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Selection of the design concepts is one of the significant activities in product development process. Most of the products are usually failed due to inappropriate decision during the selection of the design concepts at the early stage of product development process. The determination of the greatest selection of design concepts at the conceptual design stage is a crucial decision due to a poor design concept which can never be compensated for by a good detailed design and it will implicate great expense of redesign cost. Analytical Hierarchy Process (AHP) is one available method in forming a systematic approach for a single decision maker or a group decision maker, is employed to solve such problem. In this paper, 7 design concepts of wheelchair designs were considered as a case study. The AHP through utilizing Expert Choice software was implemented to determine the most suitable design concept of wheelchair design at the conceptual design stage. The sensitivity analysis was performed to test the stability of the priority ranking and to increase the confidence in the selection of design concepts.

In India, 52% of e-commerce consumers actively engage in hyperlocal commerce, representing a substantial increase in online purchasing and hyperlocal services over the past two years. How individuals discover, select, and acquire products and services has been significantly altered due to the COVID-19 pandemic. Digital payments and contactless deliveries have become indispensable in the current dynamic environment, affording consumers enhanced security and convenience. Radio-frequency identification (RFID) technology has enabled the widespread adoption of contactless payments, including Europay, MasterCard, and Visa (EMV) chip cards, which eliminate the need for customers to swipe their cards during physical transactions. Particularly in the fast food, pharmaceutical, and supermarket industries, the symbiotic relationship between e-commerce and contactless payments has experienced significant expansion in India. This chapter investigates the synergies between cashless, contactless, and autonomous purchasing in the digital age. This research examines these technologies’ reciprocal reinforcement and interconnections, focusing on their profound influence on retail transactions and purchasing experiences. The progressive development of payment systems is evidenced by the expanding prevalence of contactless transactions, facilitated by innovations such as the “tap to pay” feature partnership between Paytm and Kotak Mahindra. The year 2023 is anticipated to witness several significant developments, including the expansion of virtual retail, the push for contactless transactions, and the establishment of initiatives such as the open network for digital commerce. The range of applications for frictionless, contactless, and autonomous shopping is extensive, encompassing mobile payment applications such as Apple Pay and Google Pay and proprietary apps developed by retailers to enhance the purchasing experience.

The purpose of this paper is to propose an integrative approach for improving failure modes and effects analysis (FMEA).

An extensive literature review on FMEA has been performed. Then, an integrative approach has been proposed based on literature review. The proposed approach is an integration of FMEA and quality function deployment (QFD). The proposed approach includes a two-phase QFD. In the first phase, failure modes are prioritized based on failure effects and in the second phase, failure causes are prioritized based on failure modes. The proposed approach has been examined in a case example at the blast furnace operation of a steel-manufacturing company.

Results of the case example indicated that stove shell crack in hot blast blower, pump failure in cooling water supply pump and bleeder valves failed to operate are the first three important failure modes. In addition, fire and explosion are the most important failure effects. Also, improper maintenance, over pressure and excess temperature are the most important failure causes. Findings also indicated that the proposed approach with the consideration of interrelationships among failure effects, failure mode and failure causes can influence and adjust risk priority number (RPN) in FMEA.

As manufacturing departments are mostly dealing with failure effects and modes of machinery and maintenance departments are mostly dealing with causes of failures, the proposed model can support better coordination and integration between the two departments. Such support seems to be more important in firms with continuous production lines wherein line interruption influences response to customers more seriously. A wide range of future study opportunities indicates the attractiveness and contribution of the subject to the knowledge of FMEA.

Although the literature indicates that in most of studies the outcomes of QFD were entered into FMEA and in some studies the RPN of FMEA was entered into QFD as importance rating, the proposed approach is a true type of the so-called “integration of FMEA and QFD” because the three main elements of FMEA formed the structure of QFD. In other words, the proposed approach can be considered as an innovation in the FMEA structure, not as a data provider prior to it or a data receiver after it.

The purpose of this study is to provide human resource (HR) practitioners of multinational companies aspiring to invest in these two countries with guidelines for attaining organizational effectiveness through people.

