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This case study exposes students to conflicts with distributors, escalated scenarios of a trade association and the possible repercussions of such a scenario. Upon completion of this case study, the students will be able to understand the critical success factors for a distribution setup and alignment of channels for driving growth; understand and manage the power dynamics with a stakeholder, like trade associations, distribution reach, fallacies in managing the distributors and identifying the gaps; critically evaluate negotiation opportunities when a trade association is not directly related to the principal organization.

This case study showcased a conflict between the distributor and Universal Heater Industries (UHI), a leading player in the water heater business in India. In 2015, the global leadership of UHI identified India as an emerging market and undertook a complete management overhaul to implement a new growth plan. Several measures were put in place that leveraged the global product portfolio and new people were appointed to push the agenda. Manish Singhal, the national sales head of UHI, selected Kerala as the pilot state to implement the new plan. However, the projects failed, as the distributor escalated the treatment meted out by UHI to the Electrical Trade Association (ETA). Trade associations have had a history of playing truant with players like UHI, and because of this, business came to a complete halt. The UHI and ETA teams met once; however, the suggested closure by ETA needed to be aligned with UHI’s interests. Singhal’s dilemma deepened, and they had to decide the next steps.

This case study is suitable for a postgraduate marketing course in a segment on managing channels, intermediaries, distribution management and channel conflicts. The uniqueness of this case is in the dimension of the trade association and managing the stakeholders.

Teaching notes are available for educators only.

CSS8: Marketing.

A quick search of the headlines of major newspapers reveals a treasure trove of technology procurement gone wrong. While the private sector seems to adopt and implement new technology seamlessly and quickly to deliver for customers, the government struggles to accomplish technology purchases and integrations with the same ease. As governments in the United States are looking to retain their current workforce and attract the next generation of workers, the technological capabilities and ethos of governments will be paramount. With nearly every industry being transformed by technology and Generation T being the first generation to have an ingrained “technology first” mindset, the ability of governments to attract these workers depends, in large part, on the ability to transform their government technology culture, policies, and practices.

In this chapter, the authors examine the administrative branch and observe two key components at the root of most technology failures: poor organizational structure in the bureaucracy and the lack of an empowered Chief Information/Technology Officer. Building upon case studies from Massachusetts and California, this chapter looks at the factors related to failure or success to understand the technology procurement culture. The chapter concludes by presenting four key “best practice” principles of public policy and administration that can be implemented by almost any governmental entity to improve their acquisition and implementation of technology.

This research examines consumer reluctance to purchase organic food using the theory of innovation resistance and also extends the theory by investigating the moderating influence of eco-literacy. In addition, the authors used a multigroup analysis to identify differences between consumer segments.

Data collection used computer-assisted web interviewing and a prior screening process to confirm engagement. Through stratified quota sampling, 1,000 useable responses were obtained from 2,887 recipients.

The findings reveal barriers are significant inhibitors to adopting organic food. Among these barriers, usage, risk tradition and image barriers have a significant adverse influence on purchase intention. In contrast, the value barrier has a nonsignificant influence on the purchase intention of organic food. However, the study found that eco-literacy significantly reduces the negative influence of risk and tradition barriers on consumers' intentions. In addition, a multigroup analysis examines notable differences between consumer groups based on education, age and income.

This research has significant social implications for boosting sustainable consumption in Lithuania. It identifies key barriers to organic food adoption, emphasizing the need for strategic interventions. The study highlights eco-literacy as an essential tool in diminishing resistance to organic food, advocating for targeted educational initiatives. Additionally, it reveals the importance of tailored marketing strategies based on different consumer demographics. Overall, this study provides important insights to promote environmentally conscious consumer behavior and overcome resistance to innovation in the organic food sector.

This study expressively advances the understanding of intentional behavior by exploring organic food within Lithuania's Baltic economy. It authenticates the innovation resistance theory's applicability to organic food behavior in the region while emphasizing the moderating impact of eco-literacy in the link between barriers and purchase intention concerning organic food. Furthermore, using advanced methods such as partial least squares statistical modeling and multi-group analysis, the research reveals how barriers affect the purchase intention of organic food among different groups of consumers.

Online collaboration in today's world is a topic of genuine interest to Internet researchers. The purpose of this paper is to explore the role of group knowledge heterogeneity (GKH) in open collaboration performance using the mediating mechanisms of group cognition (GC) and interaction to understand the determinants of the success of online open collaboration platforms.

Study findings are based on partial least squares structural equation modeling (PLS-SEM), the formal mediation test and moderating effect analysis from Wikipedia's 160 online open collaborative groups.

