do teachers describe their motivation and readiness for LA adoption?” The findings indicated that the factors facilitating LA adoption were performance expectancy, social influence, and feelings. Furthermore, the factors inhibiting the adoption of LA were effort expectancy, self-efficacy, facilitating conditions, and culture change. The implications and potential future research for adopting LA in schools are discussed.

Understanding pre-service teachers' intention to adopt and use artificial intelligence in Nigerian inclusive classrooms

IntroductionThis study applied the Unified Theory of Acceptance and Use of Technology (UTAUT) to provide an understanding of the behavioral intentions of pre-service teachers in the adoption and utilization of artificial intelligence (AI) tools for educational engagement in the inclusive classroom.MethodsThe cross-sectional study collected data through a validated questionnaire from 411 pre-service teachers were analyzed with descriptive statistics such as frequency counts and simple percentage calculation, as well as inferential statistics which involved correlational analysis and Structural Equation Modeling (SEM).ResultsThe study established that effort expectancy had a positive and direct significant contribution to the perceived behavioral intention of pre-service teachers to adopt and use AI for inclusive education teaching. Technological self-efficacy had no direct contributory effect on these teachers' behavioral intention to adopt and use AI for inclusive education teaching. Technological self-efficacy did, however, have a significant positive and indirect contribution to the effect of performance expectancy and social influence on the pre-service teachers' behavioral intention to adopt and use AI for inclusive education teaching, based on their technological self-efficacy.DiscussionThe implication of findings of this study points to the exigency of a need to strengthen institutional policies and teacher preparation curricula in a manner that would advance the infusion of the use of artificial intelligence for teaching of learners with special needs.

Factors Affecting Teachers’ Usage of Augmented Reality in Arabic Language to Non-Native Speakers Classroom

Objectives: Augmented reality is changing the service sectors, but there is paucity of research on its adoption in the education sector especially for Arabic language to non-native speakers institutes. Moreover, the adoption of augmented reality by teachers teaching Arabic to non-native speakers can help in improving the language learning. Therefore, this research aimed to examine the factors that affect the behavior intention of teachers towards augmented reality and actual use behavior of augmented reality. It could be the most appropriate model to predict technology adoption. Methods: The questionnaire was designed for data collection, and measurements were adapted from previous valid studies. The data were gathered from 63 Arabic language teachers, and Smart pls was used for analysis. Results: The results highlighted that among all elements of UTAUT including social influence (SI), effort expectancy, facilitating condition, performance expectancy, only social influence and FC influence the behavioral intention of teachers towards augmented reality and this behavioral intention leads to actual use behavior of augmented reality by teachers teaching Arabic to non-native speakers. Conclusions: The study has focused on UTAUT framework to investigate the factors affecting adoption of augmented reality among teachers teaching Arabic to non-native speakers. Teachers’ behavioral intention and usage of augmented reality in classrooms can be influenced by social influence and facilitating conditions. This research can help educational policymakers to emphasize social influence and FC to enhance behavioral intention and actual use behavior of augmented reality.

Health workers' adoption of digital health technology in low- and middle-income countries: a systematic review and meta-analysis.

To conduct a systematic review and meta-analysis of the facilitators of and barriers to the acceptance and use of digital health technology by health workers in low- and middle-income countries. We searched several databases for relevant articles published until 25 April 2024. We extracted data on four unified theories of acceptance and use of technology factors (performance expectancy, effort expectancy, social influence and facilitating conditions) and six additional factors (attitude, habit, incentive, risk, trust and self-efficacy); how these affected the outcomes of behavioural intention and actual use; and the strength of association if reported. We conducted a meta-analysis of the quantitative studies. We reviewed 36 publications, 20 of which were included in our meta-analysis. We observed that performance expectancy was the most frequently reported facilitator (in 21 studies; 58.3%) and that lack of facilitating conditions was the most cited barrier (10; 27.8%). From our meta-analysis, trust (r = 0.53; 95% confidence interval, CI: 0.18 to 0.76) and facilitating conditions (r = 0.42; 95% CI: 0.27 to 0.55) were the leading facilitators of behavioural intention and actual use, respectively. We identified concerns with performance expectancy (r = -0.14, 95% CI: -0.29 to 0.01) as the primary barrier to both outcomes. Our approach of clustering the facilitators of and barriers to the acceptance and use of digital health technology from the perspective of health workers highlighted the importance of creating an enabling ecosystem. Supportive infrastructure, tailored training programmes and incentive policies should be incorporated in the implementation of digital health programmes in low- and middle-income countries.

