Predicting Sustainable Farm Performance—Using Hybrid Structural Equation Modelling with an Artificial Neural Network Approach

Hayat, Naeem; Mamun, Abdullah Al; Nasir, Noorul Azwin Md; Selvachandran, Ganeshsree; Nawi, Noorshella Che; Gai, Quek Shio

doi:10.3390/land9090289

Cited by 26 publications

(39 citation statements)

References 66 publications

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“…Factors that affect the intention of decision makers to embrace green IT were investigated [ 68 ]. Another study aimed to examine the effects of personal and standard appeal factors on intention to adopt conservative agriculture practices and their influences on sustainable farm performance [ 69 ]. The second set of studies that discussed SEM–ANN with cloud computing and ERP systems includes three ( n = 3) articles.…”

Section: Search Results and Statistical Information Of Articlesmentioning

confidence: 99%

Hybrid artificial neural network and structural equation modelling techniques: a survey

Albahri

Alnoor

Zaidan

et al. 2021

Complex Intell. Syst.

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Topical treatments with structural equation modelling (SEM) and an artificial neural network (ANN), including a wide range of concepts, benefits, challenges and anxieties, have emerged in various fields and are becoming increasingly important. Although SEM can determine relationships amongst unobserved constructs (i.e. independent, mediator, moderator, control and dependent variables), it is insufficient for providing non-compensatory relationships amongst constructs. In contrast with previous studies, a newly proposed methodology that involves a dual-stage analysis of SEM and ANN was performed to provide linear and non-compensatory relationships amongst constructs. Consequently, numerous distinct types of studies in diverse sectors have conducted hybrid SEM–ANN analysis. Accordingly, the current work supplements the academic literature with a systematic review that includes all major SEM–ANN techniques used in 11 industries published in the past 6 years. This study presents a state-of-the-art SEM–ANN classification taxonomy based on industries and compares the effort in various domains to that classification. To achieve this objective, we examined the Web of Science, ScienceDirect, Scopus and IEEE Xplore® databases to retrieve 239 articles from 2016 to 2021. The obtained articles were filtered on the basis of inclusion criteria, and 60 studies were selected and classified under 11 categories. This multi-field systematic study uncovered new research possibilities, motivations, challenges, limitations and recommendations that must be addressed for the synergistic integration of multidisciplinary studies. It contributed two points of potential future work resulting from the developed taxonomy. First, the importance of the determinants of play, musical and art therapy adoption amongst autistic children within the healthcare sector is the most important consideration for future investigations. In this context, the second potential future work can use SEM–ANN to determine the barriers to adopting sensing-enhanced therapy amongst autistic children to satisfy the recommendations provided by the healthcare sector. The analysis indicates that the manufacturing and technology sectors have conducted the most number of investigations, whereas the construction and small- and medium-sized enterprise sectors have conducted the least. This study will provide a helpful reference to academics and practitioners by providing guidance and insightful knowledge for future studies.

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Section: Search Results and Statistical Information Of Articlesmentioning

confidence: 99%

Hybrid artificial neural network and structural equation modelling techniques: a survey

Albahri

Alnoor

Zaidan

et al. 2021

Complex Intell. Syst.

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“…Artificial neural network (ANN) analysis is a non-compensatory analytical technique with deep learning algorithms based on three layers: input, output, and hidden layers. The hidden layer connects the input neurons with the output neurons [87], acting as the block-box similar to the human brain [88]. The data are divided into three parts for training, testing, and holding out part of the sample.…”

