A Decision-Making Model for Adopting a Cloud Computing System

A Decision-Making Model for Selecting Product Suppliers in Crop Protection Retail Sector

This study aims to determine the importance of factors affecting supplier selection in the pesticide distribution sector as a global emerging market and present a decision-making model for the corporate marketing strategy. Specifically, a comparative study between suppliers and retail distribution experts was conducted to compare differences in the perception of supplier selection factors according to organizational characteristics. Based on previous studies, a decision-making model based on the AHP methodology was constructed with a total of 20 factors in five areas: product quality, price, flexibility, promotion support, and brand. Then, 42 Korean experts were surveyed to measure the importance of these factors. The results showed that product quality is the most critical factor in supplier selection, followed by price, brand, promotional support, and flexibility, in that order. Manufacturers consider product quality as the most important factor, while retailers consider price as the most important factor. Among the 20 factors, ‘quality excellence’, ‘expected return’, and ‘technological competitiveness’ were found to be the most important factors. In addition, while manufacturers considered factors such as ‘corporate reputation’ and ‘corporate trust’ as more important, retailers considered factors related to product characteristics, such as ‘product awareness’ and ‘brand reputation’ as more important.

Decision-Making Model for Reinforcing Digital Transformation Strategies Based on Artificial Intelligence Technology

Firms’ digital environment changes and industrial competitions have evolved quickly since the Fourth Industrial Revolution and the COVID-19 pandemic. Many companies are propelling company-wide digital transformation strategies based on artificial intelligence (AI) technology for the digital innovation of organizations and businesses. This study aims to define the factors affecting digital transformation strategies and present a decision-making model required for digital transformation strategies based on the definition. It also reviews previous AI technology and digital transformation strategies and draws influence factors. The research model drew four evaluation areas, such as subject, environment, resource, and mechanism, and 16 evaluation factors through the SERM model. After the factors were reviewed through the Delphi methods, a questionnaire survey was conducted targeting experts with over 10 years of work experience in the digital strategy field. The study results were produced by comparing the data’s importance using an Analytic Hierarchy Process (AHP) on each group. According to the analysis, the subject was the most critical factor, and the CEO (top management) was more vital than the core talent or technical development organization. The importance was shown in the order of resource, mechanism and environment, following subject. It was ascertained that there were differences of importance in industrial competition and market digitalization in the demander and provider groups.

Optimization of Industry 4.0 Implementation Selection Process towards Enhancement of a Manual Assembly Line

Last year’s developments are characterized by a dramatic drop in customer demand leading to stiff competition and more challenges that each enterprise needs to cope with in a globalized market. Production in low-mix/high-volume batches is replaced with low-volume/high-variety production, which demands excessive information flow throughout production facilities. To cope with the excessive information flow, this production paradigm requires the integration of new advanced technology within production that enables the transformation of production towards smart production, i.e., towards Industry 4.0. The procedure that helps the decision-makers to select the most appropriate I4.0 technology to integrate within the current assembly line considering the expected outcomes of KPIs are not significantly been the subject of the research in the literature. Therefore, this research proposes a conceptual procedure that focus on the current state of the individual assembly line and proposes the technology to implement. The proposed solution is aligned with the expected strategic goals of the company since procedure takes into consideration value from the end-user perspective, current production plans, scheduling, throughput, and other relevant manufacturing metrics. The validation of the method was conducted on a real assembly line. The results of the validation study emphasize the importance of the individual approach for each assembly line since the preferences of the user as well as his diversified needs and possibilities affect the optimal technology selection.

Spatial and Temporal Exposure Assessment to PM2.5 in a Community Using Sensor-Based Air Monitoring Instruments and Dynamic Population Distributions

This research was to conduct a pilot study for two consecutive days in order to assess fine particulate matter (PM2.5) exposure of an entire population in a community. We aimed to construct a surveillance system by analyzing the observed spatio-temporal variation of exposure. Guro-gu in Seoul, South Korea, was divided into 2,204 scale grids of 100 m each. Hourly exposure concentrations of PM2.5 were modeled by the inverse distance weighted method, using 24 sensor-based air monitoring instruments and the indoor-to-outdoor concentration ratio. Population distribution was assessed using mobile phone network data and indoor residential rates, according to sex and age over time. Exposure concentration, population distribution, and population exposure were visualized to present spatio-temporal variation. The PM2.5 exposure of the entire population of Guro-gu was calculated by population-weighted average exposure concentration. The average concentration of outdoor PM2.5 was 42.1 µg/m3, which was lower than the value of the beta attenuation monitor measured by fixed monitoring station. Indoor concentration was estimated using an indoor-to-outdoor PM2.5 concentration ratio of 0.747. The population-weighted average exposure concentration of PM2.5 was 32.4 µg/m3. Thirty-one percent of the population exceeded the Korean Atmospheric Environmental Standard for PM2.5 over a 24 h average period. The results of this study can be used in a long-term aggregate and cumulative PM2.5 exposure assessment, and as a basis for policy decisions on public health management among policymakers and stakeholders.

