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Employee engagement (EE) continues to be one of the most difficult challenges for organizations today. Numerous factors have been linked to EE, according to studies. However, the necessary human resource management (HRM) strategies and systems for enhancing EE have not yet been developed. It is questionable if all employees inside the company require the same HRM strategies, to boost engagement as one size does not fit all. Therefore, it is necessary to create employee profiles based on factors associated with EE. This study aims to develop employee profiles based on engagement dimensions and outcomes. It seeks to comprehend the relationship between engagement level and factors such as age, years of service and employment grade.

Using latent profile analysis (LPA), we identified five EE profiles (highly engaged, engaged, moderately engaged, disengaged and highly disengaged). These five profiles were characterized by five EE dimensions (Culture Dimensions, Leadership Dimensions, People Process, Business alignment Dimension and Job Dimension) and EE outcomes (Say, Stay and Strive).

The study revealed that Engaged profiles exhibited low stay outcomes. The highest percentage of disengaged employees fall under 25 years of age with less than 5 years of experience and are at the entry level.

The study highlights the significance of the people processes dimensions in enhancing engagement. Profiles with low people process dimensions showed high disengagement. Person-centered LPA adds and complements variable-centered approach to develop a better understanding of EE and help organizations devise more personalized strategies. The study would be of interest to both academics and practitioners.

The novelty of this study lies in its attempt to model the employee profiles to comprehend the relationship between engagement levels using LPA.

The purpose of this paper is to assess the impact of deviation from the target investment in working capital (WC) (measured by net trade cycle (NTC)) on the profitability (measured by gross operating income (GOI) and net operating income (NOI)) of the listed non-financial Indian firms.

The study is based on the data collected on NTC, GOI, NOI and other variables pertaining to 242 listed non-financial Indian firms that form part of the Bombay Stock exchange 500 Index for the period 2012–2017 (1,452 firm-year observations). Following Banos-Caballero et al. (2010), the authors use a firm fixed effect regression as the benchmark regression for finding out the determinants of NTC of the sample firms. Furthermore, this study explores the impact of deviation (above and below target) from the target investments in WC on the firm profitability (GOI and NOI) employing fixed effect regression.

The result of this study reveals that Indian firms maintain a target NTC and try to converge in case of any deviations to it. Furthermore, the profitability of the sample firms was observed to be influenced by the deviation from the target NTC irrespective of whether the deviation was above or below the target investment level in WC.

This study highlights the importance of good WC management for firms due to the negative impact of the over- and under-investments in WC and contributes to the existing body of knowledge by suggesting that managers should keep close to the target WC and not deviate from this in order to maximize the firms’ profitability.

To the best of the knowledge of the researchers, this is perhaps the first study to examine the impact of firms’ deviation from their target investment in WC on the profitability for non-financial firms listed and operating in India.

The objective of the study is to identify the themes of “family friendly practices” and to perform a literature review. The research aims to identify the emerging trends in the area of “family friendly practices” by carrying out an exhaustive literature review.

The study synthesizes the literature between the years 2010 and 2019. First of all, 150 research articles were identified by keyword search, bibliography and citation search, out of which 57 research articles were selected on the basis of the most sound theoretical background and maximum literature contribution. The citation analysis method was performed on these studies in order to study the journals, authors by using Google Scholar, ResearchGate, the international database Science Citation Index and SCImago Journal Ranking.

The author citation count shows that the research topic is still getting recognition and the research in this area is increasing. The finding of the research is that the current research in family-friendly practices has focused mainly on seven topics: availability and usability of family-friendly policy, job satisfaction, organizational performance, supervisor or manager support, work–life conflict, employee turnover employee retention and women’s employment.

The study may provide valuable inputs to the HRD practitioners, managers, research scholars, to understand the recent trends in the field of family-friendly policy. As per the best knowledge of the author, this is the first study on family-friendly practices using citation analysis.

The purpose of this study is to introduce working capital efficiency multiplier (WCEM) as a direct profitability measure of working capital management. The existing accounting measures in the literature establish an indirect approach to study the relationship between working capital efficiency and profitability of the firms.

Using the help of a set of companies from CMIE Prowess database, the study introduces WCEM as a direct profitability measure of working capital efficiency.

In this study, a new direct measure of working capital efficiency is introduced which is multiplicative in nature. WCEM is a product of three components, namely, WACC, ratio of the sum of trade receivables and inventories to trade payables and ratio of net working capital (NWC) to net sales.

