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Artificial Intelligence (AI) is a growing technology impacting several business fields. The agricultural sector is facing several challenges, which may be supported by the use of such a new advanced technology. The aim of the paper is to map the state-of-the-art of AI applications in agriculture, their advantages, barriers, implications and the ability to lead to new business models, depicting a future research agenda.

A structured literature review has been conducted, and 37 contributions have been analyzed and coded using a detailed research framework.

Findings underline the multiple uses and advantages of AI in agriculture and the potential impacts for farmers and entrepreneurs, even from a sustainability perspective. Several applications and algorithms are being developed and tested, but many barriers arise, starting from the lack of understanding by farmers and the need for global investments. A collaboration between scholars and practitioners is advocated to share best practices and lead to practical solutions and policies. The promising topic of new business models is still under-investigated and deserves more attention from scholars and practitioners.

The paper reports the state-of-the-art of AI in agriculture and its impact on the development of new business models. Several new research avenues have been identified.

The study aims to investigate the time for social media posts and reviews in order to determine the best timing to ensure maximum outreach and interactions from users. The study intends to analytically investigate a company's Facebook and Instagram pages to get meaningful insights for effective post management.

“Great Deal Tires” company’s Facebook and Instagram business pages were taken as the case study and patterns and analytical insights for different posts using Facebook and Instagram analytics were identified. The study categorically selected posts from Facebook pages and Instagram pages that were posted at different times and assessed on their impact.

According to the study, Tuesday and Wednesday have higher engagement on the Great Deal Tires Facebook page, while Friday has higher engagement on Instagram. The study also provided valuable insights into post content and timing in order to increase the marketing impact of the posts.

The study provides an analytical framework for analyzing post and review timing on various company business pages, allowing marketers to initiate more user visits and interactions.

Logistical facility location decisions can make a crucial difference in the success or failure of a company. Geographical Information Systems (GIS) have recently become a very popular decision support system to help deal with facility location problems. However, until recently, GIS methodologies have not been fully embraced as a way to deal with new facility location problems in business logistics. This research makes a framework for categorizing empirical facility location problems based on the intensity of the involvement of GIS methodologies in decision making. This framework was built by analyzing facility location models and GIS methodologies. The research results revealed the depth of the embracement of GIS methodologies in logistics for determining new facility location decisions. In the new facility location decisions, spatial data inputs are almost always coupled with the visualization of the problems and solutions. However, the usage of GIS capability solely (i.e. suitability analysis) for problem solving has not been embraced at the same level. In most cases, the suitability analysis is used together with special optimization models for choosing among the multiple alternatives.

There are two aims of this research: one is to prove the interactive effect of supply chain integration (SCI) on performance and the other is to ascertain gaps in performance among levels of SCI. The population of this research is international freight forwarders and the collected data is used in testing hypotheses through various analytical methods such as factor analysis, Cronbach’s alpha, cluster analysis, ANOVA, MANOVA, ANCOVA, post hoc analysis and regression analysis. First, the interaction between internal integration and external integration improves customer performance and financial performance. The forwarders improve internal processes following information acquired from customers and the information is shared with logistics service providers, followed by high performance. Second, gaps in performance among levels of SCI are verified. Managers of the forwarders make strategic decision making on the basis of their recognition of environment and, as a result, the forwarders enjoy different performance.

This research empirically analyzes gaps in performance among development stages of integration in supply chain management (SCM). The used variables are integration such as internal and external and performance such as cost and service. The population is international freight forwarders in the Republic of Korea and the sample frame is the membership list of the Korean International Freight Forwarders Association. The used methodology in this research is various: Cronbach’s alpha, factor analysis, cluster analysis, analysis of variance and post hoc analysis. It is important to ascertain the position in which they are included and the position is concerned with internal integration and external integration because the aim of this research is to ascertain where the forwarders are in development stages of integration. According to prior research, development stages of integration are regarded as the flow from internal to external. However, this research suggests that development stages of integration do not explain the flow of the stages but explain the strategic choice concerned with their current decision-making.

Despite the fact that chatbots have been largely adopted for the last few years, a comprehensive literature review research focusing on the intention of individuals to adopt chatbots is rather scarce. In this respect, the present paper attempts a literature review investigation of empirical studies focused on the specific issue in nine scientific databases during 2017-2021. Specifically, it aims to classify extant empirical studies which focus on the context of individuals' adoption intention toward chatbots.

The research is based on PRISMA methodology, which revealed a total of 39 empirical studies examining users' intention to adopt and utilize chatbots.

After a thorough investigation, distinct categorization criteria emerged, such as research field, applied theoretical models, research types, methods and statistical measures, factors affecting intention to adopt and further use chatbots, the countries/continents where these surveys took place as well as relevant research citations and year of publication. In addition, the paper highlights research gaps in the examined issue and proposes future research directions in such a promising information technology solution.

As far as the authors are concerned, there has not been any other comprehensive literature review research to focus on examining previous empirical studies of users' intentions to adopt and use chatbots on the aforementioned period. According to the authors' knowledge, the present paper is the first attempt in the field which demonstrates broad literature review data of relevant empirical studies.

