Proprietary Information Research Articles

Corporate venture capital contributions to strategic renewal: Neglected paths and barriers

AbstractResearch SummaryVia a study of the corporate venture capital (CVC) unit of a multinational corporation, we examine how CVC contributes to strategic renewal. We find the commonly featured direct path to renewal to have limited impact as access to venture technology is constrained by key external institutional barriers. By contrast, we highlight a less studied, but potentially more important, indirect path: the unit's and hence the parent corporation's access to a high‐technology ecosystem. Obstacles on this path are internal and organizational: including challenges of integration, goal disparities, and competition for resources and credit between the CVC unit and corporate parent. The significance of the indirect path and revelation of understudied institutional and organizational barriers suggest new directions for rechanneling and contextualizing studies of CVC.Managerial SummaryWe studied the corporate venture capital (CVC) unit of a large multinational firm to discover how such investments can have strategic contributions to the parent firm. We found numerous obstacles in using technological knowledge from the ventures the unit invests in because of legal and reputation barriers against disseminating proprietary information. By contrast, opportunities abound from participating in an entrepreneurial ecosystem to which the CVC activities provide access. These expose the unit to the promises and challenges of emerging technologies and fields, providing important information the unit is free to share with the parent to guide strategic renewal. To do so, however, managers must overcome substantial organizational, political, and resource barriers between the CVC unit and the parent corporation.

A Fast Loss Model for Cascode GaN-FETs and Real-Time Degradation-Sensitive Control of Solid-State Transformers.

This paper proposes a novel, degradation-sensitive, adaptive SST controller for cascode GaN-FETs. Unlike in traditional transformers, a semiconductor switch's degradation and failure can compromise its robustness and integrity. It is vital to continuously monitor a switch's health condition to adapt it to mission-critical applications. The current state-of-the-art degradation monitoring methods for power electronics systems are computationally intensive, have limited capacity to accurately identify the severity of degradation, and can be challenging to implement in real time. These methods primarily focus on conducting accelerated life testing (ALT) of individual switches and are not typically implemented for online monitoring. The proposed controller uses accelerated life testing (ALT)-based switch degradation mapping for degradation severity assessment. This controller intelligently derates the SST to (1) ensure robust operation over the SST's lifetime and (2) achieve the optimal degradation-sensitive function. Additionally, a fast behavioral switch loss model for cascode GaN-FETs is used. This proposed fast model estimates the loss accurately without proprietary switch parasitic information. Finally, the proposed method is experimentally validated using a 5 kW cascode GaN-FET-based SST platform.

Why Muddy the Water? Short selling and the disclosure of proprietary information

The conventional wisdom of voluntary disclosure literature is that the major factor preventing firms from disclosing customer-related information is firms' concern for proprietary costs. However, non-disclosure may also happen when firms have bad news to hide and are concerned about short sellers using customer information to verify bad news about the firms. By implementing a difference-in-differences research design against the backdrop of the deregulation of short selling in China, we find that increased short-selling pressure discourages firms from disclosing the identities of major customers. The findings also reveal consistent evidence supporting the bad news hoarding hypothesis rather than the proprietary cost hypothesis. Overall, our study provides an alternative explanation for firms’ lack of disclosure of customer information.

MPharma: health-tech start-up makes medicines affordable and accessible to patients in Africa

