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The purpose of this paper is to examine the effect of trading volume and open interest on volatility of futures markets. The authors capture the size and change in speculative behaviour in futures markets by examining the role of liquidity variables (trading volume and open interest) in the behaviour of futures prices.

The sample includes daily data covering the period 1996-2014 from 36 international futures markets (including currencies, commodities, stock indices, interest rates and bonds). The authors employ a two-stage estimation methodology: first, the authors employ a E-GARCH model and consider the asymmetric response of volatility to shocks of different sign. Further, the authors consider a regression framework to examine the contemporaneous relationships between volatility, trading volume and open interest. To quantify the percentage of volatility that is caused by liquidity variables, the authors also regress the estimated volatilities on the measures of open interest and trading volume.

The authors find that: market depth has an effect on the volatility of futures markets but the direction of this effect depends on the type of contract, and there is evidence of a positive contemporaneous relationship between trading volume and futures volatility for all futures contracts. Impulse-response functions also show that trading volume has a more relevant role in explaining market volatility than open interest.

These results are recommended to financial managers and analysts dealing with futures markets.

To the best of the authors’ knowledge, no study has yet considered a complete database of futures markets to investigate the empirical relation between price changes (volatility), trading volume and open interest in futures markets.

The purpose of the paper is to study the explainability of expected and unexpected trade volume and open interest as information flow, and the asymmetric effects of unexpected shocks to the information flow on volatility in Indian commodity markets.

After having dissected into expected and unexpected components, the effects of trade volume and open interest on volatility are tested. A new interaction term is also added to measure asymmetry. Four commodities, namely, cumin, soy oil and pepper in food commodity category and guar seed in non-food commodity category are selected for the present study. These four commodities are selected based on their economic and trading importance, i.e. weight in the index and trading volume (liquidity).

It is mostly found that unexpected volatility is positively related to the volatility, and the effect of the unexpected component is more than the expected component of the trading volume. The expected open interest is negatively related to volatility while the unexpected open interest is found to be positively related in all the commodities. The effects of unexpected component are higher than the expected open interest. The effects of positive unexpected shocks to the trade volume are more than those of negative unexpected shocks. The evidence of asymmetry in unexpected shocks to open interest is inconclusive. However, the inclusion of volume of trade and open interest could not vanish away the volatility. This indicates that the trading volume and open interest are not the variable with mixed distribution. Thus, it contrasts the assumption of mixed distribution hypothesis, and they do not proxy the flow of information.

It is the unexpected information flow that matters more than the expected one. Positive unexpected shocks to trade volume are more influential than the negative shocks. However, trade volume and open interest are not good proxy of information flow in the Indian commodity markets. This study would definitely broaden the horizon of managers and policymakers to understand the volatility better.

The paper is unique in terms of understanding the effect of expected and unexpected trade volume and open interest and the asymmetric effects of unexpected shocks to volume and open interest in the Indian commodity markets.

This study aims to spot wheat data and disaggregated commitment of trader data for CME traded wheat futures to examine the effect of exogenous shocks for hedging positions of Producers and Swap Dealers on cash-futures basis and excess futures returns.

A Bayesian vector autoregression (BVAR) methodology is used to capture volatility transfer effects.

Evidence is presented that institutional short hedging positions play a major role in the pricing of asymmetric information held by Swap Dealers into the basis. The results also indicate that producer hedging contains information when conditions in the supply chain create a shift in long vs short hedging demand. Finally, the results demonstrate that that Swap Dealer short hedging has the greatest effect on risk premium size and historical volatility.

Various proxies for spot prices are used in the literature, although actual spot price data is not common. In addition, stationarity for basis and open interest data is induced using the Baxter-King filter which allows us to work with levels, rather than percentage changes, in the time series data. This provides the ability to directly observe the effect of outright open interest positions for hedgers on contemporaneous innovations in basis and in excess returns. The use of a BVAR methodology represents an improvement over other structural VAR models by capturing contemporaneous systemic effects within an endogeneity based structural framework.

The purpose of this research is to better understand the human aspects of open innovation in small- and medium-sized enterprises (SMEs) by exploring how intrinsic and extrinsic motivation influence enjoyment in helping others, knowledge sharing and knowledge hiding and consequently firms' open innovation.

We collected data with a survey among CEOs in 140 SMEs and performed confirmatory factor analysis applying structural equation modeling in IBM SPSS AMOS (v. 26).

Results reveal that intrinsic motivation is positively associated with helping behavior and knowledge sharing and negatively associated with knowledge hiding. We also confirm the positive relationship between extrinsic motivation and knowledge sharing. Moreover, we find that knowledge sharing increases and knowledge hiding decreases the firm-level open innovation. Especially in high-tech industry, knowledge sharing is a vital determinant of open innovation.

Responding to the calls for a deeper understanding of the individual-level factors that determine organization-level open innovation, in this research we focus on the human aspect of open innovation in SMEs. Open innovation is a widely recognized and implemented concept among large corporations and facilitates better understanding of new technological and market developments both within and outside of organizations. However, understanding of the microfoundations of open innovation in smaller firms is still limited, but this steam of research is growing rapidly.

This paper aims to explore the open government data initiative in the Food Protection program area within the New York State’s Department of Health to assess the impacts of opening data in terms of data quality and public value. An ecosystem lens is used to explore the dynamics of actors and their interactions, the processes involved in the program and the consequences such interplay brought forth to data quality.

The data were collected through 15 semistructured interviews with multiple stakeholders from different sectors, such as county officials, administrators and technicians, food sanitarians, data journalists and restaurant owners. At the analysis stage, the ecosystem perspective helped to capture the big picture of the open data actor interrelationships within this community regarding the food service inspections datasets.

