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This paper aims to trace the history, application areas and users of Classical Analytics and Big Data Analytics.

The paper discusses different types of Classical and Big Data Analytical techniques and application areas from the early days to present day.

Businesses can benefit from a deeper understanding of Classical and Big Data Analytics to make better and more informed decisions.

This is a historical perspective from the early days of analytics to present day use of analytics.

The purpose of this research is to establish a conceptual model to understand the impact of Total Productive Maintenance (TPM) and Industry 4.0 (I4.0) on the transition of a Circular Economy (CE). Also, the paper explores the combined impact of TPM, I4.0 and CE on the sustainability performance (SP) of manufacturing firms.

The conceptual model is proposed using the dynamic capability view (DCV) and empirically validated by partial least squares-structural equation modelling (PLS-SEM) using 304 responses from Indian manufacturing firms.

The results suggest that I4.0 positively impacts TPM, CE and SP, also showing TPM's positive impact on CE and SP. In addition, CE has a positive influence on the SP of manufacturing firms. Furthermore, CE partially mediates the relationship between I4.0 and SP with TPM and SP. The study also identifies TPM, I4.0 and CE as a new bundle of dynamic capabilities to deliver SP in manufacturing firms.

The present research adds to the knowledge and literature on DCV by identifying the importance of CE in the settings of I4.0 and TPM, especially in the context of sustainability. Also, the current study offers a new set of dynamic capabilities and provides some significant future recommendations for researchers and practitioners.

This paper aims to provide information of parameters and techniques used in the automated fraud detection system during online transaction. With the increase in the use of online transactions, the concerns regarding data security have also increased. To tackle the frauds, lot of research has been done and plethora of papers are available on the related topics. The purpose of this paper is to provide the clear pathway for researchers to move in the direction of development of automated fraud detection system to prevent the fraud during online transaction.

This literature review analyses and compares the different types of techniques for detecting fraud during online transaction. An in-depth study of the most prominent journals has been done and the core methodology of the papers has been presented. This article also shed some light on different types of parameters used in fraud detection techniques during online transaction.

There are vast varieties of various fraud detection techniques, and every technique has completed task in its own way. After studying approximately 41 research papers, 14 books and four reports, in total 30 parameters have been identified and a detailed study of the parameters has been presented. The parameters are also listed with their details that how these parameters are used in the security system for detecting online transaction fraud.

This paper provides empirical insight about the parameters and their prominence in the development of automated fraud detection security system of online transaction. This paper encourages the researchers to development of improved fraud detection system.

This paper will pave the way for researchers to do a focused research on the fraud detection methodologies. The analysis will help in zeroing down the most prevalent topic of research in this field. The researchers will be able to understand the internal details of parameters and techniques used in the fraud detection systems. This literature also helps the research to think in a variety of ways that how these parameters will be used in the development of fraud detection system.

This paper is one of the most comprehensive reviews in its field. It tries and attempts to fill a void created because of lack of compilation of the laid fraud detection parameters.

In present research work, the effects of rotational speed, feed rate and vibration amplitude have been investigated during novel method of ultrasonic-assisted bone grinding. During dissection of tumors, firstly a bone flap is removed near the target region to create passage for grinding burr. During abrasion, heat is produced, which sometimes increases the temperature to unsafe levels. So, efforts have been made to limit the temperature below the threshold levels of osteonecrosis during bone grinding.

The temperature produced during osteotomy has been measured using infrared thermography camera during the implementation of L18 Taguchi orthogonal array design. Subsequently, main effect plots and contour plots have been presented to analyze and visualize the effect of grinding parameters on temperature rise during bone grinding. Furthermore, the process parameters have been optimized for optimum results for response characteristics using Taguchi SN ratio-based optimization methodology. For multiobjective optimization, the process parameters are further optimized using grey relational analysis.

It is revealed that all three process parameters substantially affect the response characteristics. The proposed optimization methodology is successfully applied on the experimental findings and the optimum results for change in temperature are found to be rotational speed = 3,000 rpm, feed rate = 20 mm/min, amplitude = 10 µm and for standard deviation are 5,000 rpm, 60 mm/min, 10 µm.

