Hari Vasudevan, Sanjaya S. Gaur and Rajesh Kumar Shinde
This study attempts to understand the impact of relational switching costs and satisfaction on commitment as well as its impact on the satisfaction – commitment link in a…
Abstract
Purpose
This study attempts to understand the impact of relational switching costs and satisfaction on commitment as well as its impact on the satisfaction – commitment link in a supplier‐to‐manufacturer context in manufacturing.
Design/methodology/approach
The data for this study were collected from the small and medium size manufacturing firms located in and around Mumbai. A total of 67 CEOs/business heads were randomly selected and personally interviewed with the help of a structured questionnaire.
Findings
Study strengthens the view that small and medium enterprises need to invest in relationships so that such investments are turned into relational switching barriers and they would thereby help in increasing customer retention. It also shows that if relational switching costs are higher, then even if satisfaction is lower the customer is less likely to terminate the relationship.
Research limitations/implications
For marketing practitioners, the findings validate the long‐held belief that relationship marketing orientation is critical for business performance. However, data in this study were obtained from manufacturing firms, which are into plastics and light engineering sector. Replication of this study on a wider scale across different industries is essential for the generalization of the findings. Further, it could be useful to explore the complexities of the relationship between relational switching costs and other types of switching costs like the set‐up and financial costs.
Originality/value
Although empirical studies have dealt with the customers’ switching behaviour, the concept of relational switching costs and its impact on other relational parameters is relatively new. This study's unique contribution is in this direction.
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Ayush, Amit Gangotia and Biswabhusan Pradhan
This study acclaims the social entrepreneurship based on cow rearing experiential tourism in Himachal Pradesh. This study aims to illustrate the role of indigenous cows in the…
Abstract
Purpose
This study acclaims the social entrepreneurship based on cow rearing experiential tourism in Himachal Pradesh. This study aims to illustrate the role of indigenous cows in the Indian society, especially in the Northern Mountain regions by taking Kangra district of Himachal Pradesh as an exemplar. This study highlights the relevance of experiential tourism that elucidates on the basis of cow tourism pertaining to health, mental and spiritual rejuvenation. Lastly, the paper is an attempt to integrate social entrepreneurship and cow tourism highlighting the relevance of experiential economy in empowering the local community.
Design/methodology/approach
The case study elucidates on the whence of Swadeshi Kamdhenu Gaushala (SKG), an initiative of Mr Rishi Dogra and Mr Rajesh Dogra, their immaculate micro-management and its benefits to the local community. It highlights how SKG is uplifting the socio-economic standards of the local villagers and providing a distinctive learning experience of indigenous knowledge to visitors. This study is qualitative in nature that uses narrative analysis of secondary data to recognise the importance of indigenous Indian cows, and case study analysis of interviews of SKG proprietors to understand the micro-management, production of organic products and community engagement in their social entrepreneurship.
Findings
The SKG is not only helping the local community in their livelihood but also creating value and positioning to the place on the tourist map. This study sheds some light on the importance of cow products in sectors such as agriculture, green energy and for human health and nutrition. The study also crystallizes the challenges faced by the cow rearers, at last the paper sorted out the benefits of cow tourism and how it can result in community empowerment and development.
Originality/value
The case study on SKG helps us in understanding the importance of social entrepreneurs in community empowerment and also the intervention of tourism in the sector that can bring new and different vertical to the tourism industry with experiential learning of the tourist, which results in knowledge sharing about the benefits of Indian cows and helps in creating and placing such destinations on tourist maps. This study attempts towards contributing to the existing knowledge, highlighting the benefits of social entrepreneurship and cow tourism for the society in general and local community in particular.
