A framework for the successful implementation of food traceability systems in China

Implementation of food traceability systems in China faces many challenges due to the scale, diversity and complexity of China’s food supply chains. This study aims to identify critical success factors specific to the implementation of traceability systems in China. Twenty-seven critical success factors were identified in the literature. Interviews with managers at four food enterprises in a pre-study helped identify success criteria and five additional critical success factors. These critical success factors were tested through a survey of managers in eighty-three food companies. This study identifies six dimensions for critical success factors: laws, regulations and standards; government support; consumer knowledge and support; effective management and communication; top management and vendor support; and information and system quality

Investigating the Barriers to Quality 4.0 Adoption in the Indian Manufacturing Sector: Insights and Implications for Industry and Policymaking

Purpose: The research explores the shift to Quality 4.0, examining the move towards a data-focused transformation within organizational frameworks. This transition is characterized by incorporating Industry 4.0 technological innovations into existing quality management frameworks, signifying a significant evolution in quality control systems. Despite the evident advantages, the practical deployment in the Indian manufacturing sector encounters various obstacles. This research is dedicated to a thorough examination of these impediments. It is structured around a set of pivotal research questions: Firstly, it seeks to identify the key barriers that impede the adoption of Quality 4.0. Secondly, it aims to elucidate these barriers' interrelations and mutual dependencies. Thirdly, the research prioritizes these barriers in terms of their significance to the adoption process. Finally, it contemplates the ramifications of these priorities for the strategic advancement of manufacturing practices and the development of informed policies. By answering these questions, the research provides a detailed understanding of the challenges faced. It offers actionable insights for practitioners and policymakers implementing Quality 4.0 in the Indian manufacturing sector. Design/methodology/approach: Employing Interpretive Structural Modelling (ISM) and Matrix Impact of Cross Multiplication Applied to Classification (MICMAC), we probe the interdependencies amongst fourteen identified barriers inhibiting Quality 4.0 adoption. These barriers were categorised according to their driving power and dependence, providing a richer understanding of the dynamic obstacles within the Technology-Organization-Environment (TOE) framework. Findings: The study results highlight the lack of Quality 4.0 standards and Big Data Analytics (BDA) tools as fundamental obstacles to integrating Quality 4.0 within the Indian manufacturing sector. Additionally, the study results contravene dominant academic narratives, suggesting that the cumulative impact of organisational barriers is marginal, contrary to theoretical postulations emphasising their central significance in Quality 4.0 assimilation. Originality: This research delineates specific obstacles to Quality 4.0 adoption by applying the TOE (Technology-Organization-Environment) framework, detailing how these barriers interact with and influence each other, particularly highlighting the previously overlooked environmental factors. The analysis reveals a critical interdependence between 'Lack of standards for Quality 4.0' and 'Lack of standardised Big Data Analytics (BDA) tools and solutions', providing nuanced insights into their conjoined effect on stalling progress in this field. Moreover, the study contributes to the theoretical body of knowledge by mapping out these novel impediments, offering a more comprehensive understanding of the challenges faced in adopting Quality 4.0. Practical implications: This research provides concrete strategies, such as developing a collaborative platform for sharing best practices in Quality 4.0 standards, which fosters a synergistic relationship between organizations and policymakers, for instance, by creating a joint task force, comprised of industry leaders and regulatory bodies, dedicated to formulating and disseminating comprehensive guidelines for Quality 4.0 adoption. This initiative could lead to establishing industry-wide standards, benefiting from the pooled expertise of diverse stakeholders. Additionally, the study underscores the necessity for robust, standardized Big Data Analytics tools specifically designed to meet the Quality 4.0 criteria, which can be developed through public-private partnerships. These tools would facilitate the seamless integration of Quality 4.0 processes, demonstrating a direct route for overcoming the barriers of inadequate standards

Moving enterprise resource planning (ERP) systems to the cloud: the challenge of infrastructural embeddedness

Cloud enterprise resource planning (ERP) solutions allow organizations to support and coordinate key business processes by leveraging virtualization. Nevertheless, moving ERPs to the cloud is not straightforward, and organizational cloud ERP initiatives raise multiple concerns. We conducted an in-depth systematic review of relevant research literature and identified six key concerns related to cloud ERP implementation: a) the introduction of new ERP work arrangements, b) the migration of legacy data, c) the assurance of compliance with extant rules and regulations for security, d) the continuous alignment between ERP functionality and business processes, e) the ongoing integration between ERPs and the rest of the organization’s application portfolio, and f) the establishment of adequate reliability levels. The identified concerns are associated with both transition management and operations supported by cloud ERPs. All the identified concerns are also related to the need to achieve infrastructural embeddedness. This need sets ERPs apart from other types of cloud-based applications, such as office automation solutions that do not have as many dependencies and exchanges with other systems and repositories within an organization’s information infrastructure. We argue that the challenge of embeddedness has different implications for organizations of different sizes, and we call for further empirical research

Moving enterprise resource planning (ERP) systems to the cloud: the challenge of infrastructural embeddedness