Reducing Road Transport Emissions in Europe: Investigating A Demand Side Driven Approach †

The European Union aims at net-zero emissions by 2050. A key sector to achieve this goal is road transport, where emissions show no signs of reducing but continue to grow. A review of policies undertaken by EU member states and the G20 to reduce transport emissions reveals that both present and planned policies focus on binding supply-side measures, but offer only weak demand-side incentives. To address this imbalance, we developed a downstream, demand-side policy prototype through an expert interview design process. We call the prototype “cap-and-surrender” because it caps road emissions, and then allocates tradable emission allowances to individual vehicles that drivers surrender at each fill-up. Allowance pricing, both by the state and in the secondary market, is designed to incentivize decarbonization of the sector. Though the system would require significant investment, its revenue potential to the state should exceed this investment by several multiples. We discuss the potential economic, environmental and social impacts of the policy, as assessed by European transport experts. We find that the approach can deliver significant transport emission reductions in an effective and economically efficient manner. Through the appropriate design of national allocation rules and a gradual phasing in of cap and surrender, potential negative social consequences can be mitigated, and public acceptance of the policy promoted.

Artificial Intelligence in the Agri-Food System: Rethinking Sustainable Business Models in the COVID-19 Scenario

The aim of the paper is to investigate the artificial intelligence (AI) function in agri-food industry, as well as the role of stakeholders in its supply chain. Above all, from the beginning of the new millennium, scholars and practitioners have paid an increasing attention to artificial intelligence (AI) technologies in operational processes management and challenges for new business models, in a sustainable and socially responsible perspective. Thus, the stakeholders can assume a proactive or marginal role in the value creation for business, according to their own environmental awareness. These issues appear still “open” in some industries, such as the agri-food system, where the adoption of new technologies requires rethinking and redesigning the whole business model. Methodologically, we brought forward an in-depth review of the literature about major articles in this field. Especially, the study has been conducted following two phases: firstly, we extracted from scientific databases (Web of Science, Scopus, and Google Scholar) and studied relevant articles; secondly, we analyzed the selected articles. The findings highlight interesting issues about AI towards a “space economy” to achieve sustainable and responsible business models, also in the perspective of the COVID-19 pandemic scenario. Theoretical and managerial implications are discussed.

Sustainable Cloud Service Provider Development by a Z-Number-Based DNMA Method with Gini-Coefficient-Based Weight Determination

The sustainable development of cloud service providers (CSPs) is a significant multiple criteria decision making (MCDM) problem, involving the intrinsic relations among multiple alternatives, (quantitative and qualitative) decision criteria and decision-experts for the selection of trustworthy CSPs. Most existing MCDM methods for CSP selection incorporated only one normalization technique in benefit and cost criteria, which would mislead the decision results and limit the applications of these methods. In addition, these methods did not consider the reliability of information given by decision-makers. Given these research gaps, this study introduces a Z-number-based double normalization-based multiple aggregation (DNMA) method to tackle quantitative and qualitative criteria in forms of benefit, cost, and target types for sustainable CSP development. We extend the original DNMA method to the Z-number environment to handle the uncertain and unreliability information of decision-makers. To make trade-offs between normalized criteria values, we develop a Gini-coefficient based weighting method to replace the mean-square-based weighting method used in the original DNMA method to enhance the applicability and isotonicity of the DNMA method. A case study is conducted to demonstrate the effectiveness of the proposed method. Furthermore, comparative analysis and sensitivity analysis are implemented to test the stability and applicability of the proposed method.

Assisting Sustainable Entrepreneurial Activities Through the Analysis of Mobile IT Services’ Success and Failure Factors

With information technology (IT) now showing advanced capabilities, many new services are being introduced to consumers of smartphones through the various available app stores. Moreover, the recent proliferation of such services related to information and communications technology has seen a momentous rise. Despite this trend, the ever-changing landscape of mobile IT services is creating a serious problem for businesses who are already experiencing fierce market conditions. Thus, in order to maintain the sustainability of an enterprise, it is necessary to make an adequate analysis of the success and failure factors of IT services in order to create a sustained competitive advantage. Considering 22 real IT service cases based on two platform models (merchant model and two-sided model) and through surveys submitted to 11 experienced entrepreneurs in IT services, we conducted a t-test analysis in order to first assess the success and failure factors of the IT service cases. Next, we performed a logistic regression analysis in order to find underlying relationships of our hypothesized model. The results showed that the participants identified 141 success and 101 failure factors in total with the t-tests, confirming that the distinction between success and failure of each IT service assessed was significant. Next, the results from the logistic regression showed which relationships were the best on the basis of the given platform model. Overall, this study was able to identify the main factors that have an influence on the success and failure of IT services based on two identified platform models. In doing so, this paper can help to inform future IT service entrepreneurs and researchers involved in developing new apps based on IT services by providing a guide to what factors need to be considered before going to market.

A privacy protection approach for XML-based archives management in a cloud environment

Purpose

The security of archival privacy data in the cloud has become the main obstacle to the application of cloud computing in archives management. To this end, aiming at XML archives, this paper aims to present a privacy protection approach that can ensure the security of privacy data in the untrusted cloud, without compromising the system availability.

