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Manufacturing industries are facing dynamic challenges in today’s highly competitive world. In the recent past, integrating Industry 4.0 with the lean six sigma improvement methodologies has emerged as a popular approach for organizational excellence. The research aims to explore and analyze critical success factors of lean six sigma integrated Industry 4.0 (LSSI).

This research study explores and analyzes the critical success factors (CSFs) of LSSI. A three-phase study framework is employed. At first, the CSFs are identified through an extensive literature review and validated through experts’ feedback. Then, in the second phase, the initial list of CSFs is finalized using the fuzzy DELPHI technique. In the third phase, the cause-effect relationship among CFSs is established using the fuzzy DEMATEL technique.

A dyadic relationship among cause-and-effect category CSFs is established. Under the cause category, top management commitment toward integrating LSSI, systematic methodology for LSSI and organizational culture for adopting changes while adopting LSSI are found to be topmost CSFs. Also, under the effect category, organizational readiness toward LSSI and adaptability and agility are found to be the uppermost CSFs.

The study offers a framework to understand the significant CSFs for LSSI implementation. Insights from the study will help industry managers and practitioners to implement LSSI and achieve organizational excellence.

To the best of the authors’ knowledge, CSFs of LSSI are not much explored in the past by researchers. Findings will be of great value for professionals in developing long-term operations strategies.

Purpose: Nowadays, many terms like computer vision, deep learning, and machine learning have all been made possible by recent artificial intelligence (AI) advances. As new types of employment have risen significantly, there has been significant growth in adopting AI technology in enterprises. Despite the anticipated benefits of AI adoption, many businesses are still struggling to make progress. This research article focuses on the influence of elements affecting the acceptance procedure of AI in organisations.

Design/Methodology/Approach: To achieve this objective, propose a hierarchical paradigm for the same by developing an Interpretive Structural Modelling (ISM). This paper reveals the barriers obstructing AI adoption in organisations and reflects the contextual association and interaction amongst those barriers by emerging a categorised model using the ISM approach. In the next step, cross-impact matrix multiplication is applied for classification analysis to find dependent, independent and linkages.

Findings: As India is now focusing on the implementation of AI adoption, therefore, it is essential to identify these barriers to AI to conceptualise it systematically. These findings can play a significant role in identifying essential points that affect AI adoption in organisations. Results show that low regulations are the most critical factor and functional as the root cause and further lack of IT infrastructure is the barrier. These two factors require the most attention by the government of India to improve AI adoption.

Implications: This study may be utilised by organisations, academic institutions, Universities, and research scholars to fill the academic gap and faster implementation of AI.

With gaining popularity, online communities are increasing. It is leading to the data and information overflow. So, there are some challenges like cyber frauds, cyberbullying, etc. while engaging with online communities. Not only this, anonymity of the participants, stress and racism are also big challenges in online communities' interaction. Online harassers' attack tactics have changed over time. In addition, there are challenges like quality of discussion, inequality in participation of the users, etc. may scale online communities towards incitement and activism. Therefore, this study will try to analyse these challenges for overall benefit of the society.

The underlying fuzzy set theory is employed to handle the fuzziness of users' perceptions since the attributes are expressed in linguistic preferences. Through exhaustive literature review, the authors have identified 15 challenges. These challenges are further categorised as cause and effect by using DEMATEL (Decision-Making Trial and Evaluation Laboratory) approach.

Lack of strategic planning and uninspired discussions between users has emerged as a major challenge in cause category. This study further demonstrates how individual challenge can be managed and developed to navigate the online communities to maintain a healthy environment in society.

Results are based on limited dataset. Therefore, findings cannot be generalised for all online communities.

The research findings offer a suitable direction to policymakers to formulate and design policies, laws and regulations to increase user engagement in the online community. The study is beneficial to firms and researchers in understanding the factors influencing effective management of online communities.

The utilisation of renewable energy sources for generating electricity and potable water is one of the most sustainable approaches in the current scenario. Therefore, the current research aims to design and develop a novel co-generation system to address the electricity and potable water needs of rural areas.