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Niraj S. Jagtap, Rajesh V. Wagh, Manish Kumar Chatli, Om Prakash Malav, Pavan Kumar and Nitin Mehta
This paper aims to highlight the candidature of papaya/Carica papaya L. extracts (PLE) and oregano/Origanum vulgare leaves extract (OLE) as novel natural antioxidants, which was…
Abstract
Purpose
This paper aims to highlight the candidature of papaya/Carica papaya L. extracts (PLE) and oregano/Origanum vulgare leaves extract (OLE) as novel natural antioxidants, which was further fortified into goat meat nuggets to evaluate quality changes and storage stability at refrigeration temperature (4 ± 1ºC) for 20 days.
Design/methodology/approach
Three different products, namely, control (without phyto-extracts), T−1: PLE (0.5 per cent) and T-2: OLE (1.0 per cent) fortified goat meat nuggets, were prepared and subjected for various quality attributes with relation to storage stability.
Findings
It was observed that pH significantly (p = 0.14) decreased till 10th day of storage i.e. from 6.49 to 6.32 (control), 6.37 to 6.28 (T−1) and 6.45 to 6.43 (T-2) afterword showed increasing trend till further storage of 20 days in control, as well as treated products. Water activity was non-significant (p = 0.01) on first day of storage and decreased up to 20th day. PLE treated product showed good margin of microbiological protection followed by OLE and least was found in control. L* value showed increasing trend (p = 0.03) throughout storage and ranged from 50.15 to 54.27, while a* values were decreased significantly from 10.36 to 9.06, 10.86 to 9.49 in PLE (p = 0.02) and OLE (p = 0.03), respectively. Sensory panel awarded the highest score for fortified goat meat nuggets, justifying the best quality attributes in term of texture attributes of the treated products. Thus, papaya and oregano leave extracts proved in the extension of shelf life and can be further harvested to develop functional goat meat nuggets.
Research limitations/implications
In search of novel bioactive phyto-extract, meat industry focussed most of the research towards natural anti-oxidants. In the view of same, the present research strategy was planned to examine candidature of Carica papaya L. and OLEs as novel natural antioxidant into meat system during aerobic packaging storage. Goat meat nuggets are amongst the most convenient and famous snack, as well as nutritious meat products, but lacks functional properties. Therefore, with implication of present research at practical level, meat industry can develop function goat meat nuggets by incorporating Carica papaya L. and Origanum vulgare extracts as natural and novel bioactive antioxidants with improved functionality.
Originality/value
This is the first attempt to develop functional goat meat nuggets incorporated with papaya/Carica papaya L. and oregano/OLE. This research can lead to be a pioneer work in meat science.
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Sujan Piya, Ahm Shamsuzzoha, Mohammed Khadem and Mahmoud Al Kindi
The purpose of this paper is to understand the drivers that create complexity in the supply chain and develop a mathematical model to measure the level of supply chain complexity…
Abstract
Purpose
The purpose of this paper is to understand the drivers that create complexity in the supply chain and develop a mathematical model to measure the level of supply chain complexity (SCC).
Design/methodology/approach
Through extensive literature review, the authors discussed various drivers of SCC. These drivers were classified into five dimensions based on expert opinion. Moreover, a novel hybrid mathematical model was developed by integrating analytical hierarchy process (AHP) and grey relational analysis (GRA) methods to measure the level of SCC. A case study was conducted to demonstrate the applicability of the developed model and analyze the SCC level of the company in the study.
Findings
The authors identified 22 drivers of SCC, which were further clustered into five complexity dimensions. The application of the developed model to the company in the case study showed that the SCC level of the company was 0.44, signifying that there was a considerable scope of improvement in terms of minimizing complexity. The company that serves as the focus of this case study mainly needs improvement in tackling issues concerning government regulation, internal communication and information sharing and company culture.
Originality/value
In this paper, the authors propose a model by integrating AHP and GRA methods that can measure the SCC level based on various complexity drivers. The combination of such methods, considering their ability to convert the inheritance and interdependence of drivers into a single mathematical model, is preferred over other techniques. To the best of the authors' knowledge, this is the first attempt at developing a hybrid multicriteria decision-based model to quantify SCC.
