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This paper elicits information, an excess of information and the stakeholders’ role in the use of information. The purpose of this paper is to create a “C5 model” (clients, connectivity, content, copyright and cost) for the consortium management to support and improve its academic and administrative decision-making skills and optimize returns.

The methodology used for this study was “strength, weakness, opportunity and threat” (SWOT) analysis. As a tool, it was used as one of the strategic analytical techniques to evaluate various aspects of consortium management. The purpose of choosing this method was to derive a set of strategies for comparing the internal (strengths/weaknesses) and external (opportunities/threats) forces of the “C5 model” of consortium management.

From the SWOT analysis of the “C5 model,” it was observed that four combinations, maxi-maxi (strengths/opportunities (SO), mini-maxi (weaknesses/opportunities (WO), maxi-mini (strengths/threats (ST)) and mini-mini (weaknesses/threats (WT)), were drawn to derive a set of strategies to benefit the consortium management. The SO strategies such as building a client-centric resource management system, centralized gateway, collaborative acquisition model, common copyright communication and clearance center and cost-benefit analysis were built on the basis of the strengths and opportunities identified. Likewise, the WO strategies such as organizational planning, equality in ICT infrastructure facilities, quality content, public disclosure of copyright obligations and effective economic models were drawn to minimize the weaknesses of each component by taking advantage of the identified opportunities. The ST strategies were worked out using strengths of the model to possibly mitigate its threats. Similarly, the WT strategies were created to minimize the weaknesses and avoid the threats as much as possible. However, these strategies would not only help consortium management in the decision-making process, but also optimize returns and improve the intellectual outcome of organizations.

Not many research studies have been developed regarding the SWOT analysis of the “C5 model” for consortium management. This study attempts to benefit a large community of library and information science professionals, consortia and their clients.

Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

The purpose of the food traceability is used to retain the good quality of raw material supply, diminish the loss and reduced system complexity.

The proposed hybrid algorithm is for food traceability to make accurate predictions and enhanced period data. The operation of the internet of things is addressed to track and trace the food quality to check the data acquired from manufacturers and consumers.

In order to survive with the existing financial circumstances and the development of global food supply chain, the authors propose efficient food traceability techniques using the internet of things and obtain a solution for data prediction.

The operation of the internet of things is addressed to track and trace the food quality to check the data acquired from manufacturers and consumers. The experimental analysis depicts that proposed algorithm has high accuracy rate, less execution time and error rate.

The purpose of this paper is to propose a new simplified numerical model, based on a very compact semi-empirical formulation, able to simulate the fluid dynamics behaviors of an electrohydraulic servovalve taking into account several effects due to valve geometry (e.g. flow leakage between spool and sleeve) and operating conditions (e.g. variable supply pressure or water hammer).

The proposed model simulates the valve performance through a simplified representation, deriving from the linearized approach based on pressure and flow gains, but able to evaluate the mutual interaction between boundary conditions, pressure saturation and leak assessment. Its performance was evaluated comparing with other fluid dynamics numerical models (a detailed physics-based high-fidelity one and other simplified models available in the literature).

Although still showing some limitations attributable to its simplified formulation, the proposed model overcomes several deficiencies typical of the most common fluid dynamic models available in the literature, describing the water hammer and the nonlinear dependence of the delivery differential pressure with the spool displacement.

Although still based on a simplified formulation with reduced computational costs, the proposed model introduces a new nonlinear approach that, approximating with suitable precision the pressure-flow fluid dynamic characteristic of a servovalve, overcomes the shortcomings typical of such models.

Determining the relevant information using financial measures is of great interest for various stakeholders to analyze the performance of the firm. This paper aims at identifying these financial measures (ratios) which critically affect the firm performance. The authors specifically focus on discovering the most prominent ratios using a two-step process. First, the authors use an exploratory factor analysis to identify the underlying dimensions of these ratios, followed by predictive modeling techniques to identify the potential relationship between measures and performance.

