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Some strategy authors suggest that in an emerging market a local conglomerate enjoys certain potential advantages over a smaller focused firm. It can leverage its corporate image to build customer loyalty and raise funds from the capital market. It can mobilise resources from within the group companies to invest in enhancing the corporate image, in developing its own management‐training centre, and for liaison with the government and bureaucracy. It can also avoid retrenchment of surplus employees by transferring them across the group companies. The authors, however, contend that many of the advantages mentioned above cannot be realised in practice and the top management finds it difficult to effectively manage a large conglomerate. They suggest a model, which will help a conglomerate decide which businesses to retain or divest. They also highlight certain strategies adopted by Indian firms to combat foreign competition in the domestic market.

The electronic nose is an array of chemical or gas sensors and associated with a pattern-recognition framework competent in identifying and classifying odorant or non-odorant and simple or complex gases. Despite more than 30 years of research, the robust e-nose device is still limited. Most of the challenges towards reliable e-nose devices are associated with the non-stationary environment and non-stationary sensor behaviour. Data distribution of sensor array response evolves with time, referred to as non-stationarity. The purpose of this paper is to provide a comprehensive introduction to challenges related to non-stationarity in e-nose design and to review the existing literature from an application, system and algorithm perspective to provide an integrated and practical view.

The authors discuss the non-stationary data in general and the challenges related to the non-stationarity environment in e-nose design or non-stationary sensor behaviour. The challenges are categorised and discussed with the perspective of learning with data obtained from the sensor systems. Later, the e-nose technology is reviewed with the system, application and algorithmic point of view to discuss the current status.

The discussed challenges in e-nose design will be beneficial for researchers, as well as practitioners as it presents a comprehensive view on multiple aspects of non-stationary learning, system, algorithms and applications for e-nose. The paper presents a review of the pattern-recognition techniques, public data sets that are commonly referred to as olfactory research. Generic techniques for learning in the non-stationary environment are also presented. The authors discuss the future direction of research and major open problems related to handling non-stationarity in e-nose design.

The authors first time review the existing literature related to learning with e-nose in a non-stationary environment and existing generic pattern-recognition algorithms for learning in the non-stationary environment to bridge the gap between these two. The authors also present details of publicly available sensor array data sets, which will benefit the upcoming researchers in this field. The authors further emphasise several open problems and future directions, which should be considered to provide efficient solutions that can handle non-stationarity to make e-nose the next everyday device.

This paper aims to study the effect of orange peel and moringa leaves extracts on microbiological safety, sensory quality, lipid oxidation and color properties of chicken sausages under frozen storage.

Chicken sausages were prepared by using orange peel, moringa leaves extracts and butylated hydroxytoluene (BHT). The sausages were stored in a freezer at −18°C. Samples were taken at a regular interval of 20 days from the day of production to spoilage of sausages and analyzed for microbiological safety, sensory quality, lipid oxidation and color properties.

In comparison to the control sausage, sausages having BHT, orange peel and moringa leaves extract had a significantly (p < 0.05) lower bacterial, yeast and mold count. All the sausages were microbiologically safe for consumption till the 100th day, and the results of the 120th day crossed the permissible limits. Sensory acceptability scores of sausages were good (>6) throughout the storage period. The color values of sausages were not affected by the addition of orange peel and moringa leaves extract. The extent of lipid oxidation increased during storage, and sausages with BHT, orange peel and moringa leaves extract had significantly (p < 0.05) lower values of thiobarbituric acid reactive substances and free fatty acids (FFAs) toward the end of the storage period.

The observations of this paper endorse the use of orange peel and moringa leaves extract in meat products formulation for acceptable storage stability under frozen conditions.

This research aims to propose a model that may be used to classify product attributes according to their effect on customer satisfaction within the services industry. It also aims to apply the model to vacation ownership products and to explore attributes related to both the purchase and use of the product: an owned luxury product.

Data from 3,231 vacation ownership customers of multiple international companies were analyzed using a modified Kano model and related questionnaire.

This study reveals the effect that specific product attributes have on customer satisfaction. It addresses previously unexplored attributes (i.e. sales techniques and hotel program benefits), confirms others previously identified with customer satisfaction (i.e. amenities, exchange benefits, hotel affiliation and vacation counselors) and reveals those that had no incremental effect on overall satisfaction (i.e. financing and activities).

Results of this study suggest that attributes have varying effects on customers’ overall satisfaction and submit that companies may wish to focus their efforts in particular areas to maintain or improve overall satisfaction. Doing so may create opportunities for companies to increase satisfaction, operate more efficiently or distinguish themselves within the marketplace.

This research is the first comprehensive examination of customer satisfaction related to the purchase and consumption of an owned luxury vacation product, reveals misconceptions related to certain product attributes, uncovers previously unidentified attributes, provides a model for examining customer satisfaction that could be applied across lodging products and provides a benchmark for future studies.