Section: Data Analysis Methodsmentioning

confidence: 99%

Sustainable customer retention through social media marketing activities using hybrid SEM-neural network approach

et al. 2022

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Social media has changed the marketing phenomenon, as firms use social media to inform, impress, and retain the existing consumers. Social media marketing empowers business firms to generate perceived brand equity activities and build the notion among consumers to continue using the firms’ products and services. The current exploratory study aimed to examine the effects of social media marketing activities on brand equity (brand awareness and brand image) and repurchase intention of high-tech products among Chinese consumers. The study used a cross-sectional design, and the final analysis was performed on 477 valid responses that were collected through an online survey. Partial least squares structural equation modelling (PLS-SEM) and artificial neural network (ANN) analysis were performed. The obtained results revealed positive and significant effects of trendiness, interaction, and word of mouth on brand awareness. Customisation, trendiness, interaction, and word of mouth were found to positively affect brand image. Brand awareness and brand image were found to affect repurchase intention. The results of multilayer ANN analysis suggested trendiness as the most notable factor in developing brand awareness and brand image. Brand awareness was found to be an influential factor that nurtures repurchase intention. The study’s results confirmed the relevance of social media marketing activities in predicting brand equity and brand loyalty by repurchase intention. Marketing professionals need to concentrate on entertainment and customisation aspects of social media marketing that can help to achieve brand awareness and image. The limitations of study and future research opportunities are presented at the end of this article.

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“…An extensive list of CAPs is available (Hayat et al, 2020 ). However, few CAPs are regarded as primarily utilized around the world.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The use of organic fertilizer like animal manure replaced the inorganic fertilizer utilized and is called bio-fertilizer (Kassam et al, 2018 ). Composting is making fertilizer from animal manure and/or from the other natural sources of nitrogen enriching the farm (Hayat et al, 2020 ). Direct seeding links with no-till farming practice and brings cost-saving benefits with agronomic and ecological gains (Kassam et al, 2018 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The development of the intention to adopt CAPs is the first stage toward the likelihood that the adoption of CAPs may occur (Adnan et al, 2017 ; Mannan et al, 2018 ). The studies exploring the farmer's intention to adopt the CAPs primarily utilized regression-based models to understand the factors contributing to the farmer's intention to adopt the CAPs (Hayat et al, 2020 ). However, farmers' personal factors of environment and production orientation are scantly studied (Small et al, 2016 ; Khatri-Chhetri et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

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A Fuzzy-Set Analysis of Conservative Agriculture Practice Adoption: Role of Farmer Orientations and Attitude

Hayat

Mamun²,

Salameh³

et al. 2022

Front. Psychol.

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Conservative agriculture practice (CAP) adoption literature advocates that adoption is caused by many factors comprising cognitive, social, economic, personal, and CAP-related factors. Evaluating the adoption of CAPs as the outcome is complex and challenging with regression-based models as the systemic interdependencies of the factors offer diverse or varying results. Farmer production and environmental orientations as cognitive stances are notable interpreters of CAP adoption. The appetite level for risk-taking, innovativeness, and trust facilitates the adoption of CAPs. However, a causal-predictive technique should be used to investigate the adoption of CAP. Hence, this study engages in a configuration approach using a fuzzy-set qualitative comparative analysis (fs/QCA) to analyze the patterns of different types of farmers' orientations, personal level of trust on extension services, and innovativeness risk-taking attitude on the intention to adopt CAPs. The analysis is based on the 155-farmer data collected employing a structured interview from Pakistan. The results suggest that a higher level of environment orientation and innovativeness is sufficient to increase the intention to adopt CAPs. Moreover, a higher intention to adopt CAPs is achieved with a lower production orientation, a higher personal level of innovativeness, and a risk-taking attitude of the farmer. The innovativeness can help to develop the intention to adopt the CAPs among the environment and production-oriented farmers. Causal solutions offer a unique understanding that the farmers' environment and production orientation can combine to suggest inclined to adopt CAPs by having an attitude of innovativeness and risk-taking. The causal solutions achieved significant predictive validity in the holdout samples. Policy and farmer-level suggestions were made to raise the intention to adopt CAPs among the farmers.

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Predicting Sustainable Farm Performance—Using Hybrid Structural Equation Modelling with an Artificial Neural Network Approach

Cited by 26 publications

References 66 publications

Hybrid artificial neural network and structural equation modelling techniques: a survey

Hybrid artificial neural network and structural equation modelling techniques: a survey

Sustainable customer retention through social media marketing activities using hybrid SEM-neural network approach

A Fuzzy-Set Analysis of Conservative Agriculture Practice Adoption: Role of Farmer Orientations and Attitude

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