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The present study spotlights the single and multicriteria decision-making (MCDM) methods to determine the optimal machining conditions and the predictive modeling for surface…
Abstract
Purpose
The present study spotlights the single and multicriteria decision-making (MCDM) methods to determine the optimal machining conditions and the predictive modeling for surface roughness (Ra) and cutting tool flank wear (VB) while hard turning of AISI 4340 steel (35 HRC) under dry environment.
Design/methodology/approach
In this study, Taguchi L16 design of experiments methodology was chosen. The experiments were performed under dry machining conditions using TiSiN-TiAlN nanolaminate PVD-coated cutting tool on which Taguchi and responses surface methodology (RSM) for single objective optimization and MCDM methods like the multi-objective optimization by ratio analysis (MOORA) were applied to attain optimal set of machining parameters. The predictive models for each response and multiresponse were developed using RSM-based regression analysis. S/N ratios, analysis of variance (ANOVA), Pareto diagram, Tukey's HSD test were carried out on experimental data for profound analysis.
Findings
Optimal set of machining parameters were obtained as cutting speed: at 180 m/min., feed rate: 0.05 mm/rev., and depth of cut: 0.15 mm; cutting speed: 145 m/min., feed rate: 0.20 mm/rev. and depth of cut: 0.1 mm for Ra and VB, respectively. ANOVA showed feed rate (96.97%) and cutting speed (58.9%) are dominant factors for Ra and VB, respectively. A remarkable improvement observed in Ra (64.05%) and VB (69.94%) after conducting confirmation tests. The results obtained through the MOORA method showed the optimal set of machining parameters (cutting speed = 180 m/min, feed rate = 0.15 mm/rev and depth of cut = 0.25 mm) for minimizing the Ra and VB.
Originality/value
This work contributes to realistic application for manufacturing industries those dealing with AISI 4340 steel of 35 HRC. The research contribution of present work including the predictive models will provide some useful guidelines in the field of manufacturing, in particular, manufacturing of gear shafts for power transmission, turbine shafts, fasteners, etc.
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Shelza Dua, Sanjay Kumar, Ritu Garg and Lillie Dewan
Diagnosing the crop diseases by farmers accurately with the naked eye can be challenging. Timely identification and treating these diseases is crucial to prevent complete…
Abstract
Purpose
Diagnosing the crop diseases by farmers accurately with the naked eye can be challenging. Timely identification and treating these diseases is crucial to prevent complete destruction of the crops. To overcome these challenges, in this work a light-weight automatic crop disease detection system has been developed, which uses novel combination of residual network (ResNet)-based feature extractor and machine learning algorithm based classifier over a real-time crop dataset.
Design/methodology/approach
The proposed system is divided into four phases: image acquisition and preprocessing, data augmentation, feature extraction and classification. In the first phase, data have been collected using a drone in real time, and preprocessing has been performed to improve the images. In the second phase, four data augmentation techniques have been applied to increase the size of the real-time dataset. In the third phase, feature extraction has been done using two deep convolutional neural network (DCNN)-based models, individually, ResNet49 and ResNet41. In the last phase, four machine learning classifiers random forest (RF), support vector machine (SVM), logistic regression (LR) and eXtreme gradient boosting (XGBoost) have been employed, one by one.
Findings
These proposed systems have been trained and tested using our own real-time dataset that consists of healthy and unhealthy leaves for six crops such as corn, grapes, okara, mango, plum and lemon. The proposed combination of Resnet49-SVM and ResNet41-SVM has achieved accuracy of 99 and 97%, respectively, for the images that have been collected from the city of Kurukshetra, India.
Originality/value
The proposed system makes novel contribution by using a newly proposed real time dataset that has been collected with the help of a drone. The collected image data has been augmented using scaling, rotation, flipping and brightness techniques. The work uses a novel combination of machine learning methods based classification with ResNet49 and ResNet41 based feature extraction.