This study develops and tests a multiple criteria decision-making model with data collected in the banking sectors of India and Mozambique. It compares the job engagement, team building and innovation strategy preferences of Indian personnel with those of Mozambican employees.

The findings of the study reveal the differences in the perceptions of the respondents of both countries regarding the importance of the strategies for organizational effectiveness.

Despite several contributions, the study has certain limitations too. Although utmost care was taken to avoid the issue of common method variance, the cross-sectional self-reported design of the study might be adversely affected by common method bias (MacKenzie and Podsakoff, 2012). Hence, future research might be conducted using different designs, such as diary studies or longitudinal studies. Future research might also be conducted making use of organizational productivity case studies to demonstrate the practicability of customizing the HR strategies using the multi-attribute decision-making approach.

This body of work is an addition to the existing literature on cross-national studies in the field of HR management (HRM) and adds to the limited literature on HRM in the least developed countries. The study is designed to provide guidelines for the HR practitioners of multi-national companies in these two countries to help them achieve enhanced organizational effectiveness. This should be of particular interest to the HR managers of the Indian companies aspiring to invest in Mozambique.

Research in the area of HRM is mainly limited to the developed and developing nations, with very few studies centering on emerging economies. While most cross-national studies on organizational effectiveness are also largely focused on developed and developing nations, this study is unusual, in that its focus is on a fast-developing nation (India) and an emerging economy (Mozambique).

The selection of an international market (IMS) is a prime factor in the success and growth of a company. Therefore, the purpose of this study is to consolidate and apply a systematic methodology that contributes toward the evaluation of international markets and promotes entry into the export market of Antioquia’s textile companies.

Through a systematic literature review, the criteria and sub-criteria involved in the IMS process are identified and a total of 5 general criteria and 23 sub-criteria are selected. A hybrid approach is used to address the gap. In total, a multiple case study of 11 companies from different range of export values are selected. Data analysis is conducted using two multiple criteria decision-making (MCDM) models, namely, the analytic hierarchy process for weighting the factors and the technique for order of preference by similarity to the ideal solution for the country selection ranking.

The results demonstrate the applicability of the hybrid MCDM technique to improve IMS decision-making in the textile sector and other sectors. It is found that Canada, Belgium and the UK are the best destinations for textile exports with a selection score of 0.7716, 0.7488 and 0.7337, respectively. The sub-criteria belonging to the dimensions of trade barriers, economic factors and costs are the main factors affecting the export of a textile-clothing product.

The possibility of achieving a generalized result through this case study is not possible, but the methodological application carried out is a novel for the selection of markets in the Colombian case and within the literature available in the domain.

From the managerial point of view, firms associated with trade have a broader vision when looking for new markets. Emerging entrepreneurs can equip themselves to enter the international market. Practitioners and policymakers can also use this methodology, which will allow them to evaluate new markets to outline promotional strategies for positioning products abroad.

To facilitate the selection of international markets for enterprises.

The contribution of the study is twofold. First, the combination of techniques will allow wider support for the selection of markets and act as a decision support system. On the other hand, this is the first time that such a methodology is used for IMS in the exporting sector not only in Colombia but also in Latin America. Finally, the detailed methodological process described in the study allows both academicians and decision-makers to replicate the study in other contexts and scenarios.

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).

Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.

Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.

Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.

Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.

Investigating antecedents that may affect the usage of electronic tax declaration platforms is not a new research topic. Nevertheless, investigating the antecedents that affect the continuance usage of these platforms beyond the COVID-19 pandemic is a relatively new research trend. Accordingly, the purpose of this study is to investigate the antecedent factors that directly influence electronic tax declaration platforms’ continuance usage in the post-pandemic era by integrating the Unified Theory of Acceptance and Use Technology Model with other related factors.

To achieve the main purpose of the study, an online questionnaire was used to gather data from 231 Jordanian taxpayers.