The present research findings cannot be generalized, and researchers are encouraged to further investigate the proposed rotary ultrasonic-assisted bone grinding at higher rotational speed up to 60k rpm on the skull bone.

The research on osteotomy is still at its initial phase, and continuous research is carried out for making patients’ life comfortable. In this direction, the authors have proposed a novel osteotomy method to limit the temperature below the threshold limit of osteonecrosis. The outcomes of the present study will be beneficial for the neurosurgeons working in this field.

The purpose of this paper is to examine the mechanical characteristics and optimization of wear parameters of hybrid (TiO₂ + Y₂O₃) nanoparticles with Al matrix using squeeze casting technique.

The hybrid aluminium matrix nanocomposites (HAMNCs) were fabricated with varying concentrations of titanium oxide (TiO₂) and yttrium oxide (Y₂O₃), from 2.5 to 10 Wt.% in 2.5 Wt.% increments. Dry sliding wear test variables were optimized using the Taguchi method.

The introduction of hybrid nanoparticles in the aluminium (Al) matrix was evenly distributed in contrast to the base matrix. HAMNC6 (5 Wt.% TiO₂ + 5 Wt.% Y₂O₃) reported the maximum enhancement in mechanical properties (tensile strength, flexural strength, impact strength and density) and decrease in porosity% and elongation% among other HAMNCs. The results showed that the optimal combination of parameters to achieve the lowest wear rate was A3B3C1, or 15 N load, 1.5 m/s sliding velocity and 200 m sliding distance. The sliding distance showed the greatest effect on the dry sliding wear rate of HAMNC6 followed by applied load and sliding velocity. The fractured surfaces of the tensile sample showed traces of cracking as well as substantial craters with fine dimples and the wear worn surfaces were caused by abrasion, cracks and delamination of HAMNC6.

Squeeze-cast Al-reinforced hybrid (TiO₂+Y₂O₃) nanoparticles have been investigated for their impact on mechanical properties and optimization of wear parameters.

3D food printing technology is an emerging smart technology, which because of its inbuilt capabilities, has the potential to support a sustainable supply chain and environmental quality management. This new technology needs a supportive ecosystem, and thus, this paper identifies and models the enablers for adopting 3D printing technology toward a sustainable food supply chain.

The enablers were identified through an extensive literature review and verified by domain experts. The identified enablers were modelled through the hybrid total interpretive structural modelling approach (TISM) and the decision-making trial and evaluation laboratory (DEMATEL) approach.

It emerged that stakeholders need technical know-how about the 3D printing technology, well supported by a legal framework for clear intellectual property rights ownership. Also, the industry players must have focused and clear strategic planning, considering the need for sustainable supply chains. Moreover, required product innovation as per customer needs may enhance the stakeholders' readiness to adopt this technology.

The framework proposed in this research provides managers with a hierarchy and categorization of adoption enablers which will help them adopt 3D food printing technology and improve environmental quality.

This research offers a framework for modelling the enablers for 3D food printing to develop a sustainable food supply chain using the TISM and DEMATEL techniques.

Construction industry is one of the leading causes of pollution generation in today's context. But the fact that the development of construction industry leads to the country's economic and social development cannot be unobserved. Hence, there is a need to develop a sustainable construction methodology, and while doing so, measures must be considered so as to not disturb the natural habitats. With the greater prominence shown toward the concept of green and sustainable construction developments, various tools have been developed in recent years in order to measure the performance of such sustainable and green buildings. In the Indian context, the assessment tools developed to measure the performance of the green building are found to be scanty in addressing various economic and social impacts.

This study aims at developing a building performance score (BPS) model concerning the sustainability model built on the triple bottom priorities considering all the three vital components, viz. environmental, economic and social factors. In this study, the different phases involved in the complete life cycle of the project are recognized and then all the phases are assessed considering all the three major components mentioned in the BPS model.