AI in the Classroom: Analysing ChatGPT Usage Among School and College Students

This study explores the usage of ChatGPT among school and college students through the lens of the Unified Theory of Acceptance and Use of Technology (UTAUT) model, with an emphasis on age group as an additional moderating factor. A total sample of 396 students was collected through structured questionnaires. The UTAUT model’s key constructs are Performance expectancy, Effort expectancy, social influence, and Facilitating conditions these were examined to determine their impact on students’ behavioural intention to use ChatGPT. Age was included as a moderating variable to assess on the relationship between these constructs and intention to use ChatGPT. The study highlights the importance of age in understanding technology adoption among students, providing valuable insights for educators and policymakers aiming to integrate AI-driven tools like ChatGPT into educational settings effectively. Key-words: School and College Students, ChatGPT, UTAUT model

The mediating role of academic stress, critical thinking and performance expectations in the influence of academic self-efficacy on AI dependence: case study in college students.

The mediating role of academic stress, critical thinking and performance expectations in the influence of academic self-efficacy on AI dependence: case study in college students.

Digital Health Transformation in Saudi Arabia: Examining the Impact of Health Information Seeking on M-Health Adoption during the COVID-19 Pandemic

This study investigates the intention of Saudi Arabian users to adopt mobile health (m-health) applications through the lens of the United Theory of Acceptance and Use of Technology (UTAUT) framework. The research highlights the growing importance of m-health solutions in Saudi Arabia, especially in the context of the country's Vision 2030 development agenda and the accelerated adoption of e-health/m-health technologies during the COVID-19 pandemic. The key findings indicate that health information seeking, and social influence are significant factors driving users' intentions to adopt m-health applications, while Performance Expectancy (PE) is not a primary driver. Additionally, Effort Expectancy (EE) positively influences users' behavioral intentions. Improving health information features in these applications could facilitate broader adoption. This research contributes to the existing literature on information systems and m-health adoption by shedding light on the critical factors influencing user intentions in a developing context. However, the study does not account for all potential technological and external variables that could affect adoption behavior. Future research, particularly qualitative or mixed-method studies, should explore the impact of age on m-health adoption, as the current findings primarily reflect younger users.

Mobile Applications for Longitudinal Data Collection: Web-based Survey Study of Former Intensive Care Patients

Purpose: Mobile health plays an important role in providing individualized information about the health status of patients. Limited information exists on intensive care unit (ICU) patients with the risk of suffering from the post-intensive care syndrome (PICS), summarizing long-term physical, mental and cognitive impairment. This web-based survey study aims to identify specific needs of former ICU patients for utilizing a newly developed, so called Post-Intensive Care Outcome Surveillance (PICOS) app to collect relevant PICS-related parameters. Methods: A prototype app was developed following interaction principles for interactive systems of usability engineering. Patients from four different German hospitals were asked about demographics, interaction with technology and their perception of the prototype regarding hedonic motivation, perceived ease of use and performance expectancy. Results: 123 patients participated in the survey; the majority owned and used smartphones. Nearly half of respondents would seek help from family members or caregivers using the app. There was a difference in affinity for technology for participants who own a smartphone and those who do not, t(116) = − 0.97, p = .335, and no significant difference in affinity for technology whether the participants would like support when using the app or not, t(97) = 1.81, p = .073. The average hedonic motivation for using the app was M = 4.44 (SD = 1.304). Conclusion: This app prototype was perceived as both beneficial and easy to use, indicating its success among former ICU patients. Due to aging and ongoing health impairments, every second patient would need assistance with the initial use of the app.

