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Entrepreneurship, Small Business, Small-scale Dairy Farmers

This case is appropriate for undergraduate final year/senior as well as graduate-level programme students.

This case explores the life of Saravanan, a small-scale dairy farmer in Malaysia. He inherited the business from his father. Small-scale farmers in Malaysia own farms with 30 (or fewer) milking cows. Over the years, milk consumption had been on the rise, but production was less than promising. Besides low-quality milk, Saravanan often experienced issues of low milk yield. Selling fresh milk as his only source of income and the milk collection centre as his sole marketing channel, Saravanan was caught in a financially tight situation when product diversification and marketing initiatives were limited. Saravanan’s problems began with rejected fresh milk, which landed him with zero income for the day. This issue was detected when the authorities identified a few contaminated batches of milk during a site visit. The problem compounded when Saravanan had to settle three months’ debt with the feed supplier on the same day. Saravanan’s predicament echoed the plight faced by small-scale farmers in Malaysia. After managing the farm for more than 30 years, Saravanan had plans to pass it to his son, Mugunthan. However, doubts about the sustainability of the business remained. Would Mugunthan suffer the same dire fate? Would he be able to find a way out? Based on the problem-solving framework, the case attempts to identify and assess the problems faced by small-scale dairy farmers in Malaysia, and at the same time, to suggest solutions that will ensure the sustainability of their business.

After attempting the case, students should learn to empathise with the hardship small-scale dairy farmers endure in the pursuit of their businesses, analyse issues and determine the root causes of the problems faced by small-scale dairy farmers in Malaysia based on the problem-solving framework, generate and justify sustainable solutions to solve the problems faced by these dairy farmers and present the case, discuss and work in teams, and critically offer sustainable solutions based on framework and theories.

Teaching notes are available for educators only.

CSS 3: Entrepreneurship.

Intelligent prediction of node localization in wireless sensor networks (WSNs) is a major concern for researchers. The huge amount of data generated by modern sensor array systems required computationally efficient calibration techniques. This paper aims to improve localization accuracy by identifying obstacles in the optimization process and network scenarios.

The proposed method is used to incorporate distance estimation between nodes and packet transmission hop counts. This estimation is used in the proposed support vector machine (SVM) to find the network path using a time difference of arrival (TDoA)-based SVM. However, if the data set is noisy, SVM is prone to poor optimization, which leads to overlapping of target classes and the pathways through TDoA. The enhanced gray wolf optimization (EGWO) technique is introduced to eliminate overlapping target classes in the SVM.

The performance and efficacy of the model using existing TDoA methodologies are analyzed. The simulation results show that the proposed TDoA-EGWO achieves a higher rate of detection efficiency of 98% and control overhead of 97.8% and a better packet delivery ratio than other traditional methods.

The proposed method is successful in detecting the unknown position of the sensor node with a detection rate greater than that of other methods.

The purpose of this paper is to discuss how the rush of technological change will consolidate the worldwide reach of the internet with more capacity, specifically to control the physical world, including the machines, industrial facilities and frameworks that characterize cutting-edge technology.

The data were collected from 203 respondents predominantly from emerging economies, specifically India and SEA. Most of the participants are working professionals. Structural equation modelling was used to analyze data, as it is a popular statistical technique because of its ability to model selected independent variables and take into account all possible forms of measurement error to test an entire theory.

The Industrial Internet of Things (IIOT) platform comprises four fundamental capabilities: connectivity, big data, advanced analytics and application development. The IIOT has the potential to provide a high level of synergies between the 4 Ms of manufacturing, namely, man, machine, material and method.

The collected data are predominately from India and SEA (close to 75 per cent), while contributions from other regions are comparatively less, so the findings cannot be generalized to the global context.

It is in the interest of service providers to collaborate and provide a universal solution to retain legacy systems to minimize the investment and reduce the security threat, which could boost IIOT adoption while ensuring that manufacturers are able to leverage this new technology efficiently.

The framework obtained has good quality of validity and reliability indicators. Thus, an alternative framework has been added to customer expectation which is currently a popular topic in the technological changes.

Since the handling of environmentally sensitive products requires close monitoring under prescribed conditions throughout the supply chain, it is essential to manage specific supply chain risks, i.e. maintaining good environmental conditions, and ensuring occupational safety in the cold environment. The purpose of this paper is to propose an Internet of Things (IoT)-based risk monitoring system (IoTRMS) for controlling product quality and occupational safety risks in cold chains. Real-time product monitoring and risk assessment in personal occupational safety can be then effectively established throughout the entire cold chain.