Prior research suggests that open data initiatives enhance data quality. However, this study shows how opening data can adversely affect the quality of data. Results are explained by competing dynamics and conflicting interests among open data actors, undermining the expected public value from open data initiatives.

The findings are in contrast with the mainstream open data literature and helps open data scholars to anticipate some currently unexpected results of open data initiatives. Limitations include potential biases associated to interpretation of interview data and that the results are based on a single case study.

This study makes governments and policymakers alert about the possibility of similar open data byproducts and unwanted outcomes and helps them to design more effective open data policies, hence gaining higher economic advantage while lowering costs of open data initiatives.

Detailed open data and open data case studies through the ecosystem perspective are still scarce and can enrich discussions about open data policy design and refinement in the public sector. The data used for this research are not used in any prior papers, and to the best of the authors’ knowledge, this is the first study to identify such adverse effects of data quality that have been reported.

The purpose of this paper is to analyse the personalised and social characteristics of open knowledge management in higher education based on social tagging in the Web 2.0 environment.

Through the function of annotation in social tagging, the paper analyses its personalised characteristics of recognising the preferences of participants, and its personalised‐social characteristics of enriching content from all kinds of aspects; through the function of association of social tagging, it analyses its social characteristics of social networking, and its social‐personalised characteristics of collaborative acquisition or recommendation.

In the process of online information and open knowledge organisation and acquisition based on the annotation function of social tagging in the Web 2.0 environment, the personalised participation of individuals will lead to social results for everyone; however, in the process of online information and open knowledge creation and sharing based on the association function of social tagging, social and collaborative sharing among participants will help with personalised knowledge allocation.

In open knowledge management in higher education, the characteristics of personalisation and sociability based on social tagging will help to personalise the organisation and acquisition of knowledge, and help with social creation and sharing of knowledge.

In terms of entrepreneurship, open data benefits include economic growth, innovation, empowerment and new or improved products and services. Hackathons encourage the development of new applications using open data and the creation of startups based on these applications. Researchers focus on factors that affect nascent entrepreneurs’ decision to create a startup but researches in the field of open data hackathons have not been fully investigated yet. This paper aims to suggest a model that incorporates factors that affect the decision of establishing a startup by developers who have participated in open data hackathons.

In total, 70 papers were examined and analyzed using a three-phased literature review methodology, which was suggested by Webster and Watson (2002). These surveys investigated several factors that affect a nascent entrepreneur to create a startup.

Eventually, by identifying the motivations for developers to participate in a hackathon, and understanding the benefits of the use of open data, researchers will be able to elaborate the proposed model and evaluate if the contest has contributed to the decision of establish a startup and what factors affect the decision to establish a startup apply to open data developers, and if the participants of the contest agree with these factors.

The paper expands the scope of open data research on entrepreneurship field, stating the need for more research to be conducted regarding the open data in entrepreneurship through hackathons.

The purpose of this study is to assess the current state of research on open source software (OSS) in the library context.

This study employs a descriptive literature review. It categorizes library OSS research according to a classification scheme developed for the study, and analyzes the research based on year published, publication outlet, type of software discussed, type of article, type of library, and article topic.

The authors found significant research attention devoted to open source repository applications, online public access catalog (OPAC) software, and integrated library systems (ILS). The majority of article types employed were case studies and discussion pieces. When articles were focused on a specific library, it was dominantly an academic library. Article topics centered around OSS adoption, development, provision of foundational or introductory information, and OSS performance.

The authors are not aware of any other study that surveys and synthesizes the literature on OSS in the library context. Not only does this study provide a reference source and classification scheme for those conducting future research in this area to utilize, but it also identifies areas in this field that have received the most research attention, as well as areas that could benefit from future studies. This study will prove helpful to scholars looking for directions to take their research.

The purpose of this article is to provide a review of the most recent literature concerning document supply and related matters.

The reading of over 140 published works, including journals, monographs, reports and web sites.

Finds that usage statistics are still not giving a clear indication of the importance of document supply, but it is confirmed that the large proportion of demand comes from a small number of titles. Open access continues to be top news, in particular the mass digitisation of books. Quite a lot on document supply – Kuwait, USA, Germany, Norway, UK and South Africa.

Provides a useful source of information for librarians and others interested in document supply and related matters.

Social media has the potential to enable exchange of diverse opinions, foster dialogue on important social issues and exert positive influence on stakeholders and society. However, evidence is contradictory as to whether this is the case; it is possible that millennials' behaviors on social media are mainly driven by conservation (conformity and safety) or self-enhancement (power and achievement). In this research, the authors examine the extent to which different human values (self-transcendence, conservation, self-enhancement and openness to change) influence millennials' activities and behaviors on social media.

The authors conduct three separate surveys on Facebook, Twitter and Instagram with 491 millennials (18–34 years of age) in the USA, examining the influence of four higher-order values of the Schwartz human values model (open self-transcendence, conservation, self-enhancement and openness to change) on specific social media activities (consumption, self-focused and sharing nonpersonal content activities).

First, the authors find that for millennial users, human values significantly influence social media activities. Second, conservation values, followed by self-enhancement values, overshadow the expression of open self-transcendence values on social media. Thus, social media platforms may function more as agents of conservation and self-enhancement than agents of personal growth.

This is among the first studies to examine the influence of human values on social media and to find that human values such as conservation and self-enhancement have a strong influence on users' social media activities, while open self-transcendence values, which lead to expansion and growth, do not find genuine expression on social media.