Impacto de la Educación Formal y Migración en el Desempeño de las Empresas Familiares: Un Caso en Jerez, Zacatecas

This research is the result of observing the performance phenomenon of family businesses (FBs) in relation to the formal education level of participating members of their administration, aiming to establish a relationship between both variables. This study was conducted through the design of a survey as a measurement instrument, with a reliability of α = 0.741, applied to a sample of n = 280 companies, using statistical hypothesis tests: 1) One-way ANOVA, 2) Student's t-test for a single sample, and 3) Hypothesis testing with two samples for verification. It was found that companies whose average educational level is high do not necessarily have better performance expectations in a Mexican urban area with low economic growth, due to the phenomenon of migration at an early age. It is important to emphasize that the data collected for the purpose of this research were obtained in the locality of Jerez, in the state of Zacatecas; therefore, its results can only be generalized to locations with similar social, economic, and demographic conditions.

Expectancy-disconfirmation and consumer satisfaction: A meta-analysis

Abstract Expectancy-disconfirmation has been the dominant paradigm to explain the formation of consumer satisfaction for over 40 years. Within this paradigm, it is possible for expectations to have opposing effects on consumer satisfaction depending on the underlying psychological processes presupposed. In general, assimilation processes predict positive effects, while contrast processes predict negative effects. A comprehensive assessment of the empirical evidence for these positions is missing. Hence, we provide a meta-analysis of expectancy-disconfirmation research, using 150 records (N = 58,597), to test the direct effects of perceived performance and performance expectations on consumer satisfaction, while also including disconfirmation as a mediator in each path (using meta-analytical path analysis). We found evidence for an overall positive relationship between expectations and consumer satisfaction (r = .29 [0.24, 0.34]) and no evidence supporting contrast effects. Moderator analyses revealed that the positive correlation between performance expectations and consumer satisfaction was significantly stronger for predictive (vs. normative) expectations, for services (vs. goods), and for cross-sectional (vs. longitudinal and experimental) studies. Furthermore, we found an unexpected downward publication bias, which suggests that the true correlation between disconfirmation and consumer satisfaction is higher than the (already high) estimate we found. We discuss how future research can empirically scrutinize popular practitioner views and promote the development of causal explanations, account for non-linear effects, and elucidate the anomalous publication bias found here.

Intention to use eLearning-based continuing professional development and its predictors among healthcare professionals in Amhara region referral hospitals, Ethiopia, 2023: using modified UTAUT-2 model

BackgroundContinuing Professional Development (CPD) is provided through in-service programs organized based on competency development and lifelong learning for healthcare professionals to stay fit with the required knowledge and skills. However, healthcare professionals’ financial constraints and tight schedules sending them away from the workplace for CPD training is a challenge. eLearning is becoming the best solution to overcome those barriers and create accessible, efficient, flexible, and convenient professional development. However, evidence is limited on health professionals’ intention to use eLearning-based CPD and its predictors. Hence this study aimed to assess healthcare professionals’ intention to use eLearning-based CPD and its predictors in Amhara region referral hospitals, in Ethiopia.MethodsA cross-sectional study was conducted from March 28 to April 28, 2023. A total of 976 healthcare professionals participated in this study. Proportional allocation and simple random sampling were used to select participants for the study. UTAUT2 model was applied to develop a theoretical framework. A structured self-administered questionnaire was used and a 5% pretest was performed. Data were entered into Epi data 4.6 and exported to SPSS 26 for descriptive analysis. AMOS 23 SEM was also used to describe and assess the degree and significance of relationships between variables.ResultsAbout 51.8% (506/976) (95% CI: 48.7–55.0) of participants have the intention to use eLearning for CPD. Performance expectancy (β = 0.233, p-value < 0.01), effort expectancy (β = 0.082, p-value < 0.05), facilitating condition (β = 0.102, p-value < 0.05), hedonic motivation (β = 0.199, P-value < 0.001), habit (β = 0.473, P-value < 0.001), and computer literacy (β = 0.116, p-value < 0.001) had a positive relationship with intention to use eLearning based CPD. Age and gender were also a moderator of the habit of using eLearning based on CPD.Conclusionand recommendation.Overall, healthcare professionals' intention to use eLearning-based CPD was found low. Performance expectancy, effort expectancy, facilitating condition, hedonic motivation, habit, and computer literacy had a significantly positive influence on the intention to use eLearning-based CPD. The development of a user-friendly eLearning-based CPD development that meets user expectations can enhance the intention to use.

