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“The purpose of this paper is to review the literature on service innovation during the past two decades and provide an analysis of sources of publication, citations and authorship using bibliometric analysis techniques (VOSviewer).” This paper aims to assesses the important trends, enhance the academic debate, identify research gaps and propose future directions and a research agenda.

This paper examines 176 articles in English language published from 2002 to 2022 from the Scopus database by adopting the bibliometric technique using VOSviewer software. This paper analyzes the different levels and boundaries of service innovation using bibliometric analysis of “service innovation research” using VOSviewer software. The methodology analyzes the number of citations, co-citations, keywords, authors, journals and countries.

The review of the past 20 years indicates a substantial growth in the number of good research publications on service innovation. The UK, the USA, Sweden and Australia dominate this research area with the most articles published to date under the subject area of “Business management.” The review highlighted that most of the studies on service innovation focused on products, companies and processes in the services industry. The most critical factors behind service innovation failure are improper management and lack of knowledge. The citation analysis revealed various research implications and directions for the future.

This study focuses only on service innovation and excludes research on performance management and control. Thus, future studies may explore this area of research in future studies. Only research articles were analyzed; conference papers, reports, manuals and white papers from practice were excluded. Research implications indicate that future studies on service innovation would be essential for organizational excellence, not process excellence.

This paper provides a comprehensive overview of the current status and essential trends of research on service innovation. This study identifies the research gaps and provides a clear research agenda for understanding the various elements of service innovation.

The purpose of the study was to produce a healthier, convenient and traditional ready-to-eat (RTE) snack option with increased nutritional value, using spent hen meat, dietary fibre (DF) and simple technological methods. The product was designed to be stable without refrigeration and be easily adoptable by local self-help groups, rural women and youth and entrepreneurs in urban and semi-urban areas.

Conventional binder used for making snacks, i.e. rice flour was partially replaced by different sources of antioxidant DFs, i.e. oat flour (T₁ – 10%), finger millet flour (T₂ – 5%) and amaranth flour (T₃ –15%) to prepare spent hen snack sticks (SHSS). The snacks were then packaged in low density polyethylene (LDPE) pouches and evaluated for their storage stability at ambient temperature for a period of 35 days. Their physico-chemical, sensory and microbiological quality was evaluated at a regular interval of 7 days. The proximate composition of developed SHSS was compared to commercially available snack products (chakli/murukku – snacks without meat).

The fibre-enriched SHSS showed significant improvement in nutritive value, as they contained more fibre (p = 0.001) and protein (p = 0.029) than control SHSS. When compared to commercially available snack product SHSS showed three-fold significant increase in protein (p = 0.000) and ash content (p = 0.001) and only 11%–12% total fat as compared to 31% fat in the market-available product. The most acceptable treatment in terms of overall sensory quality and nutritional aspects was T₃; however, T₂ was more shelf-stable during the storage period. The study showed that fibre-enriched snacks can be stored at ambient temperature for up to 35 days without substantial loss in physico-chemical, sensory and microbial quality. Hence, substituting rice flour with DFs can lead to the development of products with better sensory attributes and improved functionality.

The simplicity of the product in terms of composition, machinery and low production costs makes it an easily adoptable one by small-scale entrepreneurs, especially those belonging to semi-urban areas.

Incorporation of spent hen meat, a relatively cheap but abundant source of protein, in RTE products can serve as an effective way to alleviate protein malnutrition, whereas addition of fibre further improves the functionality of the product. The methodology can be easily taken up by small-scale entrepreneurs and create a market for snack-based functional meat products.

This primary purpose of this research is to explore the rank order of funding challenges among the other challenges faced by women entrepreneurs (WEs) in an urban setting. For this purpose, the study uses a sample of women microentrepreneurs engaged in (the relatively capital-intensive) manufacturing activities. The study further explores the perception of WEs toward borrowing as a source of funds and the challenges they face while accessing institutional finance. Lastly, the study explores whether the financial challenges faced by them are, in part, influenced by the deficiencies in their own skill set, as the human capital theory suggests.

For the purpose of analysis, this research uses summary statistics, namely the mean, mode and standard deviation for the purpose of preliminary analysis. The Friedman two-way analysis of variance by rank test and the associated chi-square value are used to statistically validate the hypothesis. Furthermore, the Wilcoxon signed-rank test is used to check for the robustness of our results.

Our findings suggest that the growth of the financial services sector in urban India has not had a significant impact on the funding challenges that WEs faced. A closer look at the evidence suggests that even for educated urban women, the funding challenges faced are no different from what has widely been reported in the context of other emerging nations. Highlighting the inadequacies in high school and college education in so far as financial skills training is concerned, the study recommends a multipronged approach to address the observed voluntary abstinence from borrowing as well as the institutional hurdles WEs faced.

Unlike previous research where WEs are treated as a monolithic whole, this study focuses on WEs engaged in manufacturing activities in an urban setting.

This study aims to improve the detection and quantification of cardiac issues, which are a leading cause of mortality globally. By leveraging past data and using knowledge mining strategies, this study seeks to develop a technique that could assess and predict the onset of cardiac sickness in real time. The use of a triple algorithm, combining particle swarm optimization (PSO), artificial bee colony (ABC) and support vector machine (SVM), is proposed to enhance the accuracy of predictions. The purpose is to contribute to the existing body of knowledge on cardiac disease prognosis and improve overall performance in health care.

This research uses a knowledge-mining strategy to enhance the detection and quantification of cardiac issues. Decision trees are used to form predictions of cardiovascular disorders, and these predictions are evaluated using training data and test results. The study has also introduced a novel triple algorithm that combines three different combination processes: PSO, ABC and SVM to process and merge the data. A neural network is then used to classify the data based on these three approaches. Real data on various aspects of cardiac disease are incorporated into the simulation.

The results of this study suggest that the proposed triple algorithm, using the combination of PSO, ABC and SVM, significantly improves the accuracy of predictions for cardiac disease. By processing and merging data using the triple algorithm, the neural network was able to effectively classify the data. The incorporation of real data on various aspects of cardiac disease in the simulation further enhanced the findings. This research contributes to the existing knowledge on cardiac disease prognosis and highlights the potential of leveraging past data for strategic forecasting in the health-care sector.

The originality of this research lies in the development of the triple algorithm, which combines multiple data mining strategies to improve prognosis accuracy for cardiac diseases. This approach differs from existing methods by using a combination of PSO, ABC, SVM, information gain, genetic algorithms and bacterial foraging optimization with the Gray Wolf Optimizer. The proposed technique offers a novel and valuable contribution to the field, enhancing the competitive position and overall performance of businesses in the health-care sector.