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Information and communication technologies (ICT)-presented technological developments, such as soil sensors, remote sensing, artificial intelligence (AI) and big data, have shown the potential to increase crop output and quality while consuming fewer resources and having a smaller environmental impact. The first step in ushering in a new era of technological advancement in the agricultural sector in the Kingdom of Bahrain is evaluating how prepared farmers and farm owners are to adopt these technologies. Therefore, the current study examines how ICT are prepared, accepted and adopted in agriculture in the Kingdom of Bahrain.

The study's goals were attained by using both quantitative and qualitative methodologies. A survey was created to learn more about the present state of ICT usage in agriculture, including its awareness, readiness, acceptance and adoption. To strengthen the conclusions and investigate the current situation related agricultural behavior, production and the use of information technology (IT) to support agriculture in the chosen farms, four exploratory field visits were made. Additionally, a strength-weakness-opportunities-threat (SWOT)-threat, opportunities, weakness, strength (TOWS) analysis was performed to evaluate the Kingdom of Bahrain's readiness and long-term plans for implementing ICT in agriculture. On the basis of secondary data, survey data and interview findings, SWOT-TOWS were created.

The findings revealed insufficient knowledge and awareness about ICT in agriculture. Despite the high level of digital infrastructure readiness in Bahrain, farmers are not ready to adopt sophisticated devices and complex applications such as crop sensing tools, the internet of things (IoT) and AI; however, there is a strong acceptance among farmers to implement new ideas and agriculture approaches.

The Arabian Gulf Countries, which are characterized by an arid environment, sporadic vegetation, weak soil and a lack of water supplies and arable land, have few studies that explore the crucial role of ICT in growing the agricultural sector. Considering the influence of ICT on the provision of more productive agriculture in a challenging and complicated environment, the study contributes to the body of knowledge by conducting an empirical investigation that addresses an urgent issue. The study is considered one of the few in the countries of the Arabian Gulf to address this subject.

Industrial Revolution 4.0 is still evolving. The purpose of this paper is to assess the progress of Indonesia in achieving an initiative for Industry 4.0. As the largest country in Southeast Asia, Indonesia plays a critical role in implementing Industry 4.0. In addition, this study proposes an open innovation strategies for small and medium enterprises (SMEs) in facing Industry 4.0, especially in the Indonesian setting. Open innovation is viewed as a long-term innovation model that relies on cross-border commerce between businesses and countries.

This study undertakes a comprehensive literature review to capture the necessary insights for establishing an early grasp of solution design. A total of 32 sample papers were qualified using a set of selection criteria designed to find the most relevant existing studies in the Industry 4.0 and Indonesia domains. The meta-details as significant discoveries were processed using a content analysis approach. In addition, the research deployed sentiment analysis from text mining to inter-operate and classify (positive, negative and neutral) in-text data using text analysis techniques to identify public sentiment toward Industry 4.0 in Indonesia.

The key finding is that there is a favorable relationship between digital ecosystem readiness and open innovation adoption for SMEs. While, knowledge management is a critical factor in guiding a country’s successful implementation of the open innovation paradigm. Furthermore, some of the major findings revealed that many initiatives for Industry 4.0 are carried out by the private sectors. In regards to the procedure, the role of government is the protection of market regulations. This could be due to preserving fair competition between corporations and SMEs. Local businesses and SMEs should be protected to ensure their survival. In addition, the major cause of the slow adoption of Industry 4.0 in Indonesia is the lack of digital equipment. This is because of the shortage of digital equipment that can create a digital divide between large and small businesses and between industries in the urban and rural areas.

This study discussed some of the most essential issues of SMEs in adopting open innovation that is required for Industrial Revolution 4.0. It focuses on how digital ecosystem’s readiness influences open innovation adoption for SMEs in Indonesia. By understanding its current state of readiness, it contributes to the policymakers in deciding how and where to adopt open innovation and develop digital ecosystem and identify which ones might best meet their needs for any developing countries.

This paper is useful to academics, practitioners and policymakers in the fields of technology and public policy. The research provides some initial insights into Indonesia and any developing countries on Industry Revolution 4.0 and the needs for SMEs in adopting open innovation.

In a context where the process of creation of technology and innovation for agriculture is being disrupted at a fast pace, the authors proposed to study one of the most prominent agtech innovation ecosystems. Therefore, this paper aims to identify key characteristics that make California’s agtech innovation ecosystem remarkable.

The paper is an exploratory and descriptive research carried out in a twofold. First, data were collected through documental research focusing on actors such as universities, R&D centers and programs, business accelerators and venture capital platforms, agtechs, as well as multinational companies. Second, structured interviews were carried out to complement the secondary data collected and to obtain experts’ perception on the relationships between actors of the ecosystem and on the characteristics that make this ecosystem remarkable.

The paper provides empirical insights about the relevance of California's agtech innovation ecosystem to creation of radical innovations in agriculture. It has a differentiated environment, where educational and research institutions play a key role in developing new knowledge. It also shows how important funding is to allow new business to succeed. Additionally, it shows that actors interact in a complex network, with multiple roles. All these key characteristics allow this agtech innovation ecosystem to be so remarkable.

Because of the chosen research approach, the research results may lack generalizability. Therefore, researchers are encouraged to survey a larger number of actors of this and other agtech innovation ecosystems to test the identified key characteristics further.

The paper includes indication of characteristics necessary to develop a prominent agtech innovation ecosystem, which may contribute to decision makers to develop policies aiming to promote this type of ecosystem.

This paper fulfils an identified need to open the “black-box” of agtech innovation ecosystems, which may then allow radical innovations within the sector to be developed and taken to the market.