The cogeneration system mainly consists of a solar parabolic dish concentrator (SPDC) system with a concentrated photo-voltaic module at the receiver for electricity generation. It is further integrated with a low-temperature thermal desalination (LTTD) system for generating potable water. Also, a novel corn cob filtration system is introduced for the pre-treatment to reduce the salt content in seawater before circulating it into the receiver of the SPDC system. The designed novel co-generation system has been numerically and experimentally tested to analyse the performance at Karaikal, U.T. of Puducherry, India.

Because of the pre-treatment with a corn cob, the scale formation in the pipes of the SPDC system is significantly reduced, which enhances the efficiency of the system. It is observed that the conductivity, pH and TDS of seawater are reduced significantly after the pre-treatment by the corncob filtration system. Also, the integrated system is capable of generating 6–8 litres of potable water per day.

The integration of the corncob filtration system reduced the scaling formation compared to the general circulation of water in the hoses. Also, the integrated SPDC and LTTD systems are comparatively economical to generate higher yields of clean water than solar stills.

Increasing public consciousness and demand for sustainable environment make selection of a safe location for effective disposal of healthcare waste (HCW) a challenging issue. This problem becomes more complicated due to involvement of multiple decision makers having varying knowledge and interest, conflicting quantitative and qualitative evaluation criteria, and presence of several alternative locations.

To efficiently resolve the problem, the past researchers have already coupled different multi-criteria decision-making tools with uncertainty models and criteria weight measurement techniques, which are time-consuming and highly computationally complex. Based on involvement of a group of experts expressing their opinions with respect to relative importance of criteria and performance of alternative locations against each criterion, this paper proposes application of ordinal priority approach (OPA) integrated with grey numbers to solve an HCW disposal location selection problem.

The grey OPA can simultaneously estimate weights of the experts, criteria and locations relieving the decision makers from complicated computational steps. The potentiality of grey OPA in solving an HCW disposal location selection problem is demonstrated here using an illustrative example consisting of three experts, six criteria and four alternative locations.

The derived results show that it can be employed to deal with real-time HCW disposal location selection problems in uncertain environment providing acceptable and robust decisions. It relieves the experts from pair-wise comparisons of criteria, normalization of data, identification of ideal and anti-ideal solutions, aggregation of information and so on, while arriving at the most consistent decision with minimum computational effort.

Conventional isolated dc–dc converters offer an efficient solution for performing voltage conversion with a large improved voltage gain. However, the small-signal analysis of these converters shows that a right-half-plane (RHP) zero appears in their control-to-output transfer function, exhibiting a nonminimum-phase stability. This RHP zero can limit the frequency response and dynamic specifications of the converters; therefore, the output voltage response is sluggish. To overcome these problems, the purpose of this study is to analyze, model and design a new isolated forward single-ended primary-inductor converter (IFSEPIC) through RHP zero alleviation.

At first, the normal operation of the suggested IFSEPIC is studied. Then, its average model and control-to-output transfer function are derived. Based on the obtained model and Routh–Hurwitz criterion, the components are suitably designed for the proposed IFSEPIC, such that the derived dynamic model can eliminate the RHP zero.

The advantages of the proposed IFSEPIC can be summarized as: This converter can provide conditions to achieve fast dynamic behavior and minimum-phase stability, owing to the RHP zero cancellation; with respect to conventional isolated converters, a larger gain can be realized using the proposed topology; thus, it is possible to attain a smaller operating duty cycle; for conventional isolated converters, transformer core saturation is a major concern, owing to a large magnetizing current. However, the average value of the magnetizing current becomes zero for the proposed IFSEPIC, thereby avoiding core saturation, particularly at high frequencies; and the input current of the proposed converter is continuous, reducing input current ripple.

The key benefits of the proposed IFSEPIC are shown via comparisons. To validate the design method and theoretical findings, a practical implementation is presented.

Spirituality is a concept that explains the spiritual dimension of human beings. Meaning of life, being together, and the bigger one's existence are the components of spirituality. In that manner, workplace spirituality is an area that endeavors to find the meaning of the work together with the community. Research reveals that workplace spirituality has several dimensions, and inner life, sense of community, and meaning at work are validly used. Though developing in the research area, misunderstandings and misuses of the phenomenon causes several dark sides. In this context, this chapter aims to reveal these in five aspects to contribute to enhancing the literature. The ambiguity in defining spirituality and religion is the first dark point in understanding workplace spirituality. Accepting spiritual executions at work as a tool for profitability is the second. The scarcity of awareness of executive leaders about workplace spirituality is the other. Considering the studies on workplace spirituality as a fad and the expected difficulties in developments about the area is the fourth. Finally, workplace spirituality-based misbehaviors at work are the last dark side, addressed in this study. In addition to the explanations in its content, the authors present a bibliometric analysis conducted by R. The chapter concludes with general evaluations and suggestions for future studies.

