This special issue deals with research related to applications of and methods to support Big Data analytics in complex social information networks. The digital age and the rise of social media have sped up changes to social systems with unforeseen consequences. However, there are major challenges created.

This study attempts to explain the interplay between digital technologies and venture creation using structural topic modeling (STM), an automatic text analysis method, to detect latent topics related to digital technologies from large texts. The profile data of 133 344 U.S. companies from CrunchBase are analyzed. The data provide detailed descriptions in text about each company: what each company does. STM enables identification of topics related to digital technologies from the large company profile data. The findings identify those digital technologies that appear to have played the biggest role in the landscape of venture activities in the United States. The study also presents findings showing the temporal changes in such “digital” topics and the association of those topics with different industry sectors. The proposed analytical framework and the findings make contributions to the growing discussion about the intersection between digital technologies and new business creation.

Decision-making is a major element in running an organization. Most decisions are made under pressure of time, without the opportunity for humans to thoroughly analyze problems. Unaided decision-making is oftenad-hoc, relying upon managerial experience and judgment. Operational research models provide a more complete analysis that hopefully lead to more reliable decisions. Individual decision makers may rely on staffs to aid them. Collecting individuals into groups may result in safer decision-making, albeit at the cost of time. Computers have provided an additional level of support in the form of automation, much faster than humans, although less resilient. Artificial intelligence research continues to expand the ability of computers to perform well in decision-making environments.Table Iprovides a comparison of our view of decision-making levels.

Blockchain, as an emerging decentralized architecture and distributed computing paradigm underlying Bitcoin and other cryptocurrencies, has attracted intensive attention in both research and applications recently. Blockchain, especially powered by chain-coded smart contracts, has the full potential of revolutionizing increasingly centralized cyber-physical-social systems (CPSSs) for constructions and applications, and reshaping traditional knowledge automation workflows. The key advantage of blockchain technology lies in the fact that it can enable the establishment of secured, trusted, and decentralized autonomous ecosystems for various scenarios, especially for better usage of the legacy devices, infrastructure, and resources.

The financing of supply chains involves decisions by supply chain members as well as by lending institutions. The optimality of lending decisions in this environment depends on the loss aversion on the part of supply chain members as well as the availability of capital. The purpose of this paper is to understand the impact of capital constraint and loss aversion on operational decisions in supply chains. Traditional models have the bank external to the supply chain, with the bank's interest rate exogenous. This research concerns a capital-constrained supply chain with the manufacturer selling to a loss averse newsvendor-like retailer, and a bank financing both the manufacturer and the retailer. The existence of supply chain finance equilibrium is proven by the use of Stackelberg game analysis. The best pricing and ordering decisions of both manufacturer and retailer are determined, and results demonstrate how these key decisions are influenced by their initial capital and the bank's financial decisions. For instance, the optimal order quantity increases or decreases with initial capital, and it is interesting that bankruptcy protection encourages a cash-constrained retailer to adopt an aggressive ordering strategy. Moreover, it is shown that the retailer's loss aversion has a significant impact on the capital constraint problem. With an increase in loss aversion, the required initial working capital decreases. Loss aversion can even change the retailer's situation from one of capital constraint to one of capital sufficiency. An extension with double orders is given for comparison. Numerical examples are given to demonstrate the impact of initial capital and loss aversion on the optimal decisions and some other managerial insights are discussed.

The outputs of upstream individual processes (members) become the inputs of downstream members in supply chains. When multiple inputs and outputs are present, data envelopment analysis has been widely applied to assess efficiency. In cooperative groups, such as supply chains, a maximin decision approach can reflect not only overall system efficiency, but also efficiency of system elements. This paper discusses a maximin efficiency multistage supply chain model capable of measuring supply chain members performance as well as overall supply chain performance.

Financial disasters in private firms led to increased emphasis on various forms of risk management, to include market risk management, operational risk management, and credit risk management. Financial institutions are motivated by the need to meet increased regulatory requirements for risk measurement and capital reserves. This paper describes and demonstrates a model to support risk management of accounts receivable. We present a decision support model for a large bank enabling assessment of risk of default on the part of loan recipients. A credit scoring model is presented to assess account creditworthiness. Alternative methods of risk measurement for fault detection are compared, and a logistic regression model selected to analyze accounts receivable risk. Accuracy results of this model are presented, enabling accounts receivable managers to confidently apply statistical analysis through data mining to manage their risk.

The Internet provides the opportunity for investors to post online opinions that they share with fellow investors. Sentiment analysis of online opinion posts can facilitate both investors' investment decision making and stock companies' risk perception. This paper develops a novel sentiment ontology to conduct context-sensitive sentiment analysis of online opinion posts in stock markets. The methodology integrates popular sentiment analysis into machine learning approaches based on support vector machine and generalized autoregressive conditional heteroskedasticity modeling. A typical financial website called Sina Finance has been selected as an experimental platform where a corpus of financial review data was collected. Empirical results suggest solid correlations between stock price volatility trends and stock forum sentiment. Computational results show that the statistical machine learning approach has a higher classification accuracy than that of the semantic approach. Results also imply that investor sentiment has a particularly strong effect for value stocks relative to growth stocks.

Knowledge sharing is an important research area in knowledge management. This study broadens the perspective on knowledge sharing by investigating an individual’s behavior type as a cooperator, reciprocator, and free rider toward knowledge contribution. In this study, we view shared knowledge in a community of practice as a public good and adopt a theory of reciprocity to explain how different cooperative types affect knowledge contribution. In the perspective of shared knowledge as a public good, people may react in three ways: they share knowledge without need for reciprocity (cooperators), they feel obligated to share their knowledge (reciprocators), or they take knowledge for granted (free riders). Analytic and simulation results reveal that the fraction of cooperators is positively related to total knowledge contribution and to the reciprocity level, while the reciprocity level positively affects knowledge contribution.

Many software projects have failed because their requirements were poorly negotiated among stakeholders. The paper proposes a systematic model, called "multi-criteria preference analysis requirements negotiation (MPARN)" to assist stakeholders to evaluate, negotiate, and agree upon alternatives among stakeholders during requirements analysis using multi-criteria preference analysis techniques. The eight-step MPARN model is applied to requirements gathered for an industrial-academic repository system. An initial analysis demonstrates that multi-criteria preference analysis methodology with the WinWin model potentially increases stakeholders' levels of cooperation and trust by providing a systematic approach to the design of a better negotiation process, as well as focusing on unbiased aspects within a requirements negotiation.

Many software projects have failed because their requirements were poorly negotiated among stakeholders. Requirements negotiation is more critical than other factors such as tools, process maturity, and design methods. The WinWin negotiation model successfully supports general requirements negotiation. However, making decisions to evaluate alternatives is still an ad-hoc process. This paper presents a systematic model, called Multi-Criteria Preference Analysis Requirements Negotiation (MPARN) to assist stakeholders to evaluate, negotiate and agree upon alternatives among stakeholders using multi-criteria preference analysis techniques.