The outcome of this study specifies that various indicators, such as the topographical and climate change, health and safety of the construction workers, project management consultancy, risk management, security measures and solid waste management, form a chief source of a sustainable building, and these indicators are not being assessed in the existing assessment tools. Also, consideration of environmental, economic and social factors is also equally important in construction industry. Moreover, these indicators are also required to be assessed and included in the evaluation process while assessing the performance of the building.

The BPS model developed in the study will assist to improve in assessing the building performance with respect to all indicators in the complete life cycle of the project.

The purpose of this study is to consolidate the state of research in mobile commerce (m-commerce) technology adoption from 2008 to 2017. This study not only reviews the evolution of m-commerce literature but also highlights the significant contribution of authors and their productive collaborations in the development of this area.

A total of 184 peer-reviewed articles focusing on m-commerce adoption were obtained after applying search criteria on Scopus database. After using inclusion and exclusion criteria to achieve the objective of this study, 86 journals were shortlisted. The study further discusses the dispersion of various m-commerce applications being explored in m-commerce literature. In addition, dominating technology adoption theories and the most used keywords have also been identified and discussed in this study.

This study indicates that technology acceptance model and unified theory of acceptance and use of technology are the most exploited technology adoption theories. Moreover, the dominating constructs in literature are perceived usefulness, ease of use social influence/social norm on behavior intention, attitude intention followed by performance expectancy, facilitating condition and effort expectancy influence on intention.

This study presents a detailed association rule mining and community analysis to show the association among constructs such as usefulness, ease of ease, social influence and intention as dominant group in this area. The study also identifies lesser explored constructs and indicates possible future research to validate and strengthen their understanding further.

Research on employee mobility has proliferated in the past four decades across four research traditions: Economics, sociology, management, and organizational behavior/human resource management. Despite significant overlap in interest and focus, these four streams of research have evolved independent from each other, resulting in a structural divide. We provide a detailed account of the research on employee mobility and the structural divide across disciplines. We document that the payoff from this profusion of research and increasing interest has been disappointing, as reflected in the limited number of cross-disciplinary citations, even among common topics of interest. However, our analysis also provides some encouraging signs in the form of specific journals and individuals who provide a bridge for cross-disciplinary fertilization.

The purpose of this paper is to empirically examine the impact of supply chain risks (SCRs) and demand management strategies (DMSs) on the company performance in order to study the use of DMSs in delivering improved results even in the presence of SCRs. The SCRs considered under the study are as follows: demand variability, constrained capacity and quality of services delivery, and competitive performance, customer satisfaction and financial performance are the measures considered for company performance.

This study is based on a survey of 439 businesses in India representing 10 groups of services industries (information technology/IT enabled services, business process outsourcing, IT infrastructure, logistics/transportation, healthcare, hospitality, personal services, consulting, education and training, consumer products and retail), using structural equation modeling (SEM) methods.

The findings reveal that presence of demand variability risk has significant influence upon the use of demand planning and forecasting, controlling customer arrival during peaks and shifting demand to future. Mismatch of capacity against demand (unused capacity) leads to the use of techniques to influence business during lean periods, thereby resulting in enhanced supply chain (SC) and financial performance. Controlling customer arrival during peaks to shift the demand to lean periods leads to enhanced financial performance. Presence of delivery quality risk does not significantly influence the use of DMS. Also, short-term use of customer and business handling techniques does not exert significant influence on company performance.

The study has limitations as follows: (1) respondents are primarily from India while representing global organizations, (2) process/service redesign to relieve capacity as a DMS is not considered and (3) discussion on capacity management strategies (CMSs) is also excluded.

SC managers can be resourceful in shifting the peak demand to future with the application of techniques to control customer arrival during peaks. The managers can also help enhance business by influencing business through offers, incentives and promotions during lean periods to use available capacity and improve company performance.

This study is one of the first empirical works to explore how presence of SCRs influences the use of DMS and impacts the three types of company performance. The study expands current research on demand management options (DMOs) by linking three dimensions of company performance based on the data collected from ten different groups of service industry.