Learning outcomes After discussing this case, students will be able to understand the challenges faced by social entrepreneurs in starting a health-tech start-up in Africa; create and evaluate lean business models of health-tech companies as a social enterprise; evaluate how health-tech start-ups were developing innovative business models and supply chain networks to make prescription drugs accessible and available in Africa; understand how inorganic growth strategies can help health-tech start-ups scale up; and evaluate what promises investors were seeing while investing in social enterprises in the health-care sector in Africa and what social wealth they were creating. Case overview/synopsis In August 2022, Gregory Rockson (Rockson), social entrepreneur and founder of for-profit health technology (health-tech) social enterprise in Ghana, mPharma, stated that he had plans to replicate the company’s business model, which provided people access to drugs and at affordable prices, to other African nations, beyond the company’s existing footprint. However, analysts pointed out that the fragmented drug supply chain and poor regulation in the health-care market across Africa could act as a challenge for mPharma to replicate its business model successfully across the African continent. People in Africa were forced to pay higher prices to buy life-saving drugs due to the continent’s fragmented drug supply chain. To add to their woes, pharmacies struggled to keep life-saving and life-sustaining medicines in stock. Often, patients traveled miles to a pharmacy only to find out that the drugs they needed were not in stock. In addition to this, the markets were flooded with counterfeit drugs. And the Covid-19 pandemic only exacerbated the situation. mPharma managed the prescription drug inventory for pharmacies and drug suppliers using its proprietary vendor management information system. By using the technology infrastructure it had built, the company connected patients, pharmacies and hospitals through a cloud-based software. The system enabled doctors to track in real-time which drugs were available and at which location, thus giving patients reliable access to medicines. Patients registering with mPharma with their prescriptions and medical history received an alert on their mobile phones notifying them where the drugs they needed were available. mPharma bought drugs from major drug manufacturers such as Novartis International AG, Pfizer Inc. (Pfizer) and Bayer AG, on behalf of the pharmacies. This enabled the pharmacies to save on the up-front costs of stocking the drugs, reduced supply constraints and ensured availability of drugs to consumers in these underserved markets. The company had a consignment model wherein member pharmacies had to pay only for what they sold. Most pharmacies forecast the number of drugs they needed and purchased them from mPharma at pre-agreed rates. The company took the inventory liability to prevent pharmacies from going out of stock. As mPharma used its purchasing power to buy drugs in large quantities from drug manufacturers and suppliers, it was able to help patients realize cost savings of 30% to 60% in the purchase of medicines. mPharma was focusing on achieving its ambitious goal of dominating the health-care market in Africa in future. However, analysts felt that the company would face challenges related to poor regulation in the health-care market, high prices of drugs and the fragmented pharmacy retail market in the continent. Complexity academic level This case is intended for use in MBA/MS level programs as part of a course on Social Entrepreneurship, Sustainability, Business Model Innovation, Disruptive Business Models, and Supply Chain Management in the Drug Industry. Supplementary materials Teaching notes are available for educators only. Subject code CSS 3: Entrepreneurship.

Blockchain for secure decentralized energy management of multi-energy system using state machine replication

Decentralized energy management can preserve the privacy of individual energy systems while mitigating computational and communication burdens. However, most decentralized energy management methods are partially decentralized and cannot ensure information exchange security. Therefore, this paper provides a secure fully decentralized energy management by using blockchain. First, a fully decentralized energy management framework using the optimality condition decomposition (OCD) is provided, in which individual energy system operators only exchange the boundary information with their peers rather than submitting proprietary information to a centralized system operator. Then, an asynchronous mechanism is proposed for updating the information exchange in OCD, enabling the proposed decentralized management to work under potential communication latency or interruption. Furthermore, the blockchain-based framework with state machine replication (SMR) based consensus algorithm is provided to safeguard the information exchange among individual energy systems in a secure and tamper-proof manner. The proposed decentralized energy management is tested on a multi-energy system with seven subsystems and a real-world multi-energy system in North China. The numerical results demonstrate the effectiveness of the proposed method in privacy protection and data security enhancement. The proposed method can prevent the cost increase caused by cheating activities, which in some subsystems can reach 17.6%. Additionally, the proposed fully decentralized method outperforms the partially decentralized method by 37.7% in reducing computation time. Also demonstrated are the computational precision, scalability and adaptability of the proposed method.1Information about the data used in the case study of this paper, including how to access them, can be found in the Cardiff University data catalogue at http://doi.org/10.17035/d.2023.0247331015.1

Peer effect of proprietary information: A cost–benefit analysis of customer information disclosure

AbstractThis study examines how industry peer firms' information disclosures affect firms' customer disclosure. Using samples of Chinese A‐share companies from 2007 to 2019, our results show the following: first, the peer firms' customer disclosures have a positive and significant effect on firms' customer disclosures. The results remain robust after the instrumental variable alleviates the endogeneity problem. Second, firms refer to peer firms' disclosure decisions only when it is difficult to weigh the disclosure benefits and costs. Third, firms tend to imitate the disclosure decisions of industry leaders. The cost–benefit hypothesis of proprietary information disclosure proposed in this study contributes to the literature.

Impact of IFRS adoption on the disclosure of management forecasts by industry concentration

ABSTRACTWe aim to investigate the impact of the adoption of the International Financial Reporting Standards (IFRS) on a firm’s strategic voluntary disclosure decisions under different product market conditions. In the corporate world, firms tactically decide the level of voluntary disclosures considering their benefits, like resolving information asymmetry in the financial market, and costs, like providing proprietary information to their rivals. The adoption of IFRS has affected the demands in the financial market for complementary information, and therefore, it would affect the incentive to withhold corporate information in consideration of proprietary costs in the product market. Using a sample of Korean firms, we find that the likelihood of withholding management forecasts in concentrated industries is weaker in the post-IFRS adoption phase than in the pre-IFRS adoption phase. This is because there are greater demands for corporate disclosures for firms in concentrated industries in the post-IFRS adoption phase than earlier. Moreover, we find that the influence of IFRS on the decision of firms in concentrated industries to disclose management forecasts is significant only for firms with higher market power and higher financial leverage.

