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Bales of cotton run through the gins and textile mill instruments, stick to them and make it cumbersome for the ginning mill workers. This is so because more time and money have to be invested in cleaning these instruments. The stickiness of cotton causes health hazards to the workers, decreases the yarn quality and economic loss to the textile industry. The effect of cotton stickiness on textile ginning, various methods for cotton stickiness detection and the steps for reduction are discussed.

The different methods that are available for detecting and measuring cotton stickiness are described. The sugars that cause stickiness are either of plant origin (physiological sugars) or from the feeding insects (entomological origin). The methods for stickiness detection and reduction are discussed under physical, chemical and biological categories.

This review suggests possible ways to mitigate cotton stickiness.

One of the major issues of the textile industry is honeydew-contaminated cotton stickiness. However, there are few papers on detection methods for analyzing honeydew cotton stickiness along with the approaches to reduce stickiness. This paper summarizes different methods along with a study for detection as well as reduction of cotton stickiness.

The Third National Meeting of CDROM/Online Users was sponsored by National Information System for Science and Technology (NISSAT), Department of Scientific and Industrial Research (DSIR), New Delhi; organised by the National Information Centre for Leather & Allied Industries (NICLAI); and held at the Central Leather Research Institute, Madras during 9–10 August 1994. The previous two Meetings were held at New Delhi in 1992 and 1993. The Meeting's objective was to assess the current national and international situation, the utility of CDROM services, and their popularisation within the country, and to provide a forum for exchange of experiences on this new technique. The Meeting focused its attention on databases available, demand for search services, target users, problem areas and possible solutions. The Meeting was attended by 205 participants from 91 institutions all over the country, representing a galaxy of R&D, academic, corporate, entrepreneurial and so forth institutions. Eleven industries engaged in information technology products participated in the Meeting. The Meeting included technical sessions where 39 presentations were made.

Brand awareness is a key cognitive metric and a vital component of consumer-based brand equity. Generating and maintaining brand awareness has been an important metric for brands and a significant outcome of advertising and promotional activities. Brand awareness has been studied over the years in the context of market share and mind share and is considered to be the fundamental starting point of a brand relationship. Most branding variables are challenged in their linear definitions by the multi-dimensionality of the connected online world. Consumers may start their brand journey with brand awareness and then proceed to explore higher options in a non-linear fashion. This challenges traditional advertising and brand salience models. In the web context, brand awareness could even happen with little prior cognitive processing. The purpose of this paper is to explore the little researched area of brand awareness in the context of Indian websites.

The study leveraged technology to study the brand discovery behaviour of real estate website users in India. The authors looked at essentially modified processes such as the brand experience journey, specifically, the sources and antecedents of brand awareness for real estate websites. Considering the fact that direct and indirect traffic to a website is indicative of the brand awareness metrics, the authors analyse the metric as a basis of the comparison.

Brand awareness in the digital world can be attributed as firm generated, user generated and, as the authors also discover, experience initiated. The altered dimensions have a profound impact on the brand advertising spends of not just digital brands, but all marketers who will find digital technology increasingly central to their brand communication efforts.

Burgeoning websites and media proliferation have increased the source set for website discovery. Customers do not rely solely on advertising or chance encounter to discover a new brand. Usage of search engines remains the key point of discovery for brand awareness. Some of the respondents recognised websites from prior advertising exposure and selected the brand from the consideration set offered in the search results for their keyword search. In other cases, brand awareness followed brand experience as the users did not process the brand information or, in most cases, were not aware of the brand name prior to website visit. The quality of brand experience created brand awareness and had further implications, including brand visit intent.

While mass media television advertising and ubiquitous online advertising can build brand awareness, the impact of search engine advertising and search engine optimisation is equally significant, if not more important. Brand managers can additionally focus on branding on the search channel in the light of increasing search intensity. Beyond traditional keyword planning, this can be an enhanced branding platform for reaching out to specific behavioural or character traits of the target audience. Brand experience can mature from task completion to brand awareness and higher level values if the website includes key brand messages and goals.

This paper offers insight into the little known field of online brand management. Information intermediary websites are unique in their value proposition, and brand building is challenging. Real estate websites have grown in popularity, but have not merited researcher attention. This paper offers a unique perspective on branding websites, specifically in the context of emerging markets like India.