Key success factors (KSFs) of total productive maintenance (TPM) have historically played a vital role in attaining economic and ecological sustainability but have overlooked social sustainability. Hence, this study analyses and ranks the most significant TPM KSFs for attaining social sustainability in manufacturing small and medium enterprises (SMEs).

The research employs a deductive methodology to identify the relevant TPM KSFs and social sustainability indicators and then uses Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank the TPM KSFs in order to achieve social sustainability, followed by a sensitivity analysis to assess the methodological robustness.

The findings indicate that the top five TPM KSFs influencing social sustainability are employee health and safety, organizational culture, top management commitment, employee engagement and effective communication and effective workplace management. In addition, the results indicate that effective equipment utilization is the least significant TPM key factor affecting social sustainability.

SME manufacturing managers do not need to worry about all of the TPM KSFs if they only concentrate on the ones that will have the most impact. If managers use the top 5 TPM KSFs as a starting point, they may create customized TPM training programs for their companies. As a result, this will facilitate the efforts of their personnel toward social sustainability.

In the existing literature, little emphasis has been paid to social sustainability and how SMEs may implement these practices. This research adds to the current theory of TPM and social sustainability and sheds light on how SMEs might use TPM to advance toward more socially sustainable operations.

This paper aims to focus on solving the path optimization problem by modifying the probabilistic roadmap (PRM) technique as it suffers from the selection of the optimal number of nodes and deploy in free space for reliable trajectory planning.

Traditional PRM is modified by developing a decision-making strategy for the selection of optimal nodes w.r.t. the complexity of the environment and deploying the optimal number of nodes outside the closed segment. Subsequently, the generated trajectory is made smoother by implementing the modified Bezier curve technique, which selects an optimal number of control points near the sharp turns for the reliable convergence of the trajectory that reduces the sum of the robot’s turning angles.

The proposed technique is compared with state-of-the-art techniques that show the reduction of computational load by 12.46%, the number of sharp turns by 100%, the number of collisions by 100% and increase the velocity parameter by 19.91%.

The proposed adaptive technique provides a better solution for autonomous navigation of unmanned ground vehicles, transportation, warehouse applications, etc.

Kids get exposed to advertising on social media platforms when they visit them to perform various goals. The purpose of this study is to find out the factors which affect kids' behaviour when the kids encounter advertising on social media and also to establish causal relationships amongst the factors of social media advertising (SMA).

A total of 11 factors of SMA have been identified with the help of experts and the causal relationships amongst the SMA factors have been constructed by the implementation of decision-making trail and laboratory evaluation (DEMATEL). Based on the established relationships, a causal diagram has been also developed to understand the structural nature of interdependence amongst the factors.

DEMATEL technique is based on logical steps, which have assisted in categorising the identified factors into two groups: cause group and effect group. Cause group factors are the reasons for the effect group factors to occur. Customisation, entertainment, information and interactivity have been observed as cause factors whereas, relevance, engagement with SMA, purchase intention, product involvement, advertising value, attitude towards SMA and irritation have been observed as effect factors. Product involvement is found to have the highest level of interaction with all other factors. Information and interactivity are observed to influence all other factors.

Kids possess a limited understanding of the selling intent of advertisers which makes kids vulnerable to advertising. This study supports that the content of the advertisement should be kept in accordance with the need of kids and also suggests that marketers should emphasise cause group factors which derive subsequent consequences on effect group factors. The foremost limitation of this study lies in the process of identifying the factors through expert opinions. The sets of contextual relationships may vary when different experts are considered.

This study strives to identify the factors which affect kids' understanding of SMA and also establishes causal relationships amongst them. This kind of study is unique in state of the art and to the authors' knowledge no significant research has been conducted in India which involves establishment of inter-relationships amongst SMA factors that affect kids' behaviour.