Research of artificial intelligence in the retail management problems

Semenov, Viktor P.; Chernokulsky, Vladimir V.; Razmochaeva, Natalya V.

doi:10.1109/ctsys.2017.8109560

Cited by 21 publications

(8 citation statements)

References 1 publication

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“…In this sense, some authors attempt to explain the shopping experience of consumers in those businesses that implement ICT directly linked to AI (Liu et al, 2018). However, the contributions in this line of research are still scarce, and studies that try to explain the main advantages and disadvantages of this type of tool, both from the point of view of companies and consumers are still at an exploratory level (Semenov et al, 2017;Liu et al, 2018;Mahmoud et al, 2020;Rodgers et al, 2021).…”

Section: Ict In Retailmentioning

confidence: 99%

The Moderating Effect of Store Format on the Relationships Between ICT, Innovation and Sustainability in Retailing

et al. 2021

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Innovation and sustainability are postulated as key variables for the future of large commercial distribution. In addition, the development of Information and Communication Technologies (ICT) solutions, and especially those related to Artificial Intelligence (i.e., Just Walk Out, Intelligent Retail Lab) and digitization, are particularly relevant factors in the current pandemic scenario in which retail companies operate. These tools are essential to face the derived changes in commercial relations, especially between companies and consumers. For all these reasons, this work aims to examine the effect of ICT, as a driving factor for innovation and its direct and indirect impact on sustainability in retailing. Furthermore, this study takes into account the eventual differences in these relationships according to the types of store formats. To achieve the aforementioned objective, a theoretical model is proposed that is tested through an empirical study carried out on a sample of 510 consumers of three store formats of grocery retail formats (i.e., hypermarkets, supermarkets and discount stores) in Spain. For the analysis of the data obtained, the partial least squares (PLS) regression technique and the Multigroup Analysis were used. The results obtained confirm the direct and indirect effect of ICT on innovation and sustainability in grocery retailing. In addition, the results indicate that consumers unequally perceive the technological progress of companies. These differences are greater between discount stores versus hypermarkets and supermarkets. The larger the size of the store format, the higher the incidence of ICT in relation to innovation. Therefore, it is vital to take ICT into consideration, where Artificial Intelligence is imperative for the growth and development of sustainable competitive advantages in retail companies.

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Section: Ict In Retailmentioning

confidence: 99%

The Moderating Effect of Store Format on the Relationships Between ICT, Innovation and Sustainability in Retailing

et al. 2021

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“…Investigating the performance of ANNs, the adoption of two or more neurons in the hidden layer is recurrent in non-linear problems. 61,62 Consequently, the onion's productivity losses are solved by linear regressions when the variables coexistence period and irrigation system are used as inputs. Although the ANN with two entrances reached a solution, this network's performance was similar to the MLR obtained from each irrigation system, restricting the use of this network for decision-making.…”

Section: Discussionmentioning

confidence: 99%

Machine learning models as an alternative to determine productivity losses caused by weeds

Souza

Monteiro

Silva

et al. 2021

Pest Management Science

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BACKGROUND Weed control can be economically viable if implemented at the necessary time to minimize interference. Empirical mathematical models have been used to determine when to start the weed control in many crops. Furthermore, empirical models have a low generalization capacity to understand different scenarios. However, computational development facilitated the implementation of supervised machine learning models, as artificial neural networks (ANNs), capable of understanding complex relationships. The objectives of our work were to evaluate the ability of ANNs to estimate yield losses in onion (model crop) due to weed interference and compare with multiple linear regression (MLR) and empirical models. RESULTS MLR constructed from non‐destructive and destructive methods show R2 and root mean square error (RMSE) values varying between 0.75% and 0.82%, 13.0% and 19.0%, respectively, during testing step. The ANNs has higher R2 (higher than 0.95) and lower RMSE (less than 6.95%) compared to MLR and empirical models for training and testing steps. ANNs considering only the coexistence period and system have similar performance to MLR models. However, the insertion of variables related to weed density (non‐destructive ANN) or fresh matter (destructive ANN) increases the predictive capacity of the networks to values close to 99% correct. CONCLUSION The best performing ANNs can indicate the beginning of weed control since they can accurately estimate losses due to competition. These results encourage future studies implementing ANNs based on computer vision to extract information about the weed community.

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“…However, their key concerns were regarding ethics and employment opportunities. Semenov et al. (2017) studied the problems of AI-based retail systems particularly in the vending machines.…”

Section: Literature Review On Agricultural Value Chain Actorsmentioning

confidence: 99%

“…However, their key concerns were regarding ethics and employment opportunities. Semenov et al (2017) studied the problems of AI-based retail systems particularly in the vending machines. Their study found that consumers are comfortable with technology, and it brings more profit to the organization.…”

Section: Distribution and Retailmentioning

confidence: 99%

Artificial intelligence in agricultural value chain: review and future directions

Ganeshkumar

Jena

Sivakumar

et al. 2021

JADEE

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PurposeThis paper is a literature review on use of artificial intelligence (AI) among agricultural value chain (AVC) actors, and it brings out gaps in research in this area and provides directions for future research.Design/methodology/approachThe authors systematically collected literature from several databases covering 25 years (1994–2020). They classified literature based on AVC actors present in different stages of AVC. The literature was analysed using Nvivo 12 (qualitative software) for descriptive and content analysis.FindingsFifty percent of the reviewed studies were empirical, and 35% were conceptual. The review showed that AI adoption in AVC could increase agriculture income, enhance competitiveness and reduce cost. Among the AVC stages, AI research related to agricultural processing and consumer sector was very low compared to input, production and quality testing. Most AVC actors widely used deep learning algorithm of artificial neural networks in various aspects such as water resource management, yield prediction, price/demand forecasting, energy efficiency, optimalization of fertilizer/pesticide usage, crop planning, personalized advisement and predicting consumer behaviour.Research limitations/implicationsThe authors have considered only AI in the AVC, AI use in any other sector and not related to value chain actors were not included in the study.Originality/valueEarlier studies focussed on AI use in specific areas and actors in the AVC such as inputs, farming, processing, distribution and so on. There were no studies focussed on the entire AVC and the use of AI. This review has filled that literature gap.

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Research of artificial intelligence in the retail management problems

Cited by 21 publications

References 1 publication

The Moderating Effect of Store Format on the Relationships Between ICT, Innovation and Sustainability in Retailing

The Moderating Effect of Store Format on the Relationships Between ICT, Innovation and Sustainability in Retailing

Machine learning models as an alternative to determine productivity losses caused by weeds

Artificial intelligence in agricultural value chain: review and future directions

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