The study uses data of 25 financial variables for a sample of 1923 Indian manufacturing firms which exist continuously between 2011 and 2018. For prediction models, four popular decision tree algorithms [Chi-squared automatic interaction detector (CHAID), classification and regression trees (C&RT), C5.0 and quick, unbiased, efficient statistical tree (QUEST)] were investigated, and the information fusion-based sensitivity analyses were performed to identify the relative importance of these input measures.

Results show that C5.0 and CHAID algorithms produced the best predictive results. The fusion sensitivity results find that net profit margin and total assets turnover rate are the most critical factors determining the firm performance in an Indian manufacturing context. These findings may enable managers in their decision-making process and also have vital implications for investors in assessing the performance of the firm.

To the best of the authors’ knowledge, the current paper is the first to address the application of decision tree algorithms to predict the performance of manufacturing firms in an emerging economy such as India, with the latest data. This practical perspective helps the organizations in managing the critical parameters for the firm’s growth.

Fish quarantine is a measure to prevent the entry and spread of quarantine fish pests and diseases abroad and from one area to another within Indonesia's territory. Based on these backgrounds, this study aims to identify the knowledge, knowledge management (KM) processes and knowledge management system (KMS) priority needs for quarantine fish and other fishery products measures (QMFFP) and then develop a classification model and web-based decision support system (DSS) for QMFFP decisions.

This research methodology uses combination approaches, namely, contingency factor analysis (CFA), the cross-industry standard process for data mining (CRISP-DM) and knowledge management system development life cycle (KMSDLC). The CFA for KM solution design is performed by identifying KM processes and KMS priorities. The CRISP-DM for decision classification model is done by using a decision tree algorithm. The KMSDLC is used to develop a web-based DSS.

The highest priority requirements of KM technology for QMFFP are data mining and DSS with predictive features. The main finding of this study is to show that web-based DSS (functions and outputs) can support and accelerate QMFFP decisions by regulations and field practice needs. The DSS was developed using the CTree algorithm model, which has six main attributes and eight rules.

This study proposes a novel comprehensive framework for developing DSS (combination of CFA, CRISP-DM and KMSDLC), a novel classification model resulting from comparing two decision tree algorithms and a novel web-based DSS for QMFFP.

The purpose of this paper is to consider the question of what is an appropriate enterprise resource planning (ERP) reference model for specifying areas of change in the context of IT-driven ERP implementation and through the model matching. There are other implicit goals to increasing the awareness of the reference models, as this highlights the principles embedded in ERP systems and explains the classification of reference models, which is useful in terms of the reuse of knowledge.

In this paper, a comparison between ERP reference models is conducted using a suitable decision-making technique and the final results are discussed. The comparison depends on nine criteria related to conceptual ERP reference models: scope, abstraction, granularity, views, purpose, simplicity, availability, ease of use for model matching, and target audience.

This study concludes that the business process reference model is best for specifying areas of change in the context of IT-driven ERP implementations. The final ranking of the alternatives based on all criteria places the system organizational model second, followed by the function and data/object reference models, in that order.

This paper is one of very few studies on the selection of appropriate ERP reference models according to the ERP implementation approach and model matching factors. This research also provides an in-depth analysis of various ERP reference model types.

This chapter investigates whether traditional design-oriented coordination instruments or more modern management concepts have a stronger influence on the success of forward technology transfers within MNCs.

We conducted an empirical study analyzing the relative influence of (a) traditional coordination instruments (structural, technocratic, and person-oriented) and (b) modern management concepts (epistemic community and absorptive capacity) on the success of forward technology transfers within MNCs.

The study finds evidence that the traditional coordination instruments relate to specific aspects of the success of such transfers. Comparing the different types of coordination instruments, this chapter shows that not only the person-oriented, but also the structural and technocratic coordination instruments relate positively with the achievement of technology transfer goals. The study finds stronger relationships between the traditional coordination instruments and the technology transfer goals than between the modern management concepts and the technology transfer goals.

We believe that these results have important implications for the management of international technology transfers in particular and for the focus of future (international) management research in general. Future MNC research studies need to include traditional coordination instruments, since they continue to strongly influence organizational behavior and outcomes. This would help to make organizational research on MNCs more cumulative and complete.