Purpose: The introduction of artificial intelligence (AI) technology has had a substantial influence on the retail industry. However, AI adoption entails considerable responsibilities and risks for senior managers. In this study, the authors developed an evaluation and selection mechanism for successful AI technology adoption in the retail industry. The multifaceted measurement and identification of critical factors (CFs) can enable retailers to adopt AI technology effectively and maintain a sustainable competitive advantage.Design/methodology/approach: The evaluation and adoption of organisational AI technology involve multifaceted decision-making for management. Therefore, the authors used the analytic network process to develop an AI evaluation framework for calculating the weight and importance of each consideration. An expert questionnaire survey was distributed to senior retail managers and 17 valid responses were obtained. Finally, the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method was used to identify CFs for AI adoption.Findings: The results revealed five CFs for AI adoption in the retail industry. The findings indicated that after AI adoption, top retail management is most concerned with factors pertaining to business performance and minor concerned about the internal system's functional efficiency. Retailers pay more attention to technology and organisation context, which are matters under the retailers' control, than to external uncontrollable environmental factors.Originality/value: The authors developed an evaluation framework and identified CFs for AI technology adoption in the retail industry. In terms of practical application, the results of this study can help AI service providers understand the CFs of retailers when adopting AI. Moreover, retailers can use the proposed multifaceted evaluation framework to guide their adoption of AI technology.

The microwave-assisted induction heating (MAIH) method—an emerging thermal technique—was studied to heat the prepackaged raw hard clam (Meretrix lusoria). The cooking effects on microbial and physiochemical qualities of clam were investigated. After the heating of the clam meat samples, the aerobic plate count (APC), psychrotrophic bacteria count (PBC), and total volatile basic nitrogen (TVBN) levels decreased with increasing heating time, but the shucking ratio, area shrinkage, and texture (hardness, cohesiveness, and chewiness) increased. In addition, the L* (lightness) and W (whiteness) of the clam meat samples increased significantly at the beginning of the heating period, whereas they decreased significantly with extended heating time. However, a* (redness) had the opposite trend. This study found that when clams were heated for more than 120 s at 130 °C or 150 s at 90 °C, they displayed obvious shrinking and a yellow-brown appearance, indicating that they are overcooked. After heating by MAIH for at least 110 s at 130 °C or 130 s at 90 °C, the samples were cooked well and gains a completely shucking, along with no microbial count detected. Therefore, the results indicated that the optimum heating conditions for prepackaged hard clams subjected to an MAIH machine were 130 °C for 110 s or 90 °C for 130 s.

In the research, we evaluated the effects of high-pressure processing (HPP) on the growth and histamine formation of histamine-forming bacteria (HFB) in yellowfin tuna meat during storage. Tuna meat samples inoculated with the individual HFB species Morganella morganii and Photobacterium phosphoreum were subjected to HPP treatment at 250, 350, 450, and 550 MPa for 5 min, and the changes in bacterial count, total volatile basic nitrogen (TVBN) content, pH, and histamine content during storage at 4 and 15 °C were analyzed. The results indicate that the bacterial counts of the HFB species decreased significantly with increasing pressure, and HFB became undetectable in the samples treated at 450 and 550 MPa. At a storage temperature of 15 °C, the bacterial counts of both HFB species in the control group and samples subjected to HPP treatment at 250 and 350 MPa increased significantly with storage time. The bacterial counts of M. morganii in the samples stored at 4 °C decreased, whereas those of P. phosphoreum increased gradually owing to its psychrophilic nature. HPP treatment (>250 MPa) inhibited the increases in pH and TVBN content of the samples stored at 15 °C and delayed histamine formation in the samples during storage; these effects were more significant as the pressure during HPP treatment was increased.

In this study, we explore the needs of different valuable customer groups for service quality and how limited resources are allocated to enhance service quality. Accordingly, we propose a hybrid multi-criteria decision-making (MCDM) tool that uses fuzzy synthetic evaluation (FSE) in combination with the analytic hierarchy process (AHP) to help companies enhance understanding of quantitative data (the weights of the factors that affect service quality) and qualitative information to identify valuable customers. Fifty-three experts and 304 consumers at convenience stores (CVS) comprise the data set. We employed the AHP to obtain index weights in the second step of FSE and conducted FSE to determine the importance of various valuable customer groups. The results demonstrate that different valuable customer groups have dissimilar perceptions and feelings about service quality. The findings indicate that customers between “20 to 29 years old” are the most valuable customer group and that most consumers do not care much about “problem solving”. The analysis is distinct from extant work in that it examines the effect of receiving service quality from a consumer viewpoint, as we conducted a comprehensive analysis from both customer and expert perspectives.

This article builds a systematic and reliable site selection prediction model for a chain of convenience stores (CVSs) to improve the existing decision method of using experienced managers to select sites. Specifically, this study used an artificial neural network (ANN) technique—back-propagation neural network (BPN)—to build the prediction model. To achieve optimization in executing the BPN, the Taguchi method (TM) was adopted to find the optimal parameters for the BPN. The actual data from a chain of CVSs was employed to validate the model. The results indicated that the prediction accuracy rate and decision quality of the proposed model were higher than those of the existing manager-directed decision method. With intense retail competition, the accurate determination of the location of a new convenience store (CVS) is vital to its success. This study asserts that with systematic and scientific assessment, the site selection decision for new chain CVSs will be less human-biased in nature if the prediction model is used as an auxiliary decision-making tool.