This paper is a literature review on use of artificial intelligence (AI) among agricultural value chain (AVC) actors, and it brings out gaps in research in this area and provides directions for future research.

The authors systematically collected literature from several databases covering 25 years (1994–2020). They classified literature based on AVC actors present in different stages of AVC. The literature was analysed using Nvivo 12 (qualitative software) for descriptive and content analysis.

Fifty percent of the reviewed studies were empirical, and 35% were conceptual. The review showed that AI adoption in AVC could increase agriculture income, enhance competitiveness and reduce cost. Among the AVC stages, AI research related to agricultural processing and consumer sector was very low compared to input, production and quality testing. Most AVC actors widely used deep learning algorithm of artificial neural networks in various aspects such as water resource management, yield prediction, price/demand forecasting, energy efficiency, optimalization of fertilizer/pesticide usage, crop planning, personalized advisement and predicting consumer behaviour.

The authors have considered only AI in the AVC, AI use in any other sector and not related to value chain actors were not included in the study.

Earlier studies focussed on AI use in specific areas and actors in the AVC such as inputs, farming, processing, distribution and so on. There were no studies focussed on the entire AVC and the use of AI. This review has filled that literature gap.

The purpose of this paper is to develop a model to estimate the value of information generated by and stored within vehicles to help people, businesses and researchers.

The authors provide a taxonomy for data within connected vehicles, as well as for actors that value such data. The authors create a monetary value model for different data generation scenarios from the perspective of multiple actors.

Actors value data differently depending on whether the information is kept within the vehicle or on peripheral devices. The model shows the US connected vehicle data market is worth between US$11.6bn and US$92.6bn.

This model estimates the value of vehicle data, but a lack of academic references for individual inputs makes finding reliable inputs difficult. The model performance is limited by the accuracy of the authors’ assumptions.

The proposed model demonstrates that connected vehicle data has higher value than people and companies are aware of, and therefore we must secure these data and establish comprehensive rules pertaining to data ownership and stewardship.

Estimating the value of data of vehicle data will help companies understand the importance of responsible data stewardship, as well as drive individuals to become more responsible digital citizens.

This is the first paper to propose a model for computing the monetary value of connected vehicle data, as well as the first paper to provide an estimate of this value.

Laser land leveling (LLL) is a climate-smart technology that improves water use efficiency and reduces risk in crop cultivation due to weather variability. Hence, this technology is useful for cultivating water-intensive crops in a sustainable way. Given this background, the state government of Karnataka initiated to promote LLL in drought-prone districts and selected Raichur district for implementation. Moreover, farmers in this district had observed drought situation during monsoon paddy growing season in 2018. Therefore, this study attempts to investigate the importance of LLL technology for paddy cultivation under drought conditions.

A primary survey with 604 farmer households had been conducted in Raichur in 2018. Among them, 50% are adopters of LLL who have been selected purposively and rest 50% are non-adopters who have grown paddy in the adjacent or nearest plot of the laser-leveled plot. The adoption and causal impact of LLL has been estimated using propensity score matching, coarsened exact matching and endogenous switching regression methods.

The result reveals a positive and significant impact of LLL on paddy yield and net returns to the farmers. The results indicate an increment of 12 and 16% in rice yield and net income, respectively, for LLL adopters in comparison to the non-adopters of LLL.

The major limitation of the study is that it does not adopt the method of experimental study due to certain limitations; hence, the authors employed a quasi-experimental method to look at the possible impact of adoption of LL.

There have been various agronomic studies focusing on the ex-ante assessment of the LLL. This study is an ex-post assessment of the technology on the crop yield and farmers' income in a dry semi-arid region of India, which, according to the authors, is the first in this approach.

The purpose of this paper is to present a literature review of the fresh produce supply chain management (FSCM). FSCM includes the processes from the production to consumption of fresh produce (fruits, flowers and vegetables).

Literature review is done by systematically collecting the existing literature over a period of 20 years (1989‐2009) and classifying it on the basis of structural attributes such as problem context, methodology and the product under consideration. The literature is also categorized according to the geographic region and year of publication.

There is an increase in interest towards FSCM still there is an absence of a journal with the prime attention towards FSCM. The key finding of this review is that the main interest is towards consumer satisfaction and revenue maximization with post‐harvest waste reduction being a secondary objective. It is revealed from the review that most of the literature is fragmented and is in silos. Lack of demand forecasting, demand and supply mismatch, lesser integrated approach etc are the major causes of concerns.

The authors have taken only the fresh produce (fruits, flowers and vegetables), authors may also look at other perishable items such as milk, meat, etc.

Result shows a product‐problem‐methodology mapping which may serve as a framework for the managers addressing issues in FSCM.

Most of the prior literature reviews are focused on a specific issue such as production planning or inventory management and ignore the broader perspective. There exists a need of having a detailed literature review covering all the operational issues in FSCM. This review fills this gap in the FSCM literature.