Design/methodology/approach

The basic idea of the approach is as follows. First, the privacy data before being submitted to the cloud should be strictly encrypted on a trusted client to ensure the security. Then, to query the encrypted data efficiently, the approach constructs some key feature data for the encrypted data, so that each XML query defined on the privacy data can be executed correctly in the cloud.

Findings

Finally, both theoretical analysis and experimental evaluation demonstrate the overall performance of the approach in terms of security, efficiency and accuracy.

Originality/value

This paper presents a valuable study attempting to protect privacy for the management of XML archives in a cloud environment, so it has a positive significance to promote the application of cloud computing in a digital archive system.

Cloud Computing Research Profiling: Mapping Scholarly Community and Identifying Thematic Boundaries of the Field

The aim of the study was to map the scholarly community interested in research on cloud computing and to identify thematic boundaries of the field. The methodology of research profiling, representing bibliometric descriptive studies, was applied to achieve the aim of the study. Using research profiling for mapping the cloud computing field can be considered as an innovation. Although the research profiling methodology has been widely used across various subject areas, including Computer Science, Social Sciences, Engineering, Arts and Humanities, Business, Management and Accounting, and Psychology, thus far neither Scopus nor Web of Science indexed publications including the conjunction of phrases “cloud computing” and “research profiling” in their titles, keywords and abstracts. The previous important scientometric study of the research output in the field was published by Heilig and Voß in 2014. Taking into account a very dynamic growth of the field, all this indicates the research gap to be filled. The research sample is made of 14,158 publications indexed in Scopus database comprising the phrase “cloud computing” in their titles. The study was purposely limited to the title search to concentrate the attention of publications relating directly to the issue of cloud computing. Applying the quantitative approach provides an opportunity for broad scanning of subject-related literature. First, general publication profiling recognized the main contributors (countries, research intuitions, source titles and authors) to the scholarly community interested in cloud computing. Secondly, subject area profiling was applied to find how multidisciplinary is the research in the field and how the research output is distributed across subject areas. Finally, topic profiling unveiled leading topics of studies in the field and their distribution by authors, journal, subject areas and core references.

MODELLING THE EFFICIENCY OF THE CLOUD COMPUTING IMPLEMENTATION AT ENTERPRISES

The article describes the main characteristics, types and properties of cloud computing. The most widespread cloud technologies in Ukraine are analyzed. It is identified that the largest share among users of cloud technologies in Ukraine currently belong to large holdings, IT companies, commercial enterprises and banks, but other sectors of business are also involved in the development of these services. The aim of the article is to develop the methodology for evaluating the efficiency of cloud technologies implementation at enterprises and its experimental verification. The economic component of the cloud computing implementation at enterprises (expenditures and revenues of both cloud technology owners and users) is considered. The efficiency of using cloud computing at enterprises is proved. It is found that organizations usually do not use the power of their personal data centers to a full extent. This leads to idle equipment, extra cost on maintenance and servicing of hardware, amortization, staff salaries and etc. The feasibility of transition of enterprises to cloud computing in such situations has been proved, which considerably reduce the costs of the enterprise due to the absence of need for hardware and necessary staff to support the operation of information systems. Usability of the methodology of total cost of ownership in evaluating the effectiveness of using services for the enterprise has been proved. The proposed methodology compares the main costs of using personal data centers and the cost of using cloud computing. It is experimentally proven that in most cases, the cost of maintaining personal data center (PDC) is higher than the cost of cloud services. It is also proved that the efficiency of cloud technology operation depends on the internal structure and organization of computing processes inside the systems, as well as on external factors such as the size of enterprises-clients, industries, costs for the organization of data centers, etc. Cloud computing is an advanced technology which has future prospects and is cost-effective for both enterprise users and provider organizations.

Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review

With the rapid development of sensing, communication, computing technologies, and analytics techniques, today’s manufacturing is marching towards a new generation of sustainability, digitalization, and intelligence. Even though the significance of both sustainability and intelligence is well recognized by academia, industry, as well as governments, and substantial efforts are devoted to both areas, the intersection of the two has not been fully exploited. Conventionally, studies in sustainable manufacturing and smart manufacturing have different objectives and employ different tools. Nevertheless, in the design and implementation of smart factories, sustainability, and energy efficiency are supposed to be important goals. Moreover, big data based decision-making techniques that are developed and applied for smart manufacturing have great potential in promoting the sustainability of manufacturing. In this paper, the state-of-the-art of sustainable and smart manufacturing is first reviewed based on the PRISMA framework, with a focus on how they interact and benefit each other. Key problems in both fields are then identified and discussed. Specially, different technologies emerging in the 4th industrial revolution and their dedications on sustainability are discussed. In addition, the impacts of smart manufacturing technologies on sustainable energy industry are analyzed. Finally, opportunities and challenges in the intersection of the two are identified for future investigation. The scope examined in this paper will be interesting to researchers, engineers, business owners, and policymakers in the manufacturing community, and could serve as a fundamental guideline for future studies in these areas.