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Small and medium-sized enterprises (SMEs) are said to engage significantly less with corporate sustainability (CS) than their larger peers, because they are more reluctant to voluntarily engage in CS beyond regulatory thresholds. However, the mechanisms for changing the reluctance of SMEs with regard to CS are poorly understood. By drawing on self-determination and stakeholder theory from a social proximity perspective, this paper argues that stakeholders play a key role in influencing the controlled CS motivation of SMEs, and that SMEs will consider the claims of 'proximate' stakeholders as being more salient than 'distant' regulatory pressure, with the latter even potentially exercising a negative effect on SMEs' controlled CS motivation. The hypotheses are empirically tested using survey-based data from a sample of 344 privately-held SMEs operating in Germany and Austria. Results of the structural equation model confirm that 'proximate' employee and community pressure reduces controlled CS motivation and ultimately increases overall CS performance, whilst 'distant' regulatory pressure has precisely the opposite effect, ultimately reducing the CS performance of SMEs. These findings help to clarify that the close attachment of SMEs to their employees, and their deep embeddedness in the local community might be important catalysts with regard to CS improvements of SMEs, whilst regulatory pressure reduces their willingness to engage in CS, since this is often perceived as an unfair, demotivating, external imperative, which compromises their self-determination. Lastly, theoretical and managerial implications are provided. [Display omitted] • We analyze the influence of stakeholder pressure on corporate sustainability (CS). • Social proximity (close vs. distant) is a driver of stakeholder salience in SMEs. • Close employee and community pressure reduces controlled CS motivation. • Distant regulatory pressure increases controlled CS motivation. • Policy-makers should be cautious to apply regulatory pressure to spur CS. [ABSTRACT FROM AUTHOR]

One month-ahead electricity prices open up a great potential for manufacturing companies that aim at reducing energy costs. On the machine level, an energy cost-oriented scheduling can be realized through determining the least electricity cost-intensive launch times for job processing during a day. To obtain reliable price information at production planning stage in advance, well-performing forecast models are required. In this paper, we apply a Weighted Nearest Neighbor (WNN) approach, a TBATS approach, and a Deep Feedforward Neural Network (DFNN) approach to forecast EPEX Spot day-ahead electricity prices of the European Energy Exchange in Germany/Austria, while accounting for a forecast horizon of up to one month. Model performances are compared by means of established error measures as well as by adjusted error measures to better interpret results from a production planner's point of view. For evaluation purposes, 1357 different forecasts were computed for each of the methods, allowing valid conclusions as to their performance. The results show that all approaches lead to acceptable accuracies. However, the DFNN outperforms WNN and TBATS, providing well-fitting forecasts even 29 days ahead. Image 1 • Artificial Neural Networks outperform TBATS and Weighted Nearest Neighbor. • Forecast results are highly applicable for mid-term production planning purposes. • Neural Network forecasts for longer terms are almost as precise as for shorter ones. • On average, only 5% of the adjusted forecasts exceed the limit of a 10.00 EUR error. [ABSTRACT FROM AUTHOR]