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The aim of the study is to examine the effects of opportunity creation and discovery on the performance of family firms. Specifically, from the tenets of dynamic capabilities and organizational contingency perspectives, this study proposes and tests a framework of how family firms' creation and discovery behavior impact venture growth and the conditions under which such impact can vary.

The study uses moderated-hierarchical regression to analyze survey data from 156 family-owned small and medium-sized enterprises (SMEs) operating within a sub-Saharan African economy.

The findings indicate that creation behavior has a curvilinear U-shaped relationship with venture growth, while discovery behavior has a direct positive relationship with venture growth. Further analysis reveals that the curvilinearity of the U-shaped relationship between creation and venture growth will be stronger for older family firms than for younger ones.

The study findings may be limited by the cross-sectional nature of the data and the specific focus on family firms only.

The results highlight the significance of pursuing both opportunities among family firms. In fact, both creation and discovery opportunities are significant drivers of family firm growth, albeit in different capacities. Relatedly, managers of older family firms (compared to younger firms) can invest more in exploiting creative opportunities.

From these findings, governments and other stakeholders should create enabling environment and institutional frameworks conducive to exploiting opportunities by entrepreneurial firms.

The study is novel – as it provides unique findings on the performance implications of creation and discovery behavior of entrepreneurial family firms within developing economies.

Transparent and fair price discovery is essential to commodity market participants in the trade value chain for competitive benefit. The purpose of this paper is to investigate the price discovery of Indian cardamom at e-auction, spot and futures markets in addition to the existence of the day of the week effect at e-auction apart from exploring a novel price risk management framework.

This study used Johansen co-integration, vector error correction model, Granger causality and regression with dummy variables to understand a day of the week effect in high-value agri-commodity of cardamom e-auction prices. These price data were based on authenticated sources of Spices Board India and Multi Commodity Exchange of India Ltd.

The statistical results indicate price discovery exists in the e-auction market and it leads to spot and futures prices. cardamom e-auction prices are negatively related to cardamom futures and positively related to spot prices. It also finds the non-existence of the day of the week effect in the high-value cardamom e-auction system in India. The study revealed that a cardamom e-auction is more active in price discovery than a cardamom futures contract.

These results shall facilitate policymakers to explore intervention of online forward market mechanism at the national level to ensure price discovery and market efficiency. However, the study did not explore reasons for the non-equilibrium of a cardamom futures contract with spot and e-auction market.

The results of this study are useful in understanding the price discovery of cardamom e-auction and its role in the spot and futures market. Cardamom price discovery depends upon the e-auction system; any change of auction policy shall be binding on Indian cardamom prices. The introduction of an online forward market mechanism as described in the paper shall facilitate price risk management apart from improving the efficiency of price discovery.

This is the first study considering cardamom e-auction, spot and futures prices in the price discovery process in India. Statistical results of a day of the week effect clearly show no significant volatility of cardamom prices during the week. Besides, this study did not find the role of cardamom futures contracts intended to serve the economic function of price discovery and price risk management. Hence, suggests policy intervention for implementing an online Forward Market mechanism for Indian cardamom to ensure market efficiency and manage price risk.

Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth.

In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery.

The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.

To the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.

To better understand the reality of living with the diseases and conditions that its drugs and therapies are developed to treat, the Novartis leadership determined a need for more meaningful insights into patients’ lives. They sought to develop a systematic, creative methodology – informed by the psychology of insightful rather than analytical thinking – to properly integrate and deploy the research commissioned into its day-to-day business decision-making. For it is well established that better understanding of the patient reality drives both compliance and adherence “beyond the pill”. The purpose of this paper is to bring the novel methodology of creativity to a wider audience and ensure that many others – notably in patient advocacy organizations – can benefit from this approach.

A core team of Insight and Analytics and Patient Engagement leads from various therapeutic area teams worked in partnership with a psychologist and practitioner in the field of insightful thinking, to develop an effective methodology that could reliably surface and articulate genuine patient insights. This methodology – the i4i Insights Discovery™ process – was developed, piloted, refined and codified in 2020 and implemented across the company in 2021–2022. It uses a combination of convergent and divergent thinking techniques – human rather than artificial intelligence, combining diverse research outputs – to understand patients’ lives better. With enhanced understanding, the insights then shape educational and behavioral strategies to drive adherence and compliance.

At a time of tightening budgets and demands to deliver enhanced impact from research budgets, i4i Insights Discovery™ has enabled Novartis teams to turn existing research outputs into profound and useful understandings of what it means to live with specific diseases and develop evidence-based patient engagement strategies; insight-driven decision-making around the lifecycle of any compound. i4i Insights Discovery™ has been applied across Novartis’s diverse areas of expertise, from heart disease to cancer, from organ transplantation to dermatology, from food allergy to ophthalmology.

The i4i Insights Discovery™ process enables Novartis teams to gain deeper understanding of patients’ lives without the need to commission additional research; to do more with less. These insights enable cross-functional Novartis teams to develop better-informed strategies that better address the needs of patients and their care partners, of health-care professionals and health-care systems. The team creating the process is looking to make the i4i Insights Discovery™ approach a gold standard of insight discovery, both for pharma and health care and in other categories, too.

