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This paper aims to present social trust as a variable of influence by demonstrating the possibilities of trusted social nodes to improve influential capability and rate of successfully influenced social nodes within a social networking environment.

This research will be conducted using simulated experiments. The base algorithm in research uses genetics algorithm diffusion model (GADM) where it carries out social influence calculations within a social networking environment. The GADM algorithm will be enhanced by integrating trust values into its influential calculations. The experiment simulates a virtual social network based on a social networking site architecture from the data set used to conduct experiments on the enhanced GADM and observe their influence capabilities.

The presence of social trust can effectively increase the rate of successfully influenced social nodes by factorizing trust value of one source node and acceptance rate of another recipient node into its probabilistic equation, hence increasing the final acceptance probability.

This research focused exclusively on conceptual mathematical models and technical aspects so far; comprehensive user study, extensive performance and scalability testing is left for future work.

Two key contributions of this paper are the calculation of social trust via content integrity and the application of social trust in social influential diffusion algorithms. Two models will be designed, implemented and evaluated on the application of social trust via trusted social nodes and domain-specified (of specific interest groups) trusted social nodes.

This paper aims to extend previously reported research on the Sisyphus Effect in consumers’ decision making to consumers classified as either maximizers or satisficers. The purpose of this study was to examine whether the Sisyphus Effect influences consumer behavior related to purchasing brand-extension products and explore factors that influence the Sisyphus Effect in brand extension.

A survey was administered to participants in three studies. A convenience sample consisting of 875 participants was asked to complete the questionnaires. The authors assessed whether the participants were maximizers or satisficers. In addition, the participants were given information on brand-extension products that differed in the level of involvement and price and were asked whether they would purchase them.

Using regression analysis, the authors found that consumers’ willingness to purchase extended products became weaker as maximization tendencies became stronger. In addition, purchase involvement was confirmed as a situational factor that can increase maximization tendencies even in individuals who do not maximize as a default strategy. Finally, for low-involvement extended products, the authors found that product price played a moderating role in the negatively correlated relationship between maximization tendency and willingness to purchase a product.

This study suggests avenues to increase the effectiveness of a brand-extension strategy and illustrates some tactics that are not likely to be successful.

The purpose of this paper is to integrate Modigliani-Miller (MM) theory and stock repurchases strategy to procure a practical concept for capital decision.

No-arbitrage proof model deduction was used in this study. The authors consider corporate tax and funding sources as two crucial factors drawn in the model. The paper derives some propositions by trichotomy property and keeps the key assumptions of MM Capital Structure Theory.

There are two different effects on firm's value through stock repurchases. The positive effect occurs on firm's value through stock repurchases with loan fund. And the negative impact exists on firm's value through stock repurchases with idle fund.

Notably, in the real world there are three limitations with such an arbitrage transaction (Stulz, 2000). The first one is the default risk, and the second one is transaction costs and the last one is the perfect credit market assumption. In the near future, the authors suggest it would be interesting to involve the interest rate factor and contingent tax variable into our model.

On the basis of no arbitrage opportunity, this paper considers both trichotomy property and MM theory. It proves the share repurchase strategy should be financed by borrowing fund. In contrast, share repurchase should not be executed with idle fund because of opportunity cost.

In online social network analysis, the problem of identification and ranking of influential nodes based on their prominence has attracted immense attention from researchers and practitioners. Identification and ranking of influential nodes is a challenging problem using Twitter, as data contains heterogeneous features such as tweets, likes, mentions and retweets. The purpose of this paper is to perform correlation between various features, evaluation metrics, approaches and results to validate selection of features as well as results. In addition, the paper uses well-known techniques to find topical authority and sentiments of influential nodes that help smart city governance and to make importance decisions while understanding the various perceptions of relevant influential nodes.

The tweets fetched using Twitter API are stored in Neo4j to generate graph-based relationships between various features of Twitter data such as followers, mentions and retweets. In this paper, consensus approach based on Twitter data using heterogeneous features has been proposed based on various features such as like, mentions and retweets to generate individual list of top-k influential nodes based on each features.