Determinants of M-Commerce Platform Adoption Among Individuals in South African Township Communities

Since the COVID-19 pandemic outbreak in 2020, there has been an increase in the adoption of e-commerce (electronic commerce) from which m-commerce (mobile commerce) was born. M-commerce is the use of a mobile phone to purchase goods and services. Most of the South African population resides in townships and rural areas and contributes significantly to the economy. The study researched m-commerce adoption in South African townships by determining the factors that affect m-commerce adoption in South African townships. The study only focused on two townships in Johannesburg: Soweto and Tembisa. A conceptual research model based on the following factors from the UTAUT2 technology model: performance expectancy, hedonic motivation, and facilitating conditions, with social media and trust as two additional constructs. A digital online survey was used to collect respondents’ data, and it was analysed quantitatively using SPSS. The findings of the study are discussed in detail, including testing the hypotheses formulated. Social media and perceived security have a significant, positive effect on the trust of South African township residents to adopt m-commerce platforms. Performance expectancy, hedonic motivation, trust, and facilitating conditions positively affect the intention of South African township residents to adopt m-commerce; however, trust and facilitating conditions were insignificant.

Why do rural and urban users adopt phygital payments differently? A UTAUT3-based comparative study of mobile wallets and plastic money

Purpose This paper aims to identify the factors affecting the adoption of plastic money and mobile wallets by comparing rural and urban perspectives. For this, the study uses a unified theory of acceptance and use of technology (UTAUT3) model with additional variables, perceived value and perceived risk, to predict use behavior. Design/methodology/approach This research incorporates two cross-sectional surveys, Study R, which includes 417 rural respondents and Study U, which includes 431 urban respondents, regarding plastic money and mobile wallet adoption. This paper used the Statistical Package for Social Science and partial least squares-structural equation modeling for data analysis. Findings Both studies showed that performance expectancy, social influence and hedonic motivation substantially influence behavioral intention and use behavior. In contrast, effort expectancy has an insignificant influence in rural (Study R) and urban (Study U). In addition, personal innovativeness and perceived value positively influence, whereas perceived risk negatively influences behavioral intention and use behavior in both studies. However, facilitating conditions had a positive effect in Study U, but it had a negative effect on behavioral intention and use behavior in Study R. Practical implications This research lays a practical foundation for governments, policymakers and marketers to encourage a phygital payment service that explicitly addresses the rural and urban context. In addition, the findings of this paper also help regulatory authorities develop effective strategies and campaigns to encourage the sustainable development of countries. Originality/value This paper attempts to address the gap in the prevailing literature by investigating the role of geographical differences in the technology adoption system, especially in emerging nations like India, where these studies are missing. The adoption differences between rural and urban areas, along with the insightful findings by the authors, help to highlight the unique aspects of the context. As one of the pioneering studies, this research tests the UTAUT3 model, incorporating two additional constructs, to provide a comprehensive framework for using plastic money and mobile wallets – valuable for both researchers and practitioners.

