The use of ADKAR to instil change in the accessibility of university websites

Vázquez, Silvia Rodríguez

doi:10.1145/3493612.3520476

Cited by 3 publications

(8 citation statements)

References 10 publications

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“…Al-Alawi et al identified strengths in the desire for change but weaknesses in knowledge, inability, and performance enhancement [45]. Vázquez suggested the suitability of the ADKAR model for creating an effective change roadmap and enhancing university web accessibility [72]. Yasid and Laela demonstrated the efficacy of the ADKAR model, emphasizing the significance of awareness, desire, knowledge, ability, and reinforcement in facilitating successful transformation [73].…”

Section: Discussionmentioning

confidence: 99%

E-Learning Readiness Assessment Using Machine Learning Methods

Zine,

Harrou,

Terbeche

et al. 2023

Sustainability

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Assessing e-learning readiness is crucial for educational institutions to identify areas in their e-learning systems needing improvement and to develop strategies to enhance students’ readiness. This paper presents an effective approach for assessing e-learning readiness by combining the ADKAR model and machine learning-based feature importance identification methods. The motivation behind using machine learning approaches lies in their ability to capture nonlinearity in data and flexibility as data-driven models. This study surveyed faculty members and students in the Economics faculty at Tlemcen University, Algeria, to gather data based on the ADKAR model’s five dimensions: awareness, desire, knowledge, ability, and reinforcement. Correlation analysis revealed a significant relationship between all dimensions. Specifically, the pairwise correlation coefficients between readiness and awareness, desire, knowledge, ability, and reinforcement are 0.5233, 0.5983, 0.6374, 0.6645, and 0.3693, respectively. Two machine learning algorithms, random forest (RF) and decision tree (DT), were used to identify the most important ADKAR factors influencing e-learning readiness. In the results, ability and knowledge were consistently identified as the most significant factors, with scores of ability (0.565, 0.514) and knowledge (0.170, 0.251) using RF and DT algorithms, respectively. Additionally, SHapley Additive exPlanations (SHAP) values were used to explore further the impact of each variable on the final prediction, highlighting ability as the most influential factor. These findings suggest that universities should focus on enhancing students’ abilities and providing them with the necessary knowledge to increase their readiness for e-learning. This study provides valuable insights into the factors influencing university students’ e-learning readiness.

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Section: Discussionmentioning

confidence: 99%

E-Learning Readiness Assessment Using Machine Learning Methods

Zine,

Harrou,

Terbeche

et al. 2023

Sustainability

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“…The ADKAR model is a Prosci Methodology model that researchers have successfully applied in various fields, such as education, 17 business, 18 information technology, 13 and healthcare. 14 We selected ADKAR model for its reliability, validity, and alignment with our study aim.…”

Section: Methodsmentioning

confidence: 99%

“…The Prosci's ADKAR model has been widely employed across diverse industries such as education and information technology to evaluate and facilitate the implementation of new processes, technologies, and initiatives. 12 , 13 For instance, Adel et al. employed the ADKAR model to investigate barriers to change management in the public sector of educational institutions.…”

Section: Introductionmentioning

confidence: 99%

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The assessment of readiness to change among head managers of primary healthcare centers in Makkah, KSA

Arbaein,

Alharbi,

Alzhrani

et al. 2024

Journal of Taibah University Medical Sciences

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“…Nevertheless, websites are still of interest to researchers of HEIs and sustainability who have looked at a wide range of very specific environments, but without global studies like Vallez et al (2022), De Filippo et al (2020) and Moreno and Capriotti (2009) in Spain; Vázquez (2022) in Swiss universities; Fernández‐Vázquez (2021) in Latin America; Reisinger and Dános (2021) in Hungarian universities; Xiong and Li (2020) in China; Amey et al (2020) in Canadian universities; Son‐Turan and Lambrechts (2019) in Turkey; Hasim et al (2018) in Australia; Ptáčkova (2017) in Czech universities; Wimaladharma and Manjula Herath (2016) in Sri Lanka; Balaji and Kumar (2011) in India; and Gaiser et al (2007) in Germany.…”

Section: Introductionmentioning

confidence: 99%

University social responsibility and sustainability. How they work on the SDGS and how they communicate them on their websites

Carrillo‐Durán,

Blanco Sánchez,

García

2023

Higher Education Quarterly

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This paper shows how the leading Higher Education Institutions (HEIs) in the UI GreenMetric World University Ranking communicate their environmental sustainability policies through their websites. Specifically, this paper analyses the presence of sustainability‐related sustainable development goals on the websites of the top 100 universities in the UI GreenMetric World University Ranking (2021). In the area of university social responsibility, sustainability is less commonly communicated, hence the need to delve deeper into the presence of these messages, carrying out a descriptive study using content analysis as a form of data extraction. The conclusion is that neither position in the ranking nor geographic area are decisive for developing better communication about sustainability. In terms of content, sustainability features on the websites are in three key dimensions of the communication of HEIs worldwide (Climate change, Efficiency of sustainable cities and communities and Striving for peace and justice). Thus, the areas for improvement are around the positioning of this sustainability‐related content on their home pages, improving location and format (especially video). Moreover, reinforcing reputational messages, through plans or programmes, as well as improving engagement working on the multi‐directionality to other social media, is required.

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The use of ADKAR to instil change in the accessibility of university websites

Cited by 3 publications

References 10 publications

E-Learning Readiness Assessment Using Machine Learning Methods

E-Learning Readiness Assessment Using Machine Learning Methods

The assessment of readiness to change among head managers of primary healthcare centers in Makkah, KSA

University social responsibility and sustainability. How they work on the SDGS and how they communicate them on their websites

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