This paper aims to identify and analyze the agtech classification and categorization systems in the Brazilian context.

The systematic literature review (SLR) was carried out according to the protocol of Kitchenham and Charters (2007). The classification systems found in literature were evaluated using the thinking aloud protocol, as proposed by Ericsson and Simon (1993). The responses obtained were evaluated through lexicographic analysis, described by Bécue-Bertaut (2019) and content analysis, described by Bardin (2011).

SLR identified four agtech classification systems. The model proposed by Dias, Jardim, and Sakuda (2019) was the one with the highest adherence to classify Brazilian agtechs. From the analysis of the systems found in literature, the authors proposed a new categorization model of agricultural startups (agtechs).

The study has limitations in relation to the theoretical and empirical validation of the model proposed by the authors. This limitation can be the subject of subsequent research.

The SLR study considers the evolution of the classification systems of a new agribusiness reality, the agtechs. In addition, there is a practical contribution in proposing a new classification system that attempts to address some of the limitations found in previous studies.

Agtechs are startups focused on developing solutions for agriculture and have shown a significant increase in recent years. However, there are few studies focused on this type of company. Even rarer are the studies that seek to classify and categorize them. The present work opens the horizon for future studies focused on this new reality.

This study aims to investigate the adoption of Quality 4.0 (Q4.0) and assess the critical failure factors (CFFs) for its implementation and how its failure is measured.

A qualitative study based on in-depth interviews with quality managers and executives was conducted to establish the CFFs for Q4.0.

The significant CFFs highlighted were resistance to change and a lack of understanding of the concept of Q4.0. There was also a complete lack of access to or availability of training around Q4.0.

The study enhances the body of literature on Q4.0 and is one of the first research studies to provide insight into the CFFs of Q4.0.

Based on the discussions with experts in the area of quality in various large and small organizations, one can understand the types of Q4.0 initiatives and the CFFs of Q4.0. By identifying the CFFs, one can establish the steps for improvements for organizations worldwide if they want to implement Q4.0 in the future on the competitive global stage.

The concept of Q4.0 is at the very nascent stage, and thus, the CFFs have not been found in the extant literature. As a result, the article aids businesses in understanding possible problems that might derail their Q4.0 activities.

Considering the relevance of innovative business models in the digitally transformed market and the lack of clarity on the internet of services (IoS) contribution for a business model deployment in current literature, this study aims to fill this gap by evaluating a business model that converges to an IoS adoption in a direct sale of free-range eggs from farmers to consumers.

From the bibliographical research regarding the IoS and business model, the authors developed an IoS-based model framework. The framework has been evaluated in a real business scenario by using a single case study through an interview with the entrepreneur and documental analysis.

As the main result, a framework with the attributes can be considered a tool for an IoS-based business model deployment. The case study concluded that the business is aligned with the IoS adoption, and the framework presents adherence to it.

The case study was limited to only one company owing to the IoS’s novelty and the lack of correlated business models. Although the case study limits to the agriculture field, the proposed framework may be broadly applied.

Considering that the lack of a comprehensive business model causes new businesses to face challenges, it is relevant bringing up the present case study of the IoS-based business model, which correlates these two subjects, still poorly explored in the scientific literature: IoS and business models.

This study analyses the literature on artificial intelligence (AI) and its implications for the agri-food sector. This research aims to identify the current research streams, main methodologies used, findings and results delivered, gaps and future research directions.

This study relies on 69 published contributions in the field of AI in the agri-food sector. It begins with a bibliographic coupling to map and identify the current research streams and proceeds with a systematic literature review to examine the main topics and examine the main contributions.

Six clusters were identified: (1) AI adoption and benefits, (2) AI for efficiency and productivity, (3) AI for logistics and supply chain management, (4) AI for supporting decision making process for firms and consumers, (5) AI for risk mitigation and (6) AI marketing aspects. Then, the authors propose an interpretive framework composed of three main dimensions: (1) the two sides of AI: the “hard” side concerns the technology development and application while the “soft” side regards stakeholders' acceptance of the latter; (2) level of analysis: firm and inter-firm; (3) the impact of AI on value chain activities in the agri-food sector.

This study provides interpretive insights into the extant literature on AI in the agri-food sector, paving the way for future research and inspiring practitioners of different AI approaches in a traditionally low-tech sector.

The purpose of this study is to analyse the gender wage gap (GWG) in Spain adopting a labour market segmentation approach. Once we obtain the different labour segments (or idiosyncratic labour markets), we are able to decompose the GWG into its observed and unobserved heterogeneity components.

We use the data from the Continuous Sample of Working Lives for the year 2021 (matched employer–employee [EE] data). Contingency tables and clustering techniques are applied to employment data to identify idiosyncratic labour markets where men and/or women of different ages tend to match/associate with different sectors of activity and occupation groups. Once this “heatmap” of labour associations is known, we can analyse its hottest areas (the idiosyncratic labour markets) from the perspective of wage discrimination by gender (Oaxaca-Blinder model).

In Spain, in general, men are paid more than women, and this is not always justified by their respective attributes. Among our results, the fact stands out that women tend to move to those idiosyncratic markets (biclusters) where the GWG (in favour of men) is smaller.

It has not been possible to obtain remuneration data by job-placement, but an annual EE relationship is used. Future research should attempt to analyse the GWG across the wage distribution in the different idiosyncratic markets.

Our combination of methodologies can be adapted to other economies and variables and provides detailed information on the labour-matching process and gender wage discrimination in segmented labour markets.

Our contribution is very important for labour market policies, trying to reduce unfair inequalities.

The study of the GWG from a novel labour segmentation perspective can be interesting for other researchers, institutions and policy makers.