SEC confidential treatment and regulatory filing reviews

We study the interplay between the redaction of information from regulatory filings and SEC monitoring of the redacting firms. We find that redactions are associated with more intense SEC monitoring, as evidenced by higher incidence of comment letters and a longer letter resolution process. Hand collected data indicate seller firms that redact information from their sales, licensing, and royalty contracts are more likely to receive revenue recognition-related comment letters, suggesting spurious correlation is not a likely explanation of the inference. We supplement our findings by providing evidence that redacting firms tend to manage proprietary information disclosure, withholding proprietary information that should be made public under extant rules and regulations. Our findings shed light on how various SEC monitoring processes interact and support one another, offering a novel look on the interaction between a firm’s management of proprietary information disclosure and regulatory oversight.

Viking Therapeutics accuses competitor of pilfering proprietary information

Viking Therapeutics accuses competitor of pilfering proprietary information

Sparse Modeling of Energy Storage Systems in Presence of Noise

This paper presents a reduced-order modeling technique for energy storage systems such as Lithium-ion batteries (LiBs). Data-driven models offer a solution to represent the system dynamics without requiring in-situ measurements and proprietary information needed by mechanistic models. However, such models can have poor performance in unseen scenarios as they tend to overfit the training data. Here, we present a physics-inspired data-driven model to discover the governing equations of LiBs using only the excitation inputs and measured outputs. Instead of adding generic terms to discover the model, we seek physics-inspired reduced-order nonlinear models. The method is based on sparse identification of nonlinear dynamics with control, and the sparsification was achieved using sequentially thresholded ridge regression. Further, we extended the technique to work with noisy data using unscented Kalman filters, which update the terms for an enhanced state of charge (SOC) and voltage estimation. The trade-off between model accuracy and complexity is determined using threshold and regularization parameters. We formulated the problem to treat them as hyperparameters and adjust them using a training and a validation set. The model was trained on uniformly distributed electrical current signals with maximum amplitudes of 2C charge and 4C discharge rates. We used the US-highway profile as the validation set. The model's ability to predict unseen scenarios was assessed with urban dynamometer driving schedule data, where the identified model achieved the normalized root mean square error of <1.1e-3 for SOC and Voltage predictions.

Cyber Security Maturity Assessment Framework for Technology Startups: A Systematic Literature Review

Cybersecurity has gained increasing importance among firms of different sizes and industries due to the significant rise of cyber-attacks over time. Technology startups are particularly vulnerable to cyber-attacks due to the lack of cyber security measures. This is because of limited human capital and financial resources to quantify cyber risks and allocate appropriate investments to cyber security. Technology startups are suppliers and vendors to large organisations such as MNCs, government and financial institutions. They could possibly have a network connection back to the large organisations and might even store confidential information of these large organisations such as financial records, personal data and other proprietary information. As such, with the lack of appropriate cyber security measures, technology startups may be an attack vector for malicious hackers to gain entry to the large organisations. Focusing on technology startups, this study conducted a systematic literature review on cyber security maturity assessment frameworks. This study addressed five research questions on the existing cyber security maturity assessment frameworks in various industries, the target for implementation, cyber security maturity level, shared control domains of these frameworks, and the quantification of the return of cyber security investments. Referring to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist, a detailed analysis was performed on 24 published research articles (out of 650) from reputable journals and conference proceedings from January 2011 to June 2022. The results revealed the lack of an end-to-end cyber security maturity assessment framework for technology startups. Despite the similarities in the cyber security maturity level for certain frameworks, the results revealed no singular framework that can evaluate the cyber security maturity level of technology startups. The results further revealed the lack of studies on the quantification of the return of cyber security investments in an end-to-end cyber security maturity assessment framework for technology startups. This put the startup in a vulnerable position since management is not able to obtain relevant data on the startup’s cyber maturity posture and without such information, they are not able to appropriately justify their security investments to mitigate the evolving cyber risks.