For online knowledge heterogeneous groups, open collaboration performance is mediated by both GC and collaborative interaction (COL). The mediating role of GC is weak, while the mediating role of COL is strengthened when knowledge complexity (KC) is higher. By dividing group interaction into COL and communicative interaction (COM), the authors also observed that COL is effective for online open collaboration, whereas COM is limited.

These findings suggest that for more heterogeneous large groups, group interaction would explain more variance in performance than GC, offering an in-depth understanding of the relationship between group heterogeneity and open collaboration performance, answering what determines the success of online open collaboration platforms as well as explaining the inconsistency in prior findings. In addition, this study expands the application of Interactive Team Cognition (ITC) theory to the online open collaboration context.

High resolution simulations of body-internal electric field strengths induced by magneto-quasistatic fields from wireless power transfer systems are computationally expensive. The exposure simulation can be split into two separate simulation steps allowing the calculation of the magnetic flux density distribution, which serves as input into the second simulation step to calculate the body-internal electric fields. In this work, the magnetic flux density is interpolated from in situ measurements in combination with the scalar-potential finite difference scheme to calculate the resulting body-internal field. These calculations are supposed to take less than 5 s to achieve a near real-time visualization of these fields on mobile devices. The purpose of this work is to present an implementation of the simulation on graphics processing units (GPUs), allowing for the calculation of the body-internal field strength in about 3 s.

This work uses the co-simulation scalar-potential finite difference scheme to determine the body-internal electric field strength of human models with a voxel resolution of 2 × 2 × 2 mm³. The scheme is implemented on GPUs. This simulation scheme requires the magnetic flux density distribution as input, determined from radial basis functions.

Using NVIDIA A100 GPUs, the body-internal electric field strength with high-resolution models and 8.9 million degrees of freedom can be determined in about 2.3 s.

This paper describes in detail the used scheme and its implementation to make use of the computational performance of modern GPUs.

Customised capacity building is thought to be essential for organisations. However, empirical studies are lacking with respect to its effect on employee outcomes. This study aims to examine the effect of customised capacity building on employee outcomes including employee empowerment and employee engagement through employee learning in Ghanaian local government institutions.

Valid responses from 281 employees of Metropolitan, Municipal, and District Assemblies (MMDAs) in Ghana were collected through a survey. A structural equation model was used to analyse the data and test the hypotheses formulated.

The results showed that customised capacity building has an effect on employee learning, employee empowerment, and employee engagement. Employee learning also had an effect on employee empowerment and employee engagement. In addition, employee learning partially mediated the effect of customised capacity building on both employee empowerment and employee engagement.

This study is of particular relevance to public organisations. As there is a dearth of studies focusing on customised capacity building, this study provides insight into incorporating the phenomenon into public sector organisations to enhance employee learning, empowerment and engagement.

Communities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is critical for analyzing complex systems in various areas ranging from collaborative information to political systems. Given the different characteristics of networks and the capability of community detection in handling a plethora of societal problems, community detection methods represent an emerging area of research. Contributing to this field, the authors propose a new community detection algorithm based on the hybridization of node and link granulation.

The proposed algorithm utilizes a rough set-theoretic concept called closure on networks. Initial sets are constructed by using neighborhood topology around the nodes as well as links and represented as two different categories of granules. Subsequently, the authors iteratively obtain the constrained closure of these sets. The authors use node mutuality and link mutuality as merging criteria for node and link granules, respectively, during the iterations. Finally, the constrained closure subsets of nodes and links are combined and refined using the Jaccard similarity coefficient and a local density function to obtain communities in a binary network.

Extensive experiments conducted on twelve real-world networks followed by a comparison with state-of-the-art methods demonstrate the viability and effectiveness of the proposed algorithm.

The study also contributes to the ongoing effort related to the application of soft computing techniques to model complex systems. The extant literature has integrated a rough set-theoretic approach with a fuzzy granular model (Kundu and Pal, 2015) and spectral clustering (Huang and Xiao, 2012) for node-centric community detection in complex networks. In contributing to this stream of work, the proposed algorithm leverages the unexplored synergy between rough set theory, node granulation and link granulation in the context of complex networks. Combined with experiments of network datasets from various domains, the results indicate that the proposed algorithm can effectively reveal co-occurring disjoint, overlapping and nested communities without necessarily assigning each node to a community.

This study carries important practical implications for complex adaptive systems in business and management sciences, in which entities are increasingly getting organized into communities (Jacucci et al., 2006). The proposed community detection method can be used for network-based fraud detection by enabling experts to understand the formation and development of fraudulent setups with an active exchange of information and resources between the firms (Van Vlasselaer et al., 2017). Products and services are getting connected and mapped in every walk of life due to the emergence of a variety of interconnected devices, social networks and software applications.

