Many citizens experience ambivalence – having simultaneously positive and negative evaluations – about changing their behaviour towards a more environmentally friendly lifestyle. Based on 36 studies, this study identifies and synthesises the current evidence on how ambivalence impacts environmental behaviours. In most studies, ambivalence is shown to be directly and negatively associated with environmental behaviours, i.e., higher levels of ambivalence are linked to lower levels of environmentally friendly and unfriendly behaviours. This applies to both types of ambivalence: objective (OA) and subjective (SA). Mediator analyses show, in line with the theory, that SA, not OA, drives behavioural change. In addition, results indicate that ambivalence moderates the relationship between independent–dependent variables mainly negatively, for example, by weakening attitude–behaviour relationships. This review shows the potential of ambivalence to facilitate behaviour change: SA about environmentally friendly behaviour can hinder, whereas SA about environmentally unfriendly behaviour can motivate, behaviour change. In addition, this review highlights some significant knowledge gaps in this body of research. A lack of validated standardised measurements of ambivalence makes it challenging to compare studies and reach conclusions about underlying theoretical constructs. Methods, research designs, and theoretical underpinnings need improvement to fully understand ambivalence and progress towards the transition of environmentally friendly behaviours.
MULTIFILE
This paper introduces and explores the psychological and social factors that both contribute to and inhibit behaviour change vis-à-vis sustainable (tourist) mobility. It is based on papers presented at the Freiburg 2012 workshop. Specifically, it reviews climate change attitudes and perceptions, the psychological benefits of tourism mobilities, addictive elements of mobility and social norming effects, the attitude-behaviour gap (i.e. cognitive dissonance between understandings of, and responses to, climate change), the psychology of modal shifts, the psychology of travel speed/time and psychological explanations for the perceived importance of long distance travel. It notes that anthropogenic climate change is an inescapable reality and that tourism's share of greenhouse gas emissions appears set to rise substantially. There is little prospect of technical solutions adequately addressing this problem. The paper concludes that, while a comprehensive understanding of tourist psychology is necessary to inform policy-makers, it alone will be insufficient to achieve emission reductions, and bring tourism to a climatically sustainable pathway, if treated in isolation. Radical change in the structures of provision is also necessary. That change may take the form of infrastructure planning, including financial and economic infrastructure (e.g. taxation regimes and emission trading schemes) for sustainable mobility.
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The last decades, the number of dairy cows per farm has increased and the time spent on individual cows by the farmer has been reduced. To help the farmer detect changes in activity of cows associated with health issues, activity monitoring systems are being developed. The systems can help with daily farm management decisions, thus increasing farm profitability. Besides this economic benefit there is a social benefit: farmers highly value the herd being under continuous surveillance. Nedap Livestock Management (the Netherlands) introduced a leg activity meter and a neck activity collar: Smarttag Leg and Smarttag Neck. By registering activity of individual cows, the Smarttags help the farmer to detect oestrus and give alerts when activity deviates from normal patterns. The objective of this study was to validate results from the Smarttag Leg and Smarttag Neck, by comparing them with results from live observations and video recordings. Eight lactating dairy cows were observed for 22.5 hours each and video recordings were made of six dry dairy cows for 5.5 hours each. Lying, standing, walking, eating, standing up and ruminating were recorded. Data were compared with results from the Smarttag Leg and Smarttag Neck by calculating Cohen’s Kappa, univariate linear regression analysis, Pearson’s correlation and concordance correlation coefficient. Visual observations and video observations show correlation coefficients of >0.85 with the results of the Smarttags for all behaviours except walking. Correlations between visual and video observations and Smarttag results for walking were 0.45 and 0.50 respectively, possibly due to low incidence and difficulties in observing this behaviour. These results provide strong evidence that the Smarttag Leg and Smarttag Neck can reliably be used to monitor specific behaviours. With this system, the farmer can monitor behaviour and detect behavioural changes.
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