The importance of direct measure like WCEM could be enormous in performance evaluation of a firm. It can be used as an indicator for choosing a suitable investment opportunity by an investor. This is due to the fact that the firm that is highly efficient in managing working capital is less exposed to liquidity risk. At the same time, the firm is less dependent on external financing. Therefore, such firms eventually create more value for their shareholders. Another indication that WCEM provides is to gauge the bargaining power of the firm and its competitive position in the market. Lower WCEM indicates higher bargaining power of a firm across the value chain, and its superior position relative to its competitors.

Most of the studies on WCM are of the empirical type and there is a complete dearth on theoretical framework. Researchers hereafter can consider WCEM as one of the financial performance variables in place of the existing measures such as return on asset (ROA), return on invested capital (ROIC), return on equity (ROE), gross operating income (GOI) and net operating income (NOI) and thereby can contribute new empirical insights through their research outcomes.

This paper aim to find optimal cluster head and minimize energy wastage in WSNs. Wireless sensor networks (WSNs) have low power sensor nodes that quickly lose energy. Energy efficiency is most important factor in WSNs, as they incorporate limited sized batteries that would not be recharged or replaced. The energy possessed by the sensor nodes must be optimally used so as to increase the lifespan. The research is proposing hybrid artificial bee colony and glowworm swarm optimization [Hybrid artificial bee colony and glowworm swarm optimization (HABC-GSO)] algorithm to select the cluster heads. Previous research has considered fitness-based glowworm swarm with Fruitfly (FGF) algorithm, but existing research was limited to maximizing network lifetime and energy efficiency.

The proposed HABC-GSO algorithm selects global optima and improves convergence ratio. It also performs optimal cluster head selection by balancing between exploitation and exploration phases. The simulation is performed in MATLAB.

The HABC-GSO performance is evaluated with existing algorithms such as particle swarm optimization, GSO, Cuckoo Search, Group Search Ant Lion with Levy Flight, Fruitfly Optimization algorithm and grasshopper optimization algorithm, a new FGF in the terms of alive nodes, normalized energy, cluster head distance and delay.

This research work is original.

This paper aims to introduce recently an unregulated unsupervised algorithm focused on voice activity detection by data clustering maximum margin, i.e. support vector machine. The algorithm for clustering K-mean used to solve speech behaviour detection issues was later applied, the application, therefore, did not permit the identification of voice detection. This is critical in demands for speech recognition.

Here, the authors find a voice activity detection detector based on a report provided by a K-mean algorithm that permits sliding window detection of voice and noise. However, first, it needs an initial detection pause. The machine initialized by the algorithm will work on health-care infrastructure and provides a platform for health-care professionals to detect the clear voice of patients.

Timely usage discussion on many histories of NOISEX-92 var reveals the average non-speech and the average signal-to-noise ratios hit concentrations which are higher than modern voice activity detection.

Research work is original.

The purpose of this paper is to decrease the traffic created by search engines’ crawlers and solve the deep web problem using an innovative approach.

A new algorithm was formulated based on best existing algorithms to optimize the existing traffic caused by web crawlers, which is approximately 40 percent of all networking traffic. The crux of this approach is that web servers monitor and log changes and communicate them as an XML file to search engines. The XML file includes the information necessary to generate refreshed pages from existing ones and reference new pages that need to be crawled. Furthermore, the XML file is compressed to decrease its size to the minimum required.

The results of this study have shown that the traffic caused by search engines’ crawlers might be reduced on average by 84 percent when it comes to text content. However, binary content faces many challenges and new algorithms have to be developed to overcome these issues. The proposed approach will certainly mitigate the deep web issue. The XML files for each domain used by search engines might be used by web browsers to refresh their cache and therefore help reduce the traffic generated by normal users. This reduces users’ perceived latency and improves response time to http requests.

The study sheds light on the deficiencies and weaknesses of the algorithms monitoring changes and generating binary files. However, a substantial decrease of traffic is achieved for text-based web content.

The findings of this research can be adopted by web server software and browsers’ developers and search engine companies to reduce the internet traffic caused by crawlers and cut costs.

The exponential growth of web content and other internet-based services such as cloud computing, and social networks has been causing contention on available bandwidth of the internet network. This research provides a much needed approach to keeping traffic in check.