The proposed algorithm could be extended for community detection on customer trajectory patterns and design recommendation systems for online products and services (Ghose et al., 2019; Liu and Wang, 2017). In line with prior research, the proposed algorithm can aid companies in investigating the characteristics of implicit communities of bloggers or social media users for their services and products so as to identify peer influencers and conduct targeted marketing (Chau and Xu, 2012; De Matos et al., 2014; Zhang et al., 2016). The proposed algorithm can be used to understand the behavior of each group and the appropriate communication strategy for that group. For instance, a group using a specific language or following a specific account might benefit more from a particular piece of content than another group. The proposed algorithm can thus help in exploring the factors defining communities and confronting many real-life challenges.

This work is based on a theoretical argument that communities in networks are not only based on compatibility among nodes but also on the compatibility among links. Building up on the aforementioned argument, the authors propose a community detection method that considers the relationship among both the entities in a network (nodes and links) as opposed to traditional methods, which are predominantly based on relationships among nodes only.

Jamun is an underutilised fruit crop of India whose utilization needs to be considerably increased. Despite its impressive nutritional profile and several health benefits, its cultivation is still very limited. Hence, this study aims to highlight the essential nutrients, health benefits and value-added products of jamun fruit and seed.

Major sources of bibliometric information such as Web of Science, Scopus, PubMed and Google Scholar were extensively searched with keywords such as nutritional composition of jamun, bioactive compounds, health benefits and jamun-based food products to obtain a database of 317 papers. Thirty four publications met the criteria for review.

Jamun fruit (Syzygium cumini L.) is known for its attractive colour, astringent taste, enormous nutrients and nutraceutical properties; nevertheless, this fruit is seasonal, perishable and underutilised. Furthermore, the fruit is used for the treatment of diabetes and also possess anti-cancer, anti-inflammatory, anti-obesity and cardio-protection properties. Investigations were done on the production of value-added food products such as read-to-serve beverages, jam, cookies and cake from jamun due to their several health benefits. The present review was an attempt to provide spotlights on the health benefits and food product applications of jamun fruit and its by-products.

This review systematically collates evidence from various published sources regarding the nutritional profile, development of food products from jamun and their health benefits. The compiled information will help researchers and professional become aware of the significance of jamun fruit and thereby enhances its processing and applications in food systems.

This purpose of this study is to examine the association between earnings management (accruals earnings management (AEM) and/or real activities manipulation (RAM)) and firm underperformance following seasoned equity offerings (SEOs) using cross-country data.

The study applies ordinary least squares regression analyses to a sample of 11,764 observations on firms from 22 countries over the period from 2005 to 2017. The methods include weighted least squares regression, sub-sampling approach and alternative measures of firm performance, earnings management and legal regime for robustness tests as well as a two-stage least squares instrumental variable (IV) approach to address endogeneity concerns.

The results suggest that RAM has a greater negative impact on post-SEO performance than AEM. The result is economically significant for RAM only. The results also reveal that the negative impact of earnings management, in particular RAM, on post-SEO performance is greater in countries with a strong legal regime than in other countries.

Earnings management around SEOs has important implications for investors, regulators and policymakers. The study suggests that policymakers should improve the current legal conditions to promote fairness in the equity market.

The results from the cross-country data support earlier results from single-country studies on the impact of earnings management on post-SEO performance. The study also provides new evidence on the variation in the impact of earnings management according to the strength of the legal regime operating in a country.

This study aims to enhance understanding of malnutrition's effect on academic achievement of primary school students.

This is a descriptive, cross-sectional design built on Roy's adaptation model (RAM). This study uses a random cluster sample, consisting of 453 primary school students. Contextual stimuli (mother's educational level, income and child’s breakfast eating) and focal stimuli (wasting, thinness, body mass index and stunting) were examined regarding adaptive responses to student’s academic achievement.

The investigation revealed that Model 1, which took into account factors of age, gender, the frequency of breakfast, income, the number of family members and the education of mothers, explained 12% (R² = 0.12) of the variance in academic achievement. Stuntedness (β = −3.2 and p < 0.01), BMI (β = 0.94 and p < 0.001), family income per month (β = 5.60 and p < 0.001) and mother's education (β = 2.79 and p < 0.001) were the significant predictors in Model 2.

This study provides evidence that malnutrition is associated with ineffective academic achievement. Moreover, variables such as the mother's level of education, family income and the child’s breakfast consumption have a significant impact on academic achievements.

RAM is a useful framework for determining factors affecting people's reactions to difficult circumstances.