In the design of IoTRMS, there are three major components for risk monitoring in cold chains, namely: wireless sensor network; cloud database services; and fuzzy logic approach. The wireless sensor network is deployed to collect ambient environmental conditions automatically, and the collected information is then managed and applied to a product quality degradation model in the cloud database. The fuzzy logic approach is applied in evaluating the cold-associated occupational safety risk of the different cold chain parties considering specific personal health status. To examine the performance of the proposed system, a cold chain service provider is selected for conducting a comparative analysis before and after applying the IoTRMS.

The real-time environmental monitoring ensures that the products handled within the desired conditions, namely temperature, humidity and lighting intensity so that any violation of the handling requirements is visible among all cold chain parties. In addition, for cold warehouses and rooms in different cold chain facilities, the personal occupational safety risk assessment is established by considering the surrounding environment and the operators’ personal health status. The frequency of occupational safety risks occurring, including cold-related accidents and injuries, can be greatly reduced. In addition, worker satisfaction and operational efficiency are improved. Therefore, it provides a solid foundation for assessing and identifying product quality and occupational safety risks in cold chain activities.

The cold chain is developed for managing environmentally sensitive products in the right conditions. Most studies found that the risks in cold chain are related to the fluctuation of environmental conditions, resulting in poor product quality and negative influences on consumer health. In addition, there is a lack of occupational safety risk consideration for those who work in cold environments. Therefore, this paper proposes IoTRMS to contribute the area of risk monitoring by means of the IoT application and artificial intelligence techniques. The risk assessment and identification can be effectively established, resulting in secure product quality and appropriate occupational safety management.

Introduction: Historically, the corporate governance (CG) framework was designed primarily to safeguard the economic interests of shareholders, as a result of political and legal interventions, developing into an effective instrument for stakeholders and society in general.

Purpose: The core objectives of the study include: identifying journals/publications responsible for publishing CG studies in India, key CG issues covered by CG researchers, the amount of high-impact CG literature across different time periods, sectors/industries covered by CG researchers and different research instruments (quantitative or qualitative) used in CG studies in India.

Design/methodology: The chapter used a sample of 130 corporate governance studies that fulfil the selection criteria, drawn from the repository of over 100 reputed journals that are either recognised by the Australian Business Deans Council (ABDC) or indexed by SCOPUS. A systematic literature review has been carried out pertaining to CG issues in India, based on various statistical tools, data, industries, research outlets & citations, etc.

Findings: The results show an overwhelming number of studies have assessed the relationship between CG variables and firm performance, which could be measured through a variety of performance metrics such as ROA and ROI. Apart from empirical analysis, many conceptual studies use repetitive basic statistical tools like descriptive statistics or regression analysis. The chapter offers insights into current achievements and future development.

Originality/value: This bibliometric study is a useful guide for policymakers, corporate leaders, research organisations and management faculty to draw insights from work produced by eminent researchers in GC in India.

Data mining is the process of detecting knowledge from a given huge data set. Among the data set, multimedia is the data which contains diverse data such as audio, video, image, text and motion. In this growing field of video data, mining the video data plays vital role in the field of video data mining. In video data mining, video data are grouped into frames. In this vast amount of video frames, the fast retrieval of needed information is important one. This paper aims to propose a Birch-based clustering method for content-based image retrieval.

In image retrieval system, image segmentation plays a very important role. A text file, normally, is divided into sections, that is, piece, sentences, word and character for this information which are organized and indexed effectively like in a video, the information is dynamic in nature and this information is converted to static for easy retrieval. For this, video files are divided into a number of frames or segments. After the segmentation process, images are trained for retrieval process, and from these, unwanted images are removed from the data set. The noise or unwanted image removal pseudo-code is shown below. In the code image, pixel value represents the value of the difference between the two adjacent image pixel values. By assuming a threshold for the image value, the duplicate images are found. After finding the duplicate image, it is removed from the data set. Clustering is used in many applications as a stand-alone tool to get insight into data distribution and as a pre-processing step for other algorithms (Ester et al., 1996). Specifically, it is used in pattern recognition, spatial data analysis, image processing, economic science document classification, etc. Hierarchical clustering algorithms are classified as agglomerative or divisive. BRICH uses clustering attribute (CA) and clustering feature hierarchy (CA_Hierarchy) for the formation of clusters. It perform multidimensional data objects. Every BRICH algorithm based on the memory-oriented information, that is, memory constrains, is involved in the processing of the data sets. This information is represented in Figures 6-10. For forming clusters, they use the amount of object in the cluster (A), the sum of all points in the data set (S) and need the square value of the all objects (P).