The findings revealed the positive and significant effects of performance expectancy, social influence, knowledge and trust in e-government services on continuance usage of electronic tax declaration platforms. Contrary to what is expected, effort expectancy is insignificant, and hence the related hypothesis (H2) was rejected.

The findings of current research provided theoretical and practical implications by using a well-established theoretical lens to investigate what factors affect the continuance usage of electronic tax declaration platforms within the context of a developing country, namely, Jordan.

Smart cities are concepts much loved by politicians and technologists but are very difficult to bring about in practice. There are many isolated applications in cities such as operating streetlamps, but very few, if any, examples of integrated applications sharing data and managing the city as a holistic entity rather than a set of disparate and unconnected applications. This is despite hundreds of trials and indicates how difficult bringing about a smart city will be. The key challenge is the wide range of interested parties in a city including the elected city authority, subcontractors and suppliers to the authority, emergency services, transport providers, businesses, residents, workers, tourists, and other visitors. Some of these entities will be primarily driven by finance, such as businesses and transport providers. Some will be driven by political considerations. Some will be concerned with the quality of life as well as financial costs. In some cases, there will be conflicting interests – the city may want as much information as possible on people in the city, whereas individuals may want privacy and the minimum data stored concerning their movements and attributes. COVID-19 does not change any of these issues, but it does increase the importance of some applications such as smart health, logistics, people surveillance, data security, and crisis management, while reducing the importance of others such as traffic management. It may result in more willingness for monitoring and data sharing if this can be shown to result in better control of the virus.

The aim of this paper is to improve the service quality of digital libraries (DLs) through an evaluation model for DLs' user interfaces. This evaluation model can provide a useful reference for existing DLs or relevant research.

Relevant literature was synthesized, an evaluation framework was established, and the Fuzzy AHP was employed to investigate the evaluation model for DLs' user interfaces, obtain the relative weights for the importance of the evaluation criteria, and establish the priority ranking for the criteria of DLs' user interfaces.

The top five criteria are ease of use, searching, language, presentation, and design, respectively. For users, an interface should be intuitive. Good interface presentation and design are critical when users evaluate a digital library.

All of the students, teachers and experts considered that the presentation and design of DL user interfaces were what users had had contact with in the very beginning, and they were critical influential factors in DL user interfaces. Furthermore, ease-of-use and searching were fundamental interface functions, as well as important evaluation criteria. Interfaces should also provide interactive functions to improve interaction, while tailor-made services should be taken into account to establish an excellent interface in order to meet each user's need.

The evaluation model for DLs' user interfaces employed in this study will help developers of DL user interfaces discover the criteria that they should aim for as a reference, perform comprehensive criteria evaluation according to their actual needs, and employ top criteria for evaluation.

Ovens are semi-industrial multipurpose equipment that are used to provide a desired temperature for specific chemical processes. Temperature regulation in the presence of different type of disturbances and dealing with nonlinear dynamics with large dead time (up to a few minutes) are some undesirable factors that have to be considered in the controller design procedure of the oven systems. Due to these factors, the classical PID controller tuned using Cohen-Coon or Ziegler–Nichol’s tuning methods often fails to meet satisfactory closed-loop performance.

In this paper, to deal with the limitations on the oven system due to the undesirable factors, a hierarchical automaton-guided form controller has been designed. The proposed controller includes several discrete PI controllers, each of which operates locally in the defined operating regions whose separation idea is specific to this paper. Based on the idea proposed in the separation of regions, the controller’s coefficients tuning rules are extracted prior to any determination. Then, a supervisor controller has assumed the task of switching between local controllers. In the next step, by considering a conceptual model for the oven system and using a candidate Lyapunov function, the stability conditions of closed-loop system are discussed and the necessary conditions for the asymptotic stability are derived. The proposed controller is practically implemented with the help of the Arduino Nano platform.

Using several experiments, the superiority of the proposed hierarchical controller in terms of performance and energy consumption has been demonstrated.

The proposed hierarchical controller has been implemented practically and an acceptable closed-loop performance has been achieved. To illustrate the efficiency of the proposed method, the closed-loop stability of this method is shown using the Lyapunov theory.