Cloud enterprise resource planning (ERP) solutions allow organizations to support and coordinate key business processes by leveraging virtualization. Nevertheless, moving ERPs to the cloud is not straightforward, and organizational cloud ERP initiatives raise multiple concerns. We conducted an in-depth systematic review of relevant research literature and identified six key concerns related to cloud ERP implementation: a) the introduction of new ERP work arrangements, launch b) the migration of legacy data, c) the assurance of compliance with extant rules and regulations for security, d) the continuous alignment between ERP functionality and business processes, e) the ongoing integration between ERPs and the rest of the organization’s application portfolio, and f) the establishment of adequate reliability levels. The identified concerns are associated with both transition management and operations supported by cloud ERPs. All the identified concerns are also related to the need to achieve infrastructural embeddedness. This need sets ERPs apart from other types of cloud-based applications, such as office automation solutions that do not have as many dependencies and exchanges with other systems and repositories within an organization’s information infrastructure. We argue that the challenge of embeddedness has different implications for organizations of different sizes, and we call for further empirical research

Moving enterprise resource planning (ERP) systems to the cloud: the challenge of infrastructural embeddedness

Cloud enterprise resource planning (ERP) solutions allow organizations to support and coordinate key business processes by leveraging virtualization. Nevertheless, moving ERPs to the cloud is not straightforward, and organizational cloud ERP initiatives raise multiple concerns. We conducted an in-depth systematic review of relevant research literature and identified six key concerns related to cloud ERP implementation: a) the introduction of new ERP work arrangements, launch b) the migration of legacy data, c) the assurance of compliance with extant rules and regulations for security, d) the continuous alignment between ERP functionality and business processes, e) the ongoing integration between ERPs and the rest of the organization’s application portfolio, and f) the establishment of adequate reliability levels. The identified concerns are associated with both transition management and operations supported by cloud ERPs. All the identified concerns are also related to the need to achieve infrastructural embeddedness. This need sets ERPs apart from other types of cloud-based applications, such as office automation solutions that do not have as many dependencies and exchanges with other systems and repositories within an organization’s information infrastructure. We argue that the challenge of embeddedness has different implications for organizations of different sizes, and we call for further empirical research.publishedVersio

A Model to Evaluate the Organizational Readiness for Big Data Adoption

Evaluating organizational readiness for adopting new technologies always was an important issue for managers. This issue for complicated subjects such as Big Data is undeniable. Managers tend to adopt Big Data, with the best readiness. But this is not possible unless they can assess their readiness. In the present paper, we propose a model to evaluate the organizational readiness for Big Data adoption. To accomplish this objective, firstly, we identified the criteria that impact organizational readiness based on a comprehensive literature review. In the next step using Principal Component Analysis (PCA) for criterion reduction and integration, twelve main criteria were identified. Then the hierarchical structure of criteria was developed. Further, Fuzzy Best- Worst Method (FBWM) has been used to identify the weight of the criteria. The finding enables decision-makers to appropriately choose the more important criteria and drop unimportant criteria in strengthening organizational readiness for Big Data adoption. Statistics-based hierarchical model and MCDM based criteria weighting have been proposed, which is a new effort in evaluating organizational readiness for Big Data adoption

Risks and Uncertainties in Citizens’ Trust and Adoption of E-Government: A Proposed Framework

This paper presents a conceptual framework to identify risks and uncertainty as relevant factors for assessing citizens’ trusts and their adoption intention of e-government. To strengthen the arguments on the effects of risk aversion and uncertainty avoidance on trust in the adoption intention of egovernment, a research model grounded in trust, perceived risk and uncertainty, risk aversion and uncertainty avoidance framework is proposed based on a review of an extensive literature. This study will be conducted by using an online survey questionnaire. The study findings are expected to enhance our knowledge on the factors associated with citizen’s intention to adopt e-government

Risk and Compliance Management for Cloud Computing Services: Designing a Reference Model

More and more companies are making use of Cloud Computing Services in order to reduce costs and to increase theflexibility of their IT infrastructures. Currently, the focus is shifting towards problems of risk and compliance which includeas well the realm of Cloud Computing security. For instance, since the storage locations of data may shift or remain unknownto the user, the problem of the applicable jurisdiction arises and impede the adoption and management of Cloud ComputingServices. Therefore, companies need new methods to avoid being fined for compliance violations, to manage risk factors aswell as to manage processes and decision rights. This paper presents a reference model that serves to support companies inmanaging and reducing risk and compliance efforts. We developed the model on the solid basis of a systematic literaturereview and practical requirements by analyzing Cloud Computing Service offers

Evaluation Theory for Characteristics of Cloud Identity Trust Framework

Trust management is a prominent area of security in cloud computing because insufficient trust management hinders cloud growth. Trust management systems can help cloud users to make the best decision regarding the security, privacy, Quality of Protection (QoP), and Quality of Service (QoS). A Trust model acts as a security strength evaluator and ranking service for the cloud and cloud identity applications and services. It might be used as a benchmark to setup the cloud identity service security and to find the inadequacies and enhancements in cloud infrastructure. This chapter addresses the concerns of evaluating cloud trust management systems, data gathering, and synthesis of theory and data. The conclusion is that the relationship between cloud identity providers and Cloud identity users can greatly benefit from the evaluation and critical review of current trust models