The heterogeneous features are meant for integrating to accomplish identification and ranking tasks with low computational complexity, i.e. O(n), which is suitable for large-scale online social network with better accuracy than baselines.

Identified influential nodes can act as source in making public decisions and their opinion give insights to urban governance bodies such as municipal corporation as well as similar organization responsible for smart urban governance and smart city development.

This paper attempts to reconcile two opposing views of the strategies and conduct of closely held firms: that of entrepreneurship and that of family business. The former view suggests that these firms tend to be value maximizing organizations that pursue growth strategies and outperform. The latter often argues that these businesses are utility maximizers that pursue conservative harvest strategies and fail to outperform.

In order to reconcile the controversy, this paper examines the literature in an attempt to relate ownership priorities and risk taking preferences to governance distinctions relating to family involvement, ownership, and management.

It concludes that the value‐maximization expectations of the entrepreneurship literature apply only to lone or unrelated founder businesses whose owners, unencumbered by family distractions, embrace growth and outperform. By contrast the utility‐maximization expectations of the family business literature apply when there are multiple family owners or executives. These parties are argued to be harvest‐oriented, mediocre performers, especially after a new generation has entered the firm. This may be because their priorities and loyalties are shared between business and family considerations. However, family and lone founder firm outcomes are argued to be further shaped by owners' levels of control and ownership, their managerial roles, and the breadth of family personal and generational involvement.

The analysis has implications for the effective governance, board composition, and management of these different types of firms.

The paper reconciles two important literatures to derive implications for strategy and performance that must be addressed by agents of corporate governance in family and founder firms.

Niskanen (1971) established an influential and enduring model of bureaucrats as budget maximizers. Since this theory’s inception, most empirical tests have demonstrated the limited validity of Niskanen’s vision. Using state agency heads as an analytical unit, this paper further develops ways that the rational choice assumptions inadequately characterize bureaucratic budget aspirations: First, instead of being self-interested, many bureaucrats focus on the interests of collectives across governmental and societal actors, and second, an enhanced focus on transparency in presentation of budgetary and programmatic information reduces the information asymmetry that is central to Niskanen’s theory. The findings show that intentions based in a broad public interest and motivations of accountability and transparency tend to reduce, rather than enhance, growth aspirations in state-level bureaucrats.

The social media revolution has brought tremendous change in business strategies for marketing and promoting the products and services. Online social networks have become prime choice to promote the products because of the large size of online communities. Identification of seed nodes or identifying the users who are able to maximize the spread of information over the network is the key challenge faced by organizations. It is proved as non-deterministic polynomial-time hard problem. The purpose of this paper is to design an efficient algorithm for optimal seed selection to cover the online social network as much as possible to maximize the influence. In this approach, agglomerative clustering is used to generate the initial population of seed nodes for GA.

In this paper agglomerative clustering based approach is proposed to generate the initial population of seed nodes for GA. This approach helps in creating the initial populations of Genetic algorithm from different parts of the network. Genetic algorithm evolves this population and aids in generating the best seed nodes in the network.

The performance of of proposed approach is assessed with respect to existing seed selection approaches like k-medoid, k-means, general greedy, random, discounted degree and high degree. The algorithms are compared over networks data sets with varying out-degree ratio. Experiments reveal that the proposed approach is able to improve the spread of influence by 35% as compared to contemporary techniques.

This paper is original contribution. The agglomerative clustering-based GA for optimal seed selection is developed to improve the spread of influence in online social networks. This paper is of immense importance for viral marketing and the organizations willing to promote product or services online via influential personalities.

The motivation for this paper is to provide a deeper understanding of some of the important and trending topics in the sport industry. Besides this, to consider the changing landscape of ownership, strategies and organisation of several types of sports in the contemporary environment, there is a need for deeper contextual knowledge of how different sport/s, leagues, clubs, associations, teams etc. own, organise and strategise at local to national to regional to international contexts.

This paper introduces the special issue on the sport industry that welcomes three interesting contributions of focusing on the important themes related to the sport industry. They include (1) digital transformation and needed capabilities, (2) fan engagement in a digital way using social media and finally (3) the relevance and relation of social capital in the organisational strategy of sports organisations.

