Individual and Organisational Factors Affecting Knowledge Workers’ Perceptions of the Effectiveness of Informal Learning: a Multilevel Analysis

Lee, Jae Young; Yoo, Sangok; Lee, Yunsoo; Park, Sunyoung; Yoon, Seung Won

doi:10.1007/s12186-019-09218-z

Cited by 11 publications

(11 citation statements)

References 80 publications

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“…These dimensions of learning activities are largely compatible with other models of work‐related learning (e.g., Eraut, ; Estabrooks et al, ; Froehlich, Beausaert, & Segers, ; Lee et al, ; Pool, Poell, Berings, & ten Cate, ). They all emphasize the experience that individuals obtain through direct engagement in work tasks and the learning opportunities that arise through social interactions with colleagues or other work‐related stakeholders as well as the way that learners might use codified material like books or journals to acquire more conceptual knowledge.…”

Section: Internships As Organized Workplace Learningsupporting

confidence: 83%

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Engineering students' learning during internships: Exploring the explanatory power of the job demands‐control‐support model

Göller

Harteis

Gijbels

et al. 2020

J of Engineering Edu

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Background Internships are highly relevant learning experiences for engineering students. However, such practice‐based learning settings must be carefully designed to allow their learning potential to unfold. Purpose Exploring the job demands‐control‐support (JDCS) model, this study aimed to investigate how job demands, job control, and social support affect interns' use of different learning strategies. Method The study utilized data collected from a sample of engineering students (n = 118) who completed a required internship during their degree. The data were analyzed using a structural equation modeling approach including latent factors. Results Job demands were found to be a relevant and significant driver of students' shared regulation (e.g., asking for feedback), self‐regulation (e.g., self‐directed use of codified information), efforts to relate theory to practice (e.g., connecting workplace experience with theoretical knowledge), and the absence of avoidance of learning (e.g., lack of adaptation to work situations), but not of external regulation (e.g., asking for help to solve problems). Job control, however, was not found to be a positive or significant driver of student learning. Social support was found to be a relevant and significant predictor of external and shared regulation but not of the other learning activities. Conclusions This study provided mixed findings regarding key hypotheses central to the JDCS model, generating rather poor evidence supporting it. In fact, job demands, job control, and social support exhibited much less explanatory power than expected for engineering students' learning during internships.

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Section: Internships As Organized Workplace Learningsupporting

confidence: 83%

“…Such positive effects of internships, however, should not be taken for granted. Instead, the material and social environment at work needs to actively support them (Billett, 2001(Billett, , 2011Lee, Yoo, Lee, Park, & Yoon, 2019).…”

mentioning

confidence: 99%

Engineering students' learning during internships: Exploring the explanatory power of the job demands‐control‐support model

Göller

Harteis

Gijbels

et al. 2020

J of Engineering Edu

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“…However, these authors used the term elaborate learning to designate readiness to learn. Other recent studies have also indicated that informal learning is positively related to learning goal orientation (Noe et al , 2017) and motivation (Lee et al , 2019).…”

Section: Discussionmentioning

confidence: 94%

Informal learning behaviors, interaction and workplace autonomy and readiness to learn

Barbosa

Borges–Andrade

2021

JWL

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Purpose This paper aims to find a measurement model with better evidence of validity, with data extracted from the Program for the International Assessment of Adult Competencies (PIAAC). To test a parsimonious model in which dispositional and workplace context characteristics are predictors of informal learning behaviors (ILBs). Design/methodology/approach The authors performed exploratory and confirmatory factor analyses to improve the fit of the PIAAC data measurement model. Multiple linear regression was used to examine the prediction of ILBs by one dispositional variable (Readiness to Learn) and two workplace context variables (Autonomy and Interaction in the Workplace). Findings A measurement model emerged with 18 items divided into four factors. The three antecedent variables predicted ILBs. Interaction in the workplace resulted in higher scores, and workplace autonomy resulted in lower scores. Research limitations/implications The small number of items for ILBs prevented a more detailed exploration of predictors of different types of these behaviors. ILBs can be stimulated by policies that promote readiness to learn and that encourage the design of environments that require worker interactions and autonomy. Originality/value Few studies on ILBs in the workplace have investigated the prediction of dispositional and contextual antecedents based on a theoretical model. The findings herein were obtained using a diverse sample of countries, occupations and generations, allowing better generalization. The importance of interpersonal relationships in the workplace for predicting ILBs was emphasized.

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“…In this study, firm adaptability is measured by the ability of the partners to learn from each other and other events as they unfold and the level of flexibility exhibited by a partner in uncertain situations. Being adaptive requires that firms learn new ways of doing things (Lee et al, 2019). Adaptability has been found to have an impact on the coordination practices (Mutebi et al, 2020) and also flexibility (de Leeuw et al, 2013).…”

Section: Firm Adaptability and Supply Chain Integrationmentioning

confidence: 99%

Asset specificity, relational governance, firm adaptability and supply chain integration

Farida,

Joseph,

Sheila

et al. 2023

MSCRA

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PurposeThis study investigates how asset specificity, relational governance and firm adaptability relate with supply chain integration (SCI), considering selected food processing firms (FPFs) in Uganda.Design/methodology/approachThis study applies a quantitative research methodology. This research draws on a sample of 103 FPFs that have been selected from a population of 345 FPFs located in Kampala district. Hypothesis testing was done using Smart PLS version 3.FindingsAsset specificity has a significant positive relationship with SCI, and firm adaptability partially mediates this relationship. Also, there is a full mediation impact of firm adaptability on the relationship between relational governance and SCI.Research limitations/implicationsThis study focused on perceptual measures to get responses from managers on the level of integration with key suppliers and customers, yet firms deal with a number of suppliers and customers.Originality/valueThis study contributes to existing literature on SCI by applying the transaction cost theory. The study focuses on the influence of asset specificity, relational governance and firm adaptability on SCI in the food processing sector. Literature on relational governance in supply chain using the transaction cost theory remains scanty. Few studies have also focused on firm adaptability as a mediator in the FPS with specific focus on Uganda, yet the sector is highly faced with uncertain events. The uncertain events in the sector and in developing countries call for adaptive strategies. Additionally, this study is the first to use firm adaptability to mediate the influence of asset specificity and relational governance on SCI more so in a developing country like Uganda where the FPS is one of the most important in the economy.

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Individual and Organisational Factors Affecting Knowledge Workers’ Perceptions of the Effectiveness of Informal Learning: a Multilevel Analysis

Cited by 11 publications

References 80 publications

Engineering students' learning during internships: Exploring the explanatory power of the job demands‐control‐support model

Engineering students' learning during internships: Exploring the explanatory power of the job demands‐control‐support model

Informal learning behaviors, interaction and workplace autonomy and readiness to learn

Asset specificity, relational governance, firm adaptability and supply chain integration

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