Agriculture is crucial in catering to the increasing demand for food and employment. Thus, adoption of novel technologies is important. Many scientists have developed different theories and models explaining the process of behavioral change relevant to adoption. They are either completely different, similar, or improvements of previously developed models. Therefore, compilation and summarization of these theories and models will support future studies and researchers. Thus, an analysis of literature on technology adoption was conducted. The review was prepared based on literature from various sources spanning around 50 years. The theories and models identified by different studies were compiled and analyzed in this review paper. Many theories and models in agricultural technology adoption such as transtheoretical model, theory of reasoned action, theory of interpersonal behavior, model for innovation-decision process, different versions of technology acceptance model, theory of planned behavior, theory of diffusion of innovation, task-technology fit, technology readiness, unified theory of acceptance and use of technology, expectancy livelihood model, social cognitive theory, and perceived characteristics of innovating theory were compiled. Each theory and model has its own uniqueness, which had explained different aspects of technology adoption process and factors determining the behavioral change. These theories and models included affecting factors such as technological, personal, social, and economical factors. In conclusion, it can be stated that, rather than having a single theory or a model, an integrated and amalgamated form will be more explanatory for technology adoption.

/pdf/theories-and-models-of-technology-adoption-in-agricultural-2hclfequ.pdf

Theories and Models of Technology Adoption in Agricultural Sector

Abstract Antibiotic use (and misuse) accelerates antimicrobial resistance (AMR), and addressing this complex problem necessitates behaviour change related to infection prevention and management and to antibiotic prescribing and use. As most antibiotic courses are prescribed in primary care, a key focus of antimicrobial stewardship (AMS) is on changing behaviours outside of hospital. Behavioural science draws on behaviour change theories, techniques and methods developed in health psychology, and can be used to help understand and change behaviours related to AMR/AMS. Qualitative methodologies can be used together with a behavioural science approach to explore influences on behaviour and develop and evaluate behavioural interventions. This paper provides an overview of how the behavioural science approach, together with qualitative methods, can contribute and add value to AMS projects. First, it introduces and explains the relevance of the behavioural science approach to AMR/AMS. Second, it provides an overview of behaviour change ‘tools’: behaviour change theories/models, behavioural determinants and behaviour change techniques. Third, it explains how behavioural methods can be used to: (i) define a clinical problem in behavioural terms and identify behavioural influences; (ii) develop and implement behavioural AMS interventions; and (iii) evaluate them. These are illustrated with examples of using qualitative methods in AMS studies in primary care. Finally, the paper concludes by summarizing the main contributions of taking the behavioural science approach to qualitative AMS research in primary care and discussing the key implications and future directions for research and practice.

How can behavioural science contribute to qualitative research on antimicrobial stewardship in primary care?

The aim of this paper is twofold: (1) to integrate several theoretical perspectives into a novel Meta-model of Adaption in Sport (MAS model) and (2) to support the MAS model with a narrative literature review on change-provoking events and related adaptation processes, factors involved, and outcomes. The MAS model is designed to account for two potential tracks of adaptation – fast and prolonged – that are situated along the continuum of adaptation experiences in sport. In each track, sport performers experience various types of sport-related events (i.e., acute events or change events) that are associated with specific sets of demands and perceptions related to self-efficacy beliefs and challenge-threat appraisal. Accordingly, the performer responds to these events by using the mechanisms which determine his/her adaptation process and outcomes. We present a narrative review of the literature related to both adaptation tracks that provide empirical support to the MAS model and conclude by offering reflections on the MAS model and its potential applications in sport psychology research and practice. 

Adaptation to change: a meta-model of adaptation in sport

Antibiotic use (and misuse) accelerates antimicrobial resistance (AMR), and addressing this complex problem necessitates behaviour change related to infection prevention and management and to antibiotic prescribing and use. As most antibiotic courses are prescribed in primary care, a key focus of antimicrobial stewardship (AMS) is on changing behaviours outside of hospital. Behavioural science draws on behaviour change theories, techniques and methods developed in health psychology, and can be used to help understand and change behaviours related to AMR/AMS. Qualitative methodologies can be used together with a behavioural science approach to explore influences on behaviour and develop and evaluate behavioural interventions. This paper provides an overview of how the behavioural science approach, together with qualitative methods, can contribute and add value to AMS projects. First, it introduces and explains the relevance of the behavioural science approach to AMR/AMS. Second, it provides an overview of behaviour change 'tools': behaviour change theories/models, behavioural determinants and behaviour change techniques. Third, it explains how behavioural methods can be used to: (i) define a clinical problem in behavioural terms and identify behavioural influences; (ii) develop and implement behavioural AMS interventions; and (iii) evaluate them. These are illustrated with examples of using qualitative methods in AMS studies in primary care. Finally, the paper concludes by summarizing the main contributions of taking the behavioural science approach to qualitative AMS research in primary care and discussing the key implications and future directions for research and practice. 

/pdf/oup-accepted-manuscript-9kabevj1.pdf

OUP accepted manuscript

Retracing the evolutionary steps by which human brains evolved can offer insights into the underlying mechanisms of human brain function as well as the phylogenetic origin of various features of human behavior. To this end, this article presents a model for interpreting the physical and behavioral modifications throughout major milestones in human brain evolution. This model introduces the concept of a "breakthrough" as a useful tool for interpreting suites of brain modifications and the various adaptive behaviors these modifications enabled. This offers a unique view into the ordered steps by which human brains evolved and suggests several unique hypotheses on the mechanisms of human brain function.

/pdf/five-breakthroughs-a-first-approximation-of-brain-evolution-2lo0z32u18.pdf

Five Breakthroughs: A First Approximation of Brain Evolution From Early Bilaterians to Humans.

The distinction between theories and models is blurred. Since models often have a theoretical content and theories are often expressed by models the terms are sometimes used synonymously. Patterns of data may suggest theories and/or models with the latter necessitating validation against facts. This form of scientific explanation therefore moves from data to theory from theory to model and from model back again to data. Theories models and data are considered. Theories are plausible explanatory propositions devised to link possible causes to their effects. Generally models are schematic representations of reality or of ones view of a possible world constructed to improve ones understanding about the world and/or to make predictions. Models are therefore intermediate between theory and data. Data are used to confirm or falsify theories and models. The author points out to the reader however that data do not impose themselves upon the scientist other forms of scientific explanation exist and some theories or models may have no link with data. Models without theory are common in applied research and they can be useful for smoothing interpolating and extrapolating. Theories and models should however be used in combination if one wishes to understand how the world works.

Theories, models, and data.

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Theories, models, and data.