Four generic insights related to the themes and emerging trends in marketisation, ownership and digitalisation strategies in the sport industry are introduced in this paper. The first insight is that sports digitalisation and politicisation significantly impact the strategising, organising and networking activities of sports owners to foster value capture. Secondly, owners’ performance duality of marketisation of their sports clubs and vested business interests unravels the paradoxes of idealism and profit-maximisation. Thirdly, individual sports stars position themselves as an influential platform of value creation through on-field performance, social engagement and self-interests. Finally, the fourth insight is that the dual aims of sporting on-field success and profit making of large capital-funded sports clubs endanger the autonomy and governance of sports bodies/associations and an equitable sporting competition environment.

This paper provides an overview and reflections on the contributions of the papers in this special issue. The papers give different perspectives on how sport has been influenced by the development in society (1) with increasing digitalisation influencing organising of sport clubs and strategies for engaging fans, and (2) the influence of marketisation and politics in ownership strategies. As such, four insightful reflections are developed based on the originality of the contributions and the related extant literature presented on the themes of marketisation, ownership and digitalisation of the sport industry.

The purpose of this paper is to present an integrated analysis of determining factors of farmers’ genetically modified (GM) technology adoption behavior, with a special emphasis on information acquisition, knowledge accumulation, product attributes and technology traits.

Extending the expected profit maximization framework into a random utility model which accommodates joint decisions of information acquisition and technology adoption, the authors use the full information maximum likelihood method to yield both consistent and efficient estimates. The model is applied to a field survey collecting a sample of 141 randomly selected bananas farmers.

The empirical results indicate information acquired through social network will increase the probability of adoption. Knowledge accumulation as depicted by education and farming experience is found to play a role in farmers’ technology adoption, whereas disease-resistant technology trait and flavor-enriching product attribute of GM bananas also appear to be important determinants for GM seeds adoption in Taiwan.

Empirical evidence supports significance of technology traits and product attributes in farmer's GM technology adoption, suggesting the close collaboration between industry, government and academia is the key to successful commercialization of GM crops.

Understanding the determinants of farmers’ GM technology adoption can serve as the basis for promoting new biotechnology, and thus can facilitate the establishment of tenable solutions to food security issues.

This paper is the first attempt to incorporate information acquisition into the behavioral analysis of GM technology adoption. The present study also extends previous literature by considering influential factors related to both consumers’ and producers’ preferences in modeling technology adoption.

It has been claimed that the private finance initiative (PFI) provides value for money in the overall life of the project through the lifecycle costing (LCC) process under the umbrella of lifecycle management (LCM). The available literature points to the fact that LCC is very important in getting value for money from PFI projects. However, there is no literature available on the effect of the use of LCM in PFI projects in the UK. Therefore, the purpose of this paper is to explore the factors that influence the success of LCM in educational PFI projects.

The paper adopts a post-positivist approach to literature review. Purposive sampling is utilised with a mixed methodological approach. 6 qualitative inductive interviews offer key themes, which are further investigated using quantitative deductive questionnaires, of which 35 were issued and 26 were returned.

The paper provides empirical insights about the key success factors of LCM in the education sector. The results highlight the necessity of quality standardized data collection in a big data form. It highlights the need for a cultural shift from short- to long-term profit maximisation and service provision by the use of LCM in the PFI education sector.

A purposive sample was used to maximise the validity of data collection. Although this method has garnered concise and clear results, it is understood that this study is limited into a niche sector and a set of subsequently niche professionals. It is recommended that a larger sample be utilised and the spectrum of PFI sectors be opened up to further explore the topic.

Further investigations across different sectors of PFI project may be viewed as a good comparison, sectors such as health, accommodation and prisons. Gathering responses across all sector types could have resulted in a greater number of responses received and offer greater validity to this study.

While key success factors are clearly identified, fragmentation is seen as a barrier to the wholesale collection of such data. The responsibility, obligation to collect data for the benefit of future projects is not a priority for SMEs with little or no incentive to consider the progression of the sector. Albeit, there is evidence of one particular successful constructor/SPV, which is consolidating their business and are experiencing greater and sustained success.

This paper identifies previously unknown key influencing factors of success for educational PFI projects in relation to LCM.