A connectionist implementation of the theory of planned behavior: Association of beliefs with exercise intention.

Lowe, Rodney; Bennett, Paul; Walker, Ian; Milne, Sarah; Bozionelos, George

doi:10.1037/0278-6133.22.5.464

Cited by 11 publications

(14 citation statements)

References 23 publications

(23 reference statements)

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“…Additionally, explicit comparisons of linear and nonlinear analytical approaches have shown that the fit of the latter is better than that of the former; nonlinear models can explain more variance in outcomes compared to their linear equivalents (e.g., Lowe et al, 2003).…”

Section: Uncertainty 1: Nonlinear Work-health Relationshipsmentioning

confidence: 99%

Section: The Present Studymentioning

confidence: 99%

“…Rather, the final models emerge from the pattern of activation. Thus, knowledge on the complexity of the system is contained in the network architecture and the pattern of interconnections, rather than in the exact relationships between predictors and outcomes.It has often been suggested that neural networks are better at examining nonlinear phenomena than conventional statistical approaches (e.g., Collins & Clark, 1993;Hanges et al, 2001;Lowe et al, 2003;Somers, 1999). Statistics and neural networks differ in three fundamental respects which provide the impetus for using neural networks in the present study.…”

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confidence: 99%

“…It has been surmised that neural networks are better at examining nonlinearity than conventional statistical approaches (Collins & Clark, 1993;Hanges et al, 2001). Connectionism has been used in applied areas such as workplace behavior (Collins & Clark, 1993), employee turnover (Somers, 1999), motivation theory (Lord et al, 2003), health behavior (Lowe et al, 2003), and cancer prognosis (Sargent, 2001). Although their uptake in disciplines such as engineering, physics, economics, neurobiology, cognitive psychology, and medicine has been rapid, they have been sparingly used elsewhere (Somers, 1999).…”

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confidence: 99%

“…True nonlinear models express nonlinearity in the parameters of the variables. Additional shortcomings of linear regression (Bansal et al, 1993; Lind & Sulek, 2001;Lowe et al, 2003) render it inappropriate for addressing nonlinear phenomena.The present study compared an inherently linear (multiple linear regression) and an inherently nonlinear method (artificial neural networks). It focused on two neural networks with one output, whose aim was to predict an outcome by learning the associations between that variable and a set of inputs.…”

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confidence: 99%

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The use of artificial neural networks and multiple linear regression in modelling work–health relationships: Translating theory into analytical practice

Karanika‐Murray¹,

Cox²

2010

European Journal of Work and Organizational Psychology

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Although psychological theory acknowledges the existence of complex systems and the importance of nonlinear effects, linear statistical models have been traditionally used to examine relationships between environmental stimuli and outcomes. The way that we analyze these relationships does not seem to reflect the way that we conceptualize them. The present study investigated the application of connectionism (artificial neural networks) to modeling the relationships between work characteristics and employee health by comparing it with a more conventional statistical linear approach (multiple linear regression) on a sample of 1003 individuals in employment. Comparisons of performance metrics indicated differences in model fit, with neural networks to some extent outperforming the linear regression models, such thati? for worn-out and job satisfaction were significantly higher in the neural networks.Most importantly, comparisons revealed that the predictors in the two approaches differed in their relative importance for predicting outcomes. The improvement is attributed to the ability of the neural networks to model complex nonlinear relationships. Being unconstrained by assumptions of linearity, they can provide a better approximation of such psychosocial phenomena. Nonlinear approaches are often better fitted for purpose, as they conform to the need for correspondence between theory, method and data. Modeling the impact of work and organizational characteristics on employee health is important in terms of decision-making for job design, re-design and organizational development. Effective management of work-related health relies on accurately prioritizing potential risks and explaining the highest variability in outcomes from a combination of work and organizational characteristics (Clarke & Cooper, 2004;Glendon, Clarke, & McKenna, 2006). In turn, this traditionally relies on correlational and linear approaches.Although such an approach has historically proven useful, it produces two sources of uncertainty for the assessment of risk to work-related health. First, it implies a linear relationship between work and organizational characteristics and health outcomes, which often conflicts with available empirical evidence (Karanika, 2006). Second, organizations themselves provide a demonstration of complexity (Cox et al, 2007;Schneider & Somers, 2006). It also renders examination of the impact of combinations of risks (as opposed to bivariate relationships) more pertinent to decision-making in relation to risk management for work-related health (Karanika-Murray, Antoniou, Michaelides, & Cox, in press). The present study tested a nonlinear connectionist model vis-a-vis a traditional linear regression model of the impact of work characteristics on work-related outcomes. The remainder of this section discussing two key sources of uncertainty in modeling work-health relationships which set the main arguments for artificial neural networks, before it briefly outlines their principles. Uncertainty 1: Nonlinear Work-H...