The Relevance of Trust in the Implementation of AI-Driven Clinical Decision Support Systems by Healthcare Professionals: An Extended UTAUT Model

Background. In the healthcare sector clinical decision support powered by artificial intelligence is rapidly expanding. It can help medical personnel make better-informed decisions while saving time. The opposition of healthcare professionals to AI initiatives in the healthcare industry might prove to be a significant barrier, due to the combination of optimism and fear related to the technology. Professionals in the sector may be apprehensive about AI due to job security worries and the general public's lack of faith in the technology. Objective. A primary objective of this study is to examine how trust influences healthcare professionals' willingness to use AI-driven CDSS, particularly at well-known tertiary hospitals in Delhi-NCR region India, as well as to expand the Unified theory of acceptance and use of technology (UTAUT) by including constructs such as perceived risk and self-efficacy. Method. The UTAUT model provided the basis for the construction of a new model, which was developed by integrating the variables of perceived risk and self-efficacy. The model had eight components which were assessed using 31 survey questions. Two hundred and twenty participants filled out the surveys for the study. Results. The findings indicate that there are significant relationships between (PE) and (AI), (EE) and(AI), (PR) and (AI) and (SE) and (AI). This study reveals that trust fully mediates the relationship between AI which means that fostering trust in AI technology is essential for the successful adoption and implementation of AI-CDSS technology. There was no significant relationship between social influence and adoption of AI-powered CDSS nor between intention to adopt and actual use. Conclusion. The result of this study shed light on the factors influencing healthcare professionals to adopt AI-CDSS. The outcomes of this study provide insight into how individuals' performance expectancy, effort expectancy, social influence, perceived risk, and self-efficacy determine whether they embrace AI-CDSS. This study highlights the importance of trust in the deployment of these technologies in the healthcare sector. Furthermore, it highlights the significance of addressing perceived risk and self-efficacy. Although social influence plays a crucial role in technology adoption its impact may be limited in the case of AI-CDSS deployment in India. This could be due to the complexities of AI-CDSS implementation, the requirement for training, or the fact that healthcare personnel are unaware of the benefits of AI-CDSS or have limited exposure to it.

Students’ adoption of educational technology platforms for nonformal education in Indonesia

This study examines the adoption of educational technology (EdTech) platforms for nonformal education in Indonesia, focusing on students from kindergarten to high school, using an extended UTAUT2 model. Data were collected via a questionnaire from 279 students and analyzed using PLS-SEM. A key addition to this research is the investigation of the new national curriculum in Indonesia, i.e., Kurikulum Merdeka (Independent Curriculum) and its influence on students’ behavioral intention to use EdTech platforms. The findings reveal that performance expectancy, Independent Curriculum, and habit shape behavioral intention, while facilitating conditions, habit, and behavioral intention significantly impact use behavior. This study fills a crucial research gap by analyzing EdTech adoption in younger students, offering valuable insights for EdTech companies to expand market reach and improve retention, particularly for elementary and middle school students. Additionally, it extends the UTAUT2 model with a new construct and provides sociocultural insights for policymakers to support digital learning. These contributions are vital for shaping the future of EdTech as a nonformal education platform in Indonesia.

Leaving the Institution: Academic Women and Retirement

ABSTRACT Only relatively recently have significant numbers of women been able to have academic careers and develop a sense of academic identity that encompasses scholarly contributions to their discipline. The literature has traced the progress and accomplishments of academic women and has now begun to consider the transition to retirement. Arguably, the institution that shaped academic identity is powerful in the decision to retire and permeates life in retirement. We explore this affective relationship through oral histories with 32 academic women, focusing on the decision to retire and how women narrate the academic self. The decision to retire was multi-faceted reflecting personal circumstances, such as health or caring responsibilities, but inseparable from institutional contexts that were often negative. Women often spoke of relentless demands of their institutions and their tiredness. Many remained critical of their institution after retiring while continuing unpaid academic activity, particularly research. While retirement was usually a continuation of academic identity, this was sometimes rejected for new endeavours. Leaving behind performative expectations, in retirement many women academics found greater space for pleasure and fulfilment.