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The purpose of this paper is to explore a hybrid model of the consumption of organic foods, combining the use of exploratory factor analysis (EFA) and an artificial neural network (ANN).

The study has three phases. In the first phase, the Delphi method is employed, and 15 motives for the consumption of organic food are identified; these motives are used to develop the model in the second phase. Finally, in the last phase, an ANN is used to rank the motives to determine their priority.

The EFA model explored includes four factors that have a positive effect on the level of organic food consumption. These are naturalness, trust, sanitariness and marketing. Results from the use of an ANN indicate that the main variables in organic food consumption are claims, psychological variables and doubt. From the results of the EFA model it is clear these three variables are components of the factor of trust.

Marketers can use the model developed in this paper to satisfy the needs of their customers and hence enhance their market share and profitability. This study shows that improvements in truth in the claims made for organic products, perceived security from using these products and doubts about the safety of other foods can lead marketers to their goal. Informative advertisements can inculcate trust and naturalness among consumers as main factors.

The main contribution of this study is the light it sheds on how consumers think about organic foods. It develops a model incorporating motives for consuming organic food and determining the priorities held by consumers of organic foods.

The purpose of this paper is to explore a model and note the ranking of the trust factors of messages about organic food in social networks.

The research was divided into four phases. The first employed the literature review about Trust, Trust of products, Trust of organic foods and Trust in the social networks. This review was prepared as some hypothesis about the trust of messages about organic food in the social network. The second employed a focused interview to supplement the mentioned hypothesis to 31 factors that affect the trust of messages about organic food in social networks. In the third phase, 300 forms were used to collect information from Iranian consumers for exploratory factor analysis. Finally, neural networks were used to determine the ranking of the mentioned factors.

The results show 31 factors that affect the trust of messages about organic food in social networks. The results of this study showed that Iranian and international organic foods producers may be able to spread messages of trust about their products in social networks by attending to these 31 factors. This study also explored a model constructed using EFA that showed that six factors have a positive effect on the level of trust of messages about organic food in social networks.

This research effectively helps organic food producers to better understand the trust factors and ways to improve that trust in cyberspace marketing plans and to increase their sales.

For the first time, this research seeks a model for the factors affecting consumer trust in organic foods in social networks, and in the next step, it ranks these factors with artificial neural networks.

This study aims to comprehensively analyze the intersection of technology management and innovation management amidst the fourth industrial revolution, uncovering evolving trends and influential contributors.

Using the Bibliometrix R-package, this pioneering research conducts a bibliometric analysis to delve into innovation and technology management literature, quantifying scholarly output and identifying thematic breakthroughs.

The study reveals quantitative insights into the progression of innovation and technology management research, offering guidance on evolving trends, thematic breakthroughs and influential contributors.

The findings offer valuable insights for practitioners and managers, guiding them through emerging trends and recommending a dual focus on fundamental principles and emerging areas for strategic decision-making.

By fostering active engagement with evolving trends, this research contributes to the ongoing technology and innovation management discourse, potentially leading to societal benefits and advancements.

This study pioneers an in-depth bibliometric analysis at the intersection of innovation and technology management, offering unique insights and quantitative assessments of scholarly output and thematic trends, thus adding significant value to the existing literature.

This study aims to identify the origins, mechanisms and outcomes of applying behavioral insight in public policy research.

The authors conducted a systematic literature review to answer three research questions. The authors identified 387 primary studies, dated from January 2000 to April 2021 and coded them through a thematic analysis. Related studies were obtained through searching in Emerald, ScienceDirect, Sage, Springer, Wiley and Routledge.

The results identified eight themes for origins, 16 themes for mechanisms/techniques and 13 outcome-related themes. Through the thematic analysis, the major mechanisms of behavioral approach were found to be social marketing, information provision, social norms, incentives, affect, regulation design, framing, salience, defaults, simplification, networking, environment design, scheduled announcements, commitments, attitude-preference-behavior manifestation and combining behavioral and nonbehavioral mechanisms.

The findings of this review help policymakers to design or redesign policy elements.

This review provides the first systematic exploration of the existing literature on behavioral public policy.