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Section: Uncertainty 1: Nonlinear Work-health Relationshipsmentioning

confidence: 99%

Section: The Present Studymentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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The use of artificial neural networks and multiple linear regression in modelling work–health relationships: Translating theory into analytical practice

Karanika‐Murray¹,

Cox²

2010

European Journal of Work and Organizational Psychology

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Motivation to learn and diversity training: Application of the theory of planned behavior

Wiethoff

2004

Human Resource Dev Quarterly

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Although training programs are an important component in most companies' diversity initiatives, little theoretical guidance is available for their implementation. This article proposes a model based on the theory of planned behavior, which addresses the roles of attitude, subjective norms, and perceived behavioral control in motivation to learn from a diversity training program. The model suggests a number of hypotheses that could be tested to enhance our understanding of the motivation-to-learn construct. Additionally, the model provides practical advice for companies seeking to implement successful diversity training programs.In a recent Society for Human Resource Management (SHRM) survey, 66 percent of 321 responding companies reported a strong commitment to diversity (Kluttz, 2002). These companies recognize a number of reasons to encourage the diversification of their workforce. Projected changes in the demographic composition of the population are often cited as a rationale (Digh, 1998). Many organizations also have found that diversity provides a strategic advantage (Flynn, 1998) and helps them serve an increasingly diverse customer base (Miller, 1999). Furthermore, evidence suggests that companies with innovative human resource development programs that encourage full employee participation may reap benefits of enhanced shareholder value (Orlando & Johnson, 2001).Most diversity programs begin with some form of awareness training for employees (Flynn, 1998). Indeed, recognizing the importance of effective training programs to overall diversity initiatives, corporate spending on diversity training remains strong even in economic slowdowns (Leonard, 2002;Kluttz, 2002 264Wiethoff these programs also seek to develop employees' communication skills to ensure that their interactions are free from prejudicial responses that pose potential legal liability for the organization.Despite the recognized importance of diversity training programs, neither human resource practitioners nor researchers who study diversity in organizations have developed theory-based models to explain how these programs succeed or fail. Caveats such as "make top management support for the program visible" and "tie diversity initiatives to performance evaluations" are known to be correlated with effective programs (Dobbs & Brown, 1997), but how and why these commonly accepted mandates work is yet to be determined. Given the large amounts of money and time that companies have already devoted to diversity initiatives, and the projected increase in spending for these programs in coming years (Kluttz, 2002), it is important to comb the literature carefully to develop theory-based assessments to determine, before the fact, whether a diversity training program has a good chance of succeeding. This article seeks to provide some insight into this area.I use the theory of planned behavior (TPB) to explain how and why adult learners, such as employees of an organization, are motivated to learn diversityfriendly behaviors. I begin with brief overviews ...

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A micro-level simulation for the prediction of intention and behavior

Richetin

Sengupta

Perugini

et al. 2010

Cognitive Systems Research

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A connectionist implementation of the theory of planned behavior: Association of beliefs with exercise intention.

Cited by 11 publications

References 23 publications

The use of artificial neural networks and multiple linear regression in modelling work–health relationships: Translating theory into analytical practice

The use of artificial neural networks and multiple linear regression in modelling work–health relationships: Translating theory into analytical practice

Motivation to learn and diversity training: Application of the theory of planned behavior

A micro-level simulation for the prediction of intention and behavior

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