The Role of Generative AI in Managing Industry Projects: Transforming Industry 4.0 Into Industry 5.0 Driven Economy

Abstract Industrial transformation is occurring now; the world is in the era of rapid industry movement into a collaborative robot (Cobot), empowering the human workforce, improving the project management processes, and optimizing manufacturing and supply chain procedures by utilizing the supercomputer and Generative Artificial Intelligence (GenAI) capability. GenAI is advancing and gaining traction in various economic sectors and the routine operations of businesses and people. The Fourth Industry Revolution (Industry 4.0), which focused on technical innovation, efficiency, profitability, and quality (driven by information technology (IT) and automation), gradually gave way to the Fifth Industry Revolution (Industry 5.0), which built on Industry 4.0's achievements but placed a stronger emphasis on human wellbeing, and resilience environmental sustainability. Industry 5.0 carries more societal responsibility for human-centric matters while maintaining economic growth objectives, complementing the current Industry 4.0 and together creating what is called the “Techno-Social Revolution.” The role of GenAI is positive in project management and would generate tremendous benefits to project managers, especially in large data analysis, decision-making, and automation of processes, yet GenAI is probably faced with many legal arguments against its deployment. Some of these are accountability for errors and omissions, misguided actions, data privacy, and proprietary information security. However, implementing a governance policy of tracking and auditing processes on GenAI decisions would establish proper accountability and responsibility. Additionally, adopting a multi-layered strategy that involves extensive risk assessments, strong security implementation, ongoing monitoring, training, machine learning, and evaluation of GenAI outputs mitigates these issues. Close collaboration between project managers, IT, and cybersecurity in setting up best practices and rules for employing GenAI will mitigate the legal challenges. Lastly, being open and clear with stakeholders regarding the usage of GenAI and the security and privacy precautions adopted assists in reaching agreeable legal terms and conditions.

Safeguarding Proprietary Information in the Supply Chain and Relationship Specific Investments

Safeguarding Proprietary Information in the Supply Chain and Relationship Specific Investments

Finding Fortune: How Do Institutional Investors Pick Asset Managers?

Abstract We propose and test a framework of private information acquisition and decision timing for asset allocators hiring outside investment managers. Using unique data on due diligence interactions between an institutional allocator and 860 hedge fund managers, we find that the production of private information complements public information. The allocator strategically chooses how much proprietary information to collect, reducing due diligence time by 18 months and improving outcomes. Funds selected by the manager outperform those not selected by 9$\%$ over 20 months. The outperformance relates to the allocator learning about fund return-to-scale constraints and manager skill before other investors.

Federated data processing and learning for collaboration in the physical sciences

Property analysis and prediction is a challenging topic in fields such as chemistry, nanotechnology and materials science, and often suffers from lack of data. Federated learning (FL) is a machine learning (ML) framework that encourages privacy-preserving collaborations between data owners, and potentially overcomes the need to combine data that may contain proprietary information. Combining information from different data sets within the same domain can also produce ML models with more general insight and reduce the impact of the selection bias inherent in small, individual studies. In this paper we propose using horizontal FL to mitigate these data limitation issues and explore the opportunity for data-driven collaboration under these constraints. We also propose FedRed, a new dimensionality reduction method for FL, that allows faster convergence and accounts for differences between individual data sets. The FL pipeline has been tested on a collection of eight different data sets of metallic nanoparticles, and while there are expected losses compared to a combined data set that does not preserve the privacy of the collaborators, we obtained extremely good result compared to local training on individual data sets. We conclude that FL is an effective and efficient method for the physical science domain that could hugely reduce the negative effect of insufficient data.

The MetaPharmacy: What Happens When Big Pharma Goes Virtual?

The MetaPharmacy: What Happens When Big Pharma Goes Virtual?

Suspected coastal reclamation areas detection by spatiotemporal fusion of landsat TM and Sentinel-1A: A case study of Fuzhou, Fujian, China

Increasing coastal reclamation activities have been undertaken to alleviate the shortage of land resources, posing significant challenges to the coordination of coastal reclamation, economic development and environmental protection. Over the past few decades, coastal reclamation has occurred extensively in the coastal region of China, especially the east coast, while the value of coastal wetlands and ecosystems has only been well understood and appreciated in the last decade. More recently, there is an important new trend to restore parts of reclaimed land to coastal wetlands for environmental protection purposes, which requires reliable reclamation detection results as a foundation for policy. This study aimed to propose a reclamation detection framework based on the spatiotemporal fusion of Landsat TM and Sentinel-1A. To utilize the complementary information of optical and SAR images, the proposed framework models the common and proprietary information of both types of data separately in a sparse manner. Then, with more reliable and detailed information, reclamation areas can be distinguished by band math and threshold segmentation using the spatiotemporal fusion of Landsat TM and Sentinel-1A images. Finally, taking Fuzhou in eastern China as an example, we provided a detailed analysis of coastal reclamation. The results confirmed that the proposed algorithm was effective for rapidly detecting suspected locations of coastal reclamation and showed great potential for monitoring the changes occurring in the coastal zone.