Mobile location-based service (m-LBS) seems like a new class of personalized service due to location positioning technologies. This work aims to investigate consumer readiness (RED) toward m-LBS based on integrating pull effect- and push effect-related factors into the technology acceptance model (TAM).

An online survey collected data from 423 participants, and the research framework was analyzed using structural equation modeling (SEM).

The results divulge that consumer RED is determined by TAM antecedents, including usefulness (USE) and ease of use (EOU). EOU motivates USE in m-LBS. Regarding pull effect-related factors, absorptive capacity (ABC) is the strongest positive factor influencing consumer RED to use m-LBS, followed by technology willingness (TWI) and innovativeness (INN). Moreover, INN, trust (TRU) and perceived risk (RIS) significantly influence USE and EOU.

This work endeavors to explicate customer RED toward m-LBS by incorporating some meaningful pull effect-related dimensions (i.e. ABC, TWI and INN) and pushing effect-related dimensions (i.e. RIS) into crucial antecedents rooted in TAM. Thus, the findings assist practitioners in developing marketing strategies by boosting pull effects and controlling push effects on customer engagement in m-LBS.

Inaccurate product information on retail websites lead to dissatisfied customers and profit losses. Yet, the effects of product information failures (PIFs) remain under-explored, with the mobile commerce channel commonly overlooked. This paper aims (1) to investigate the negative effects of PIFs on shoppers' attitudes and behaviours in a mobile context. The authors further (2) evaluate the impacts of the cause and duration of a PIF, changes of expectations towards the retailer after a PIF occurred and how previous mobile shopping experience in general decreases the effects of PIFs.

The authors conducted a scenario-based experiment with a one-factorial between-subjects design. The six most common PIFs of an international leading online fashion retailer are operationalized and tested against a control group. The final sample consists out of 758 mobile shoppers from the UK.

The results demonstrate that the perceived severity of PIFs based on showing customers incorrect information is higher when key information is lacking. Further, when the cause of a PIF is attributed to the retailer, it results in higher recovery expectations towards them. The results also reveal that respondents who have shopped mobile before perceive PIFs as less severe than inexperienced ones.

This research expands the online service failure literature by examining PIFs and its effects in the specific context of mobile commerce. The authors also provide recommendations for a better management of PIFs like the incorporation of PIFs information into reporting packs.

The purpose of this paper is to review the food supply chain management (FSCM) in terms of systems and implementations so that observations and lessons from this research could be useful for academia and industrial practitioners in the future.

A systematical and hierarchical framework is proposed in this paper to review the literature. Categorizations and classifications are identified to organize this paper.

This paper reviews total 192 articles related to the data-driven systems for FSCM. Currently, there is a dramatic increase of research papers related to this topic. Looking at the general interests on FSCM, research on this topic can be expected to increase in the future.

This paper only selected limited number of papers which are published in leading journals or with high citations. For simplicity without generality, key findings and observations are significant from this research.

Some ideas from this paper could be expanded into other possible domains so that involved parties are able to be inspired for enriching the FSCM. Future implementations are useful for practitioners to conduct IT-based solutions for FSCM.

As the increasing of digital devices in FSCM, large number of data will be used for decision-makings. Data-driven systems for FSCM will be the future for a more sustainable food supply chain.

This is the first attempt to provide a comprehensive review on FSCM from the view of data-driven IT systems.

The purpose of this paper is to deliver a structured literature review of existing literature on agri-fresh food supply chain quality (AFSCQ) over a period of 23 years (1994 to mid-2016) and provide a platform for practitioners and researchers trying to identify the existing state of work, gaps in current research, and future directions in the field of AFSCQ.

The existing literature is classified on the basis of several classes like number of publications per year, journal-wise publications, studies across various countries, growth of empirical research, data analysis methods or tool used, issues related to supply chain quality as well as performance measurement (with respect to entity of analysis, level of analysis and element of exchange).

Most of the research publications discuss issues in developed countries, while relatively lesser publications are available on issues in developing countries. Hence, larger opportunities in the field of AFSCQ are available in developing countries. Empirical research is also growing in the field of AFSCQ. Largely research publications make use of “case study” research approach and “statistical analysis” as a quantitative tool of research. The literature is also categorized under the various issues of supply chain quality such as sustainability management, information management, logistic management, collaboration and coordination management, strategic management, demand management, inventory management, food safety, performance management, supply chain integration, supplier management, quality management, etc. It was found that in the majority of articles, information management, sustainability management, and logistics management are very critical issues as far as AFSCQ is concerned. Performance measurement of agri-fresh food supply chain is also on a growing stage. It is also an integral part of AFSCQ.

Most of the prior reviews are concentrated on a particular issue as production and distribution planning for agri-foods, temperature monitoring, corporate and consumer social responsibility, traceability system and ignore the wider perception. There exists a necessity of having a detailed review to cover up all the issues in AFSCQ. This review fills this gap in the extant AFSCQ literature.