The proposed technique brings an effective result for cluster formation.

BRICH uses a novel approach to model the degree of inter-connectivity and closeness between each pair of clusters that takes into account the internal characteristics of the clusters themselves.

This paper aims to uncover an understanding of how the quality assurance measures used by the property management teams of Grade-B high-rise office blocks influence the satisfaction and retention of tenants and walk-in users.

This study used a case study design backed up by qualitative and quantitative research approaches on a sample size of 90 respondents, including tenants, landlords, property managers and walk-in building users. The data were collected predominantly through interview guides and transcribed, coded and illustrated by the aid of ATLAS.ti software. Data reporting was through tables, graphs and themed direct quotations and eventual discussion. The in-depth/structured interview sessions took between 40 min and 1 h, and the walk-along interviews ranged between 30 and 40 min. Content analysis through thematic coding, categorisation and analysis were used in handling qualitative data. Direct quotes from participant responses from interview transcripts were inserted in line with the themes. And participants allocated pseudo-names to guard their confidentiality.

The key themes that linked quality assurance measures of access systems and tenant retention included distribution of access systems, the retention trends, quality assurance measures followed and the tenant experiences regarding how complaints are handled.

It is important to understand how tenants and other users of high-rise buildings experience their performance and inclusiveness. In a place like Kampala city, such a phenomenon can be proven through tenants sustaining their tenures/use of the facilities. Yet, to the best of the authors’ knowledge, presently no empirical studies have explored such a connection.

The purpose of this paper is to increase awareness of the value that can be derived from embracing customer service and ensuring tenant satisfaction.

This literature review synthesizes the results of research conducted around the world to examine the value proposition of customer-focused property management. The literature was identified through a search of academic journal articles and industry publications, together with the authors' experience of working with industry practitioners. Drawing on these sources, the authors identify five emerging trends that are likely to make customer service an even more critical part of property managers' jobs and propose best practices to help property managers actualize the benefits.

Three themes emerge from the literature. First, property managers can add significant value to real estate portfolios by delivering high-quality customer service to tenants. Second, emerging trends in the real estate industry are creating new opportunities for property managers to participate in the value creation process. Third, there are a number of steps property managers can take to leverage customer service to benefit the real estate investors they represent.

Real estate investors, and the property managers working on their behalf, can realize the advantages of customer-focused property management by adopting a series of straightforward best practices, which are discussed in the paper.

The trends identified and practical steps proposed are likely to be all the more relevant in the aftermath of the Covid-19 pandemic.

This research should increase awareness of customer-focused property management and the mechanisms through which it can affect real estate returns. The ten best practices proposed should help property managers deliver effective service to tenants and achieve the associated financial benefits for investors.

This paper aims to model the mediating effects of facilitating conditions and innovativeness in the industrial Internet of Things (IIoT) and blockchain technology (BT) on manufacturing supply chain performance (MSCP).

Partial least square structural equation modelling was used to test the goodness of the model fit and hypotheses by using SmartPLS 3.3.3. Data was collected from 464 managers in Pakistan’s automotive industry through a stratified random sampling technique.

IIoT, BT, facilitating conditions and innovativeness significantly enhanced the MSCP. Therefore, the mediation between facilitating conditions and innovativeness to IIoT and BT adoption was significant in the MSCP.

The adoption of digital technology to improve the MSCP can assist companies in reducing the cost of complex procurement, production and distribution processes through secured and efficient operations. Furthermore, organisations must establish a conducive atmosphere that fosters experimentation, collaboration and resource allocation towards technological advancements to capitalise on the advantages of these technologies effectively.

This study developed a research model integrating IIoT technology, BT, facilitating conditions and innovativeness to determine the MSCP under the resource-based view theory. The outcome of this study could help organisations design a framework to improve supply chain performance by integrating innovativeness.