The spillover effects of innovation content disclosure in MD&A

We examine the spillover effects of innovation-related content from peer firms' MD&A to firm's innovation activities. We find that firms invest more in R&D when facing the circumstances that their rivals (i.e., other firms in the same industry with it) present more about innovation in their MD&A. Outcome-based social learning and competition are two major channels through which peer firms' disclosure has impact on corporate innovation activities. Moreover, the spillover effects of the tone on corporate R&D investment are fully mediated by the innovation content. The spillover effects are more pronounced when its rivals' financial statements are more comparable, rivals exercise less real earnings management, and the firm is non-state-owned enterprise (non-SOE). A regulation change in 2012 that required firms to disclose more proprietary information in MD&As strengthens the spillover effects of MD&A disclosure. The spillover effects of innovation contents in MD&A are not driven by either the information content (whether the content is forward-looking or not) or the economic connections between a firm and its peers. Peer firms' innovation disclosure also has implication to corporate innovation disclosure, patent application, R&D efficiency and firm value. Overall, we reveal the positive externalities of corporate disclosure of innovation information.

A break-even analysis of orbital debris and space preservation through monetization

While NASA and European Space Agency (ESA) publicly recognize space debris imposes high costs on space economies, they have not published monetized space debris costs. This study monetizes the increasing costs of orbital debris and costs of space preservation through Monte Carlo simulations using a break-even analysis. Orbital debris imposes satellite fortification, avoidance, and early replacement costs on satellite owners. This paper assigns values to NASA published space debris mitigation models for an estimate of space preservation costs. Estimating space debris costs has been hampered by a lack of information largely due to commercial proprietary information. This study shows how simple-realistic assumptions can turn sparse data into the basis of a robust analysis. Further, by conducting a break-even analysis of these cost streams, this study identifies the global cost savings after the costs of space debris equals the costs of space preservation. Using alternative inputs, this study conducts sensitivity analyses to identify the effectiveness of mitigation and remediation options. At a 90% success rate of post-mission disposal (PMD) and an active removal of as few as 5 defunct spacecraft per year are the sufficient and effective space preservation measures if spacefaring communities are in compliance with them sooner rather than later. Phases and timelines for global practicing measures of mitigation and remediation are sensitive to the outcome of break-even analysis. In conclusion, spacefaring communities need to stabilize the space debris environment for cost-savings and space sustainability. A simple solution is post-mission disposal and a worldwide orbital user sinking fund per launch to finance future debris removal and hedge preservation timeline uncertainty.active debris removal missions per year that proposed by NASA ODPO would cost less than one third the costs of space debris under the status quo.

Influence of coarse aggregate size and content on the properties of recycled aggregate concrete using non-destructive testing methods

Recycled concrete aggregates (RCA) are obtained by concrete recycling processes. When adopted with specially graded aggregate, long-established inexpensive and unobtrusive non-destructive techniques have the potential to revolutionize the construction sector. The combined influences of RCA size and content are commonly proprietary information or incompletely reported in available literature by means of non-destructive tests (NDTs). Hence, this paper represents the experimental investigation of the recycled aggregate concrete (RAC) properties prepared with varying sizes and replacement levels of coarse aggregate using destructive and non-destructive testing techniques. In this work, several NDTs were performed on cylindrical samples of 100 mm dia. x 200 mm and compared to destructive testing (DT) to determine the properties of 5-12 mm and 12–20 mm coarse aggregate concrete mixes having 15%, 30%, and 45% replacement of natural coarse aggregate (NCA) by RCA. For this purpose, 0%, 15%, 30%, and 45% RAC contents were utilized with the corresponding replacement of NCA for 5–12 mm and 12–20 mm coarse aggregate to determine their effects on the slump, compressive strength, digital rebound hammer, ultrasonic pulse velocity (UPV), electrical resistivity, water permeability, and modulus of elasticity (MOE) tests. It was revealed that RAC achieved lower compressive strength, rebound number, MOE, electrical resistivity, and UPV values with the increment of RCA replacement level at 7, 28, and 56 days, especially inferior values observed for 45% RCA replacement concrete mixes. On the other hand, the concrete with 5-12 mm aggregate size showed higher surface hardness, compressive strength, water permeability, UPV, and electrical resistance than the concrete with 12-20 mm aggregate size. Furthermore, several empirical relationships have been established between DT and NDT on the concrete properties and compared with other research findings. Therefore, these relationships demonstrated that NDT could efficiently predict RAC properties by increasing the pace of testing as well as reducing the cost of determining those parameters.