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Dharmendra B.V., Shyam Prasad Kodali and Nageswara Rao Boggarapu
The purpose of this paper is to adopt the multi-objective optimization technique for identifying a set of optimum abrasive water jet machining (AWJM) parameters to achieve maximum…
Abstract
Purpose
The purpose of this paper is to adopt the multi-objective optimization technique for identifying a set of optimum abrasive water jet machining (AWJM) parameters to achieve maximum material removal rate (MRR) and minimum surface roughness.
Design/methodology/approach
Data of a few experiments as per the Taguchi’s orthogonal array are considered for achieving maximum MRR and minimum surface roughness (Ra) of the Inconel718. Analysis of variance is performed to understand the statistical significance of AWJM input process parameters.
Findings
Empirical relations are developed for MRR and Ra in terms of the AWJM process parameters and demonstrated their adequacy through comparison of test results.
Research limitations/implications
The signal-to-noise ratio transformation should be applied to take in to account the scatter in the repetition of tests in each test run. But, many researchers have adopted this transformation on a single output response of each test run, which has no added advantage other than additional computational task. This paper explains the impact of insignificant process parameter in selection of optimal process parameters. This paper demands drawbacks and complexity in existing theories prior to use new algorithms.
Practical implications
Taguchi approach is quite simple and easy to handle optimization problems, which has no practical implications (if it handles properly). There is no necessity to hunt for new algorithms for obtaining solution for multi-objective optimization AWJM process.
Originality/value
This paper deals with a case study, which demonstrates the simplicity of the Taguchi approach in solving multi-objective optimization problems with a few number of experiments.
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Srinivas Rao Sriram, Saidireddy Parne, Venkata Satya Chidambara Swamy Vaddadi, Damodar Edla, Nagaraju P., Raji Reddy Avala, Vijayakumar Yelsani and Uday Bhasker Sontu
This paper aims to focus on the basic principle of WO3 gas sensors to achieve high gas-sensing performance with good stability and repeatability. Metal oxide-based gas sensors are…
Abstract
Purpose
This paper aims to focus on the basic principle of WO3 gas sensors to achieve high gas-sensing performance with good stability and repeatability. Metal oxide-based gas sensors are widely used for monitoring toxic gas leakages in the environment, industries and households. For better livelihood and a healthy environment, it is extremely helpful to have sensors with higher accuracy and improved sensing features.
Design/methodology/approach
In the present review, the authors focus on recent synthesis methods of WO3-based gas sensors to enhance sensing features towards toxic gases.
Findings
This work has proved that the synthesis method led to provide different morphologies of nanostructured WO3-based material in turn to improve gas sensing performance along with its sensing mechanism.
Originality/value
In this work, the authors reviewed challenges and possibilities associated with the nanostructured WO3-based gas sensors to trace toxic gases such as ammonia, H2S and NO2 for future research.
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Ranjith R. and S. Nalin Vimalkumar
The most difficult tasks in the design and development of products for diverse engineering applications were the selection of suitable materials. Choice of inappropriate process…
Abstract
Purpose
The most difficult tasks in the design and development of products for diverse engineering applications were the selection of suitable materials. Choice of inappropriate process variables leads to poor performance, which increases the cost of the product. The selection of the best option of available alternatives is important to improve the performance and productivity of the manufacturing enterprises.
Design/methodology/approach
The paper aims to develop Hybrid Multi-Criteria Decision Making (HMCDM) by integrating two potential optimization techniques Elimination Et Choix Traduisant la REalité and multi-objective optimization on the basis of ratio analysis. The weight of the criteria was calculated using the critic weight method.
Findings
The efficiency and flexibility of the proposed HMCDM technique were illustrated and validated by two examples. In the first case, the best electrode material among the five available alternatives was selected for the electrical discharge machining of AZ91/B4Cp magnesium composites. In the second case, the optimum weight percentage of composites providing the best tribological properties was chosen.
Originality/value
It was noted that the HMCDM methodology was quite simple to comprehend, easy to apply and provided reliable rankings of the material alternatives. The proposed hybrid algorithm is suitable for product optimization as well as design optimization.