The i4i Insights Discovery™ process is a practical, novel application of well-established principles in the psychology of insightful thinking to address a clear business imperative. By repurposing and reinterpreting existing research outputs using creative verbal and visual exercises, it delivers a more human and empathetic understanding of the patient reality. It moves teams from “So what?” – this is what the data mean – to “Now what?” – this is what we should do as a result.

This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase.

In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed.

Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed.

To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.

This paper aims to further our understanding of policy–practice gaps in organizations from an organizational learning perspective. The authors conceptualize and analyze policy–practice gaps in terms of what they label the dual challenge of organizational learning, i.e. the organizational tasks of both adapting ongoing practices to prescribed policy demands and adapting the policy itself to the needs of practice. Specifically, the authors address how this dual challenge can be understood in terms of organizational learning and how an organization can be managed to successfully resolve the dual learning challenge and, thereby, bridge policy–practice gaps in organizations.

This paper draws on existing literature to explore the gap between policy and practice. Through a synthesis of theories and an illustrative practical example, this paper highlights key conceptual underpinnings.

In the analysis of the dual challenge of organizational learning, this study provides a conceptual framework that emphasizes the important role of tensions and contradictions between policy and practice and their role as drivers of organizational learning. To bridge policy–practice gaps in organizations, this paper proposes five key principles that aim to resolve the dual challenge and accommodate both deployment and discovery in organizations.

Because this is a conceptual study, empirical research is called for to explore further and test the findings and conclusions of the study. Several avenues of possible future research are proposed.

This paper primarily contributes by introducing and elaborating on a conceptual framework that offers novel perspectives on the dual challenges of facilitating both discovery and deployment processes within organizations.

This paper estimates the contribution of KOSPI200 futures to spot price discovery based on methodology of ‘information share’, which is suggested by Hasbrouck (1995). Using the intraday data covering sample period from year 1997 to 2003, I estimate information share with specification of Vector Error Correction Model. Main empirical findings are summarized as followings; First. estimate of information share is above 60 percent on average through-out the entire sample period. Second. the contribution of KOSPI200 futures to error correction increased during the recent year of sample period. showing that futures price have strong tendency to lead the spot price. Third. price discovery of KOSPI200 futures have significantly positive relationship with program trading volume and seems to increase under contango. These empirical findings explain the ‘market maturity effect’ that role of futures in spot price discovery enhances as cointegration between futures and spot prices strengthens and futures market countervails the arbitrage opportunity. In general. this paper presents that mature futures market Significantly contributes to spot market efficiency and price discovery process.

This paper examines the price dynamics in the single stocks futures and spot markets. In order to enhance the liquidity of the stock futures market, Korea Exchange introduced the liquidity provider in 2014, and exempted the securities transaction taxes on stocks sold for hedging purposes of liquidity provider from 2015. This study performed a vector error correction model (VECM) based on spot-futures market linkage to evaluate the effectiveness of the liquidity policy by examining the difference in the price discovery around the event. The main empirical analysis results are summarized as follows. First, a statistically significant sample of price discovery over the entire period was evident in the interrelationship between spot and futures. This implies that stock futures have information effect equivalent to spot price, which is different from the previous studies in which futures lead the spot price discovery significantly as in the case of KOSPI200 futures market. Second, the tendency of feedback between spot and futures is consistent in price discovery even after introduction of liquidity provider and exemption of securities transaction tax. Overall, empirical results suggest that the effectiveness of the stock futures market policy is limited during the sample period and the additional measures to enhance the long term activation are needed.

In this paper, we make a comparison of price efficiency between the new market (KOSDAQ) and the main board (KOSPI) in the Korean stock market. More specifically, we evaluate the relative price efficiency of both markets by comparing the speed, degree and accuracy in process of intraday price discovery. Each market’s speed and degree of price discovery are measured by WPC (weighted price contribution) devised by Barclay and Warner (1993) and WPCT (weighted price contribution per trade) proposed by Barclay and Hendershott (2003), respectively. Each market’s accuracy of price discovery is measured by unbiased regression coefficient used by Biais et al. (1999). We analyze 535 KOSPI stocks and 803 KOSDAQ stocks using 1-minute-interval transaction data collected from Bloomberg. The major findings of this paper are summarized as follows: Fist, the price discovery in KOSDAQ, the new market is slower than in KOSPI, the main board. Second, the morning session’s degree of price discovery per trade in KOSDAQ is smaller than KOSPI. Finally, the price discovery in KOSDAQ is more accurate than in KOSPI. Overall, our results indicate that the prices of KOSDAQ stocks are as efficient as the prices of KOSPI stocks, thought they have smaller firm size, younger ages, and greater uncertainty in cash flow and asset value than the main board stocks do.

Korea Exchange has widen daily price limits from ±15% to ±30% of previous trading day’s closing price since June 15, 2015. In this paper, we examine how the event of widening price limits affect price discovery process over the course of trading day. In order to conduct this investigation, we compare price efficiency during such price discovery before and after the event. The changes that has occurred after the event can be summarized as follows: First, an analysis on full-sample indicates that price efficiency is maintained over the course of a trading, while it is aggravated temporary in two early intervals. Second, an analysis on sub-samples sorted by market capitalization, shares outstanding, or share price indicates that temporary aggravation of price efficiency in some mid-intervals is observed for shares outstanding lower group and share price top group. Overall, our results suggest that evidence supporting information hypothesis is found for the whole process of price discovery over the course of a trading day, though evidence supporting over-reaction hypothesis is found in some intervals or some types of stocks.