The purpose of this paper is to examine the impact of key antecedents of unified theory of acceptance and use of technology model 2 on behavioral intention to accept and use mobile payment systems in National Capital Region, India.

A sample of 267 mobile payment system users in National Capital Region was obtained through an online survey. A partial least squares method was used to find out whether key antecedents of UTAUT2 predict behavioral intention to accept mobile payment systems which further predicts use behavior toward mobile payment systems.

The research substantiates that performance expectancy, effort expectancy, habit and facilitating conditions significantly predict behavioral intention, which in turn significantly predict use behavior to use mobile payment systems. Both social influence and hedonic motivation were weak predictors of behavioral intention.

The research substantiates that performance expectancy, effort expectancy, habit and facilitating conditions significantly predict behavioral intention, which in turn significantly predict use behavior to use mobile payment systems. Both social influence and hedonic motivation were weak predictors of behavioral intention.

The research substantiates that performance expectancy, effort expectancy, habit and facilitating conditions significantly predict behavioral intention, which in turn significantly predict use behavior to use mobile payment systems. Both social influence and hedonic motivation were weak predictors of behavioral intention.

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.

This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.

The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.

This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.

This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.

The adoption of mobile payment remains low in certain regions, highlighting the need to identify the factors that enable and inhibit its adoption. This study aims to address this gap by investigating the role of information security, loss aversion and the moderating influence of the herd effect on Inertia and behavioral intentions in the adoption of mobile payment systems.

A structural equation model was developed and tested with 332 valid questionnaires to examine the proposed hypotheses.

The empirical results reveal that information security plays a significant role as an enabler, while loss aversion acts as an inhibitor of mobile payment adoption. Furthermore, the study uncovers the moderating influence of the herd effect on the relationship between Inertia and behavioral intentions.

This study was conducted in a specific region and may not be generalizable to other regions. Future studies could expand the sample size and scope to enhance the external validity of the findings.

This study offers practical implications for mobile payment service providers. Understanding the key enabling and inhibiting factors identified in this study can guide providers in designing and improving their services. Strengthening information security measures can help build trust among potential adopters, while offering incentives can mitigate the impact of loss aversion and encourage early adoption.

The findings of this study have social implications as they contribute to promoting the adoption of mobile payment systems. Increased adoption can enhance financial inclusion and stimulate economic development.

This study provides novel insights into the enabling and inhibiting factors of mobile payment adoption and highlights the moderating role of the herd effect. By shedding light on the influence of social norms on individual behavior in the context of mobile payment adoption, this study contributes to the existing literature and advances our understanding of this phenomenon.

This study aims to empirically explore e-banking service quality and its impact on the e-banking loyalty through a mediating impact of e-banking satisfaction. The account holders of three domestic systemically important banks of Pakistan were surveyed during COVID-19 to examine the electronic services provided by these banks.

The data was collected through an adapted questionnaire by using emails and messaging applications. The database of a local marketing company in Pakistan was used, and 976 responses were included in the analysis. The structured equation modeling was used to test the propositions of study.

The findings delineate that reliability and website design proved to increase e-banking loyalty, particularly during COVID-19. The link between e-banking privacy and security and e-banking loyalty was proved as fully mediated by e-banking satisfaction; however, indirect effect of the reliability and website design with e-banking loyalty was partially mediated.

In strategic planning of e-banking mechanisms and the associated consumer behavior, the results of this study can be helpful for policymakers. In case of similar epidemics and natural calamities, consumers may depict similar behavior as shown during the lockdown and social distancing during COVID-19; hence, the study can help regulatory bodies in preparing their safety roadmap.

The mediating effect of e-banking satisfaction between privacy & Security and e-banking loyalty implies that customer give importance to secure e-banking platforms. There can be a variation in their loyalty because of privacy concerns. The application of cognitive–motivational–relational (CMR) theory in a relationship between e-banking service quality and e-banking loyalty with a mediating role of satisfaction is an emotional response to capture the behavioral changes during COVID-19. It enables researchers to understand the CMR theory concerning COVID-19 and e-banking perspective.

