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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.

The purpose of this paper is to present and solve the problem of adaptive beamforming (ABF) for a uniform linear array (ULA) as an optimization problem. ABF mainly concerns with estimation of weights of antenna array so as to direct the major lobe in the direction of desired user and nulls in the direction of interfering signals with reduced side lobe level (SLL).

The potential of gravitational search algorithm is explored to optimize multi-objective fitness function for ABF using MATLAB software.

The performance of the algorithm has been compared by considering different number of interference signals at different power levels. The proposed algorithm presents good convergence rate and accurate steering of main lobe and nulls with reduced SLL compared to the well-known ABF technique, namely, minimum variance distortionless response (MVDR) and previously reported results. The simulation results are presented in tabular form.

The present work is limited to simulation. The researchers are encouraged to solve the problem of ABF using the proposed approach in hardware.

The application of proposed algorithm is to optimize multi-objective function for ABF with reduced SLL in linear antenna arrays.

Entrepreneurial marketing is a hybrid concept of entrepreneurship and marketing defining business for future challenges. This study aims to examine the following two issues of entrepreneurial marketing concerning impact of perceived quality on customer delight: first, identifying entrepreneurial marketing factors of delight and customer perceived quality to develop the model; second, applying the aforesaid model to discriminate customer delight in categories of gender as male delight and female delight.

In this endeavour, the authors examined the data collected from survey of Indian mobile users. In total ten perceived quality predictor variables are used to measure customer delight. Further, for identifying the difference in male delight and female delight, discriminant analysis is used.

The conceptual and empirical vantage point of study provides a framework to entrepreneurs emphasizing on male and female customers distinctly during marketing.

The results are used to sketch the profile of male and female customer segment, and innovative applications are discussed for business success. Though proposed approach is limited to gender, mobile users still open the avenue for researchers to work on other demographic factors and product categories.

The theoretical underpinnings warrant practical applications of quality dimensions in telecom market for creation of customer delight. The present gender classification of customer delight will assist the entrepreneurs, intrapreneurs and product developers in developing the competitive business policy.

The classification of male and female delight suggests the female segment of Indian telecom market is socially important and as similar to the male segment.

The study is a novel assessment of customer delight on gender quality perception contributing the newly explored concept of entrepreneurial marketing.

There exists insufficient literature on classification and taxonomy of tea leaves supply chain (TLSC), so the purpose of this paper is to study the existing TLSCs and classify them accordingly. Apart from this, the paper also focuses on identification of key decisions issues in the supply chains (SC) and developing a TLSC decision framework for the state of Assam in India.

The paper is based on a two-year detailed study on TLSC in Assam which encompasses 22 Tea Estates, 41 Small Tea Gardens (STGs) and a Research Institute (Tocklai Tea Research Institute). Secondary data were collected from relevant websites of various government organizations of India, company’s websites, annual reports, official statements from the companies, tea market reports, annual reports of the Indian Tea Association, the Tea Board of India, Tea Research Institute and published reports, etc.

The “point of origin” of TLSC is a tea garden, “point of consumption” is considered as a tea factory and green tea leaves (GTLs) forms the basic raw material. This SC includes mainly three players: Tea leaves growers, manufacturers of made tea and tea leaves agents. This study identifies the three types of TLSCs existing in Assam: TLSC1, TLSC2 and TLSC3. Among them, only TLSC1 is both responsive as well as an efficient chain, while the rest are only responsive chains. Later two SCs can be made efficient with the proposed TLSC4.

There is an insufficient literature on classification and taxonomy of TLSC, therefore the study (considerably the classification and taxonomy of TLSC) was developed from the primary data which were collected from the 22 Tea Estates and 41 STGs of four districts of Assam, because of limited time (two years). The study should have involved more tea estates and small tea gardens for better classification and taxonomy.

The proposed model suggests that small tea growers may create a co-operative whereby smaller tea gardens (STGs) (members of the co-operative) unite to act as a single large garden, set up their own co-operative factory and recruit permanent tea plucking laborers. This up-gradation of TLSC2 and TLSC3 to TLSC4 may enable a group of STGs to work in a manner similar to a Tea Estate.

To the best of authors’ knowledge this is one of the first studies to classify the TLSC in Assam.

The purpose of this paper is to explore channel conflict from the small retailer’s perspective in the context of emerging economies. The study investigates the factors and probable outcomes of perceived channel conflict.

A qualitative research design is adopted. This study involves semi-structured in-depth interviews with 14 small retailers, selected through the purposive sampling technique, in a capital city of North India.

This exploratory study reveals five key reasons for channel conflict in the context of small retailers: coercive sources of power, retailer dependence, competitive intensity, retailer characteristics, and distributors’ role performance. The findings also highlight channel exit and passive acceptance as two probable outcomes of perceived channel conflict. Conflict may also lead to changes in the channel structure, which may significantly affect the success of multinational firms in emerging economies. The paper concludes with a discussion and presents an agenda for further research.

Channel conflict is a well-researched area, but the context of most existing studies has been larger retailers in developed economies. The findings of this study provide the reasons for and outcomes of the channel conflict encountered by small retailers in emerging economies. This study should be of interest to marketing managers, especially of consumer packaged goods firms, who want to succeed in emerging economies. These managers can use insights from the study to manage channel conflict in the context of small retailers.

Addresses consumer perspectives of Radio Frequency Identification (RFID). States that some consumers put up barriers against new technology, while others welcome innovations. Idengtifies that market leaders can pretty much use this technology as they wish, meaning boom time for consumer product firms and pain for privacy advocates.

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.

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.

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.

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.

This paper aims to develop a slice-based representation of geometry and material information of a multi-material object to be produced by additive manufacturing. Representation of complex heterogeneous material allowing for the additive manufacturing-based build of a wide range of objects that are limited only by the constraints of the manufacturing process.

Initial 3D CAD models are created with multiple and functionally graded materials using an assembly model to create a single part with well-defined material regions. These models are then sliced to create the geometry and material boundaries required for each layer to enable layer-by-layer fabrication.

A representation schema is proposed to add multi-material attributes to a sliced file for additive manufacturing using the combination of material index and material geometry region. A modified common layer interface data format is proposed to allow for representation of a wide range of homogeneous and heterogeneous material for each slice. This format allows for a generic input for tool paths to be generated for each material of the layer.

The proposed approach allows for slice data representation for any material combination that can be defined mathematically. Three different material types, namely, composite material, functionally graded materials and combination thereof, are provided as examples. These data form the input data for subsequent tool path planning.

Cold chain has become an integral part of the supply chain domain. The purpose of this paper is to consider all the significant factors in a single study. This will result into a better model to study the effectiveness of a cold chain because there has been absence of such an integrated study.

The basis of the factors is justified by performing extensive literature review. Inter relations are drawn based on critical analysis of each factor and its implications on cold chain. Bayesian Network is used to develop the model.

The end result is an established model, depicting the interdependencies of the factors. The model ultimately provides effectiveness of a given cold chain when the corresponding values of factors are put in.

The findings will be helpful for government and non-government bodies to analyse the effectiveness of a cold chain. This can be used to increase the performance of different stages in the cold chain. From a business perspective, an investor can analyse the cold chains of various geographies in order to make an investment decision.

The value lies in developing and introducing new factors which were not considered in the related literature previously. To identify the inter relations among the factors in order to build a causal model is another contribution of the present paper. This would assist in decision-making process with respect to any given cold chain. It can be applied to any cold chain as proposed model is not specific to a particular country or material.