The purpose of this study is to study the impact of relationship marketing orientation (RMO) and relationship quality on customers' commitment and pro-marketer behavior (positive word of mouth and external attribution) after negative brand publicity by using the combined lens of relationship marketing theory and the theory of cognitive dissonance.

A survey was conducted among banking customers in India using an online questionnaire. Data were analyzed using structural equation modeling and the bootstrapping procedure using the SPSS process macro.

Contrary to conventional wisdom, findings of this study suggest that RMO and relationship quality are positively correlated to commitment even after negative publicity. The path between RMO, relationship quality and pro-provider behavior is found to be mediated by commitment. This indirect path is moderated by customers' cognitive dissonance arising out of the negative publicity.

The study establishes the combined roles of RMO and relationship quality in pre-empting the detrimental effects of negative brand publicity. Further, it establishes interactions of cognitive dissonance with these relationship variables, thereby bringing together literature from relationship marketing theory and cognitive dissonance theory.

Based on the positive features of the shark smell optimization (SSO) algorithm, the purpose of this paper is to propose a method based on this algorithm, dynamic voltage and frequency scaling (DVFS) model and fuzzy logic to minimize the energy consumption of integrated circuits of internet of things (IoT) nodes and maximize the load-balancing among them.

Load balancing is a key problem in any distributed environment such as cloud and IoT. It is useful when a few nodes are overloaded, a few are under-loaded and the remainders are idle without interrupting the functioning. As this problem is known as an NP-hard one and SSO is a powerful meta-hybrid method that inspires shark hunting behavior and their skill to detect and feel the smell of the bait even from far away, in this research, this study have provided a new method to solve this problem using the SSO algorithm. Also, the study have synthesized the fuzzy logic to counterbalance the load distribution. Furthermore, DVFS, as a powerful energy management method, is used to reduce the energy consumption of integrated circuits of IoT nodes such as processor and circuit bus by reducing the frequency.

The outcomes of the simulation have indicated that the proposed method has outperformed the hybrid ant colony optimization – particle swarm optimization and PSO regarding energy consumption. Similarly, it has enhanced the load balance better than the moth flame optimization approach and task execution node assignment algorithm.

There are many aspects and features of IoT load-balancing that are beyond the scope of this paper. Also, given that the environment was considered static, future research can be in a dynamic environment.

The introduced method is useful for improving the performance of IoT-based applications. We can use these systems to jointly and collaboratively check, handle and control the networks in real-time. Also, the platform can be applied to monitor and control various IoT applications in manufacturing environments such as transportation systems, automated work cells, storage systems and logistics.

This study have proposed a novel load balancing technique for decreasing energy consumption using the SSO algorithm and fuzzy logic.

A paradigm shift of industry revolution 4.0 is made possible by technological advances that constitute a reversal of conventional lean production (LP) processes. In addition, there is increasing pressure on the manufacturing industry to manage operations responsibly towards the environmental and social impact, on top of the economic. These have motivated the manufacturing industry to identify approaches to implementing LP to achieve sustainable organizational performance. Hence, this study aims to examine the moderating role of industry 4.0 technologies adoption in the relationship between LP and sustainable organizational performance.

This study proposed a research framework on the relationship between LP and sustainable organizational performance supported by LP theory and triple bottom line theory, with industry 4.0 technologies adoption as a moderator. A quantitative survey method was used in this study for data collection. The respondents in this study were middle or top management in manufacturing companies, including directors, managers, supervisors and coordinators. To investigate the demographic variables of respondents, descriptive statistics were generated by using IBM Statistical Packages for the Social Sciences. For measurement and structural model evaluations, partial least square structural equation modelling was used.

Based on the proposed research framework in this study, supplier feedback, just-in-time delivery, supplier development, customer involvement, pull system, continuous flow, set-up time reduction (STR), statistical process control, total productive maintenance (TPM) and employee involvement are the dimensions for LP. This study revealed that industry 4.0 technologies adoption positively moderated the relationships of five LP dimensions towards a sustainable organizational performance, namely, supplier feedback, supplier development, continuous flow, STR and TPM.

This study provided insights that would enable practitioners to better strategize the co-existence of LP and industry 4.0 technologies adoption in mutually supporting sustainable organizational performance (environmental, social and economic).