Market Setting Research Articles

Digital advancement and its effect on business model design: Qualitative-empirical insights

Disruptive technological change fosters the evolution of innovative, highly digital business models. However, few details are known of how digital advancement is reflected in business model design in detail. This paper analyses how digital advancement is reflected in the value architecture of business models. It highlights the specific relevance of distinct value elements for this innovation process, using a two-perspective multiple-case study analysis including 13 firms and 74 customers of the case firms. The data shows that digital business models are characterised by a value architecture design that allows for a high degree of customisation, a modular design of offerings, and extensive, real-time value co-creation. Furthermore, establishing digital business models is accompanied by the utilisation of stringent evaluation to ensure a continuous adaption to changing market settings. This study sheds new light on the effects of digital advancement on business model design, therefore contributing to business model and business model innovation research. It also advances customer acceptance research, as it combines both, a firm and a customer perspective.

Feasibility for rapid on-site screening of pesticide residues in fresh produce using machine learning-assisted diffuse reflectance spectroscopy

Ensuring food safety by minimizing pesticide residues in crops is crucial to safeguarding human health. However, farmers often fail to follow the minimal time interval known as the Pre-Harvest Interval (PHI) between pesticide application and crop harvest. This lapse leads to high pesticide residue levels, often exceeding the recommended maximum residue levels (MRLs). Conventional methods for detecting pesticide residues are laboratory-based and rely on complex wet chemistry procedures. Although highly accurate, these techniques are destructive, expensive, and time-consuming. In addition, they take several hours before establishing results, making them unsuitable for analyzing large volumes of perishable fresh produce, particularly in resource-constrained settings. In this study, we investigated the feasibility of utilizing machine learning-assisted diffuse reflectance spectroscopy (DRS), a direct, rapid, and cost-effective approach for conducting an on-site preliminary assessment of pesticide residue levels in fresh produce, referred to as “screening.” This method facilitates the validation of fresh produce compliance with crop-specific PHI requirements after pesticide application, indicating adherence to MRLs. We used a portable Vis/NIR spectrometer to acquire DRS measurements from tamarillo fruits that had just been sprayed with mancozeb at the cultivation site. In total, 646 measurements were collected from treated and control (untreated) fruits over five alternating days, covering the duration between pesticide application and PHI lapse. We used spectral data pre-processing techniques and principal component analysis (PCA) to differentiate the fruits as the residue levels decreased, approaching the recommended PHI and demonstrating their suitability for harvesting. Furthermore, using two principal components that explained 88% of the total variance, we developed a support vector machine (SVM) classification model. The model successfully classified both the treated and control samples, as well as the samples based on their PHI status, distinguishing between ‘before PHI’ (days 1 - 7), ‘after PHI’ (day 9), and ‘Control’ (no residues), all with 100% accuracy. Given the quick turnaround time for results, typically within five minutes, we conclude that DRS combined with machine learning holds the potential for rapid and cost-effective on-site screening of large volumes of fresh produce in a market setting. This approach allows for the rapid identification of samples that require additional investigation in a food safety laboratory, thereby improving the efficient use of laboratory resources.

Partner narcissism in a private market setting: Consequences for audit reporting decisions and audit pricing

This study investigates the association between audit engagement partners' narcissism (measured by the size of their signature) and audit reporting decisions and audit pricing in a private market setting. We analysed 133,267 (78,994) firm‐year observations from (financially distressed) Belgian firms audited by 795 individual engagement partners from 2008 to 2017. Our results suggest that narcissism is positively associated with the likelihood that audit partners issue going‐concern opinions to their financially distressed clients and with audit fees. An array of robustness checks corroborates these results. Additional results show that audit partner narcissism is positively associated with reporting conservatism. Interestingly, additional analyses also show that narcissism reverses the effect of gender on audit reporting decisions and audit pricing. Collectively, the evidence from this study suggests that partner narcissism is positively associated with conservative audit reporting decisions and audit pricing in a private market setting.

Thermoplastic polyolefin (TPO) market set to reach $7.3 bn by 2028: driving sustainability and innovation in multiple industries

Thermoplastic polyolefin (TPO) market set to reach $7.3 bn by 2028: driving sustainability and innovation in multiple industries

Professional skepticism for green reputation clients: A mixed method study of technology enabled audits

The research investigated auditor’s Professional Skepticism (PS) mindset while auditing the “integrated financial statement” of green reputation clients in a technology enabled audit environment. The study tries to understand the difference in thought and action of auditors based on perception of their client as sustainability responsible or not. Subsequently, the study offers meaningful insights about the nuances that upholds this distinction. This research comprises two studies using the mixed method procedure as per Creswell and Clark (2017). The first study is a 2 x 2 between subject experiment. The second study uses the Theories in Use (TiU) methodology by analyzing qualitative interviews of practicing auditors in an emerging market setting. The findings of study 1 (comprising the experiment) highlight that auditors are more professionally skeptical while auditing clients with a green reputation. Study 2 (utilizing qualitative interviews) points out that technology assists the PS mindset by enhancing the audit effectiveness and audit efficiency of green client’s audit. The study offers an in-depth understanding of the level of auditor’s PS mindset toward clients with a green reputation, and therefore demystifies the inherent forces at play during such a phenomenon. Although the setting of the study is an emerging market, the study offers transferable findings to improve the overall understanding of auditor’s mindset. The study has implications for multiple actors engaged in the audit process, viz., auditors, audit firms, regulator of the audit profession, audit committees, academia, and policy makers.

Exploring Volatility: Evolution, Advancements, Trends, and Applications

Volatility is a fundamental notion in financial markets, influencing investment decisions, risk management techniques, and market dynamics. This paper provides a thorough overview of the historical evolution and practical implications of volatility, focusing on important works and key advancements in the field. The overview begins with early conceptions of volatility and the necessity for measurement prompted by market collapses, then progresses to advanced quantitative models and computer tools. The study includes key innovations such as the Black-Scholes model, which revolutionized options pricing and pioneered the concept of implied volatility. The Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models introduced frameworks for modeling time-varying volatility, paving the way for greater forecasting accuracy. Advancements in computing techniques have made it easier to analyze high-frequency data and estimate realized volatility, providing timely insights into market trends. The review also investigates contemporary trends, such as the use of machine learning algorithms and the issues provided by cryptocurrency marketplaces. Furthermore, the article examines the various characteristics and metrics of volatility, emphasizing its multidimensional nature and diverse uses in risk management, portfolio optimization, derivative pricing, and market analysis. Practical examples show how investors, traders, and financial professionals may use volatility to navigate complex market settings and make sound judgments. Finally, the study highlights the enduring significance of volatility in financial markets and highlights the need for continuing research and analysis to improve our understanding of market behavior. Acknowledging the complexities of volatility prepares market participants with valuable understandings to manage risks effectively and capitalize on market opportunities, thus contributing to financial stability and optimal portfolio performance.

Factors influencing consumers’ intention to donate blood: A South African perspective

The objective of this study was to determine the factors influencing consumer intention to donate blood in an emerging market setting. A quantitative research design was followed that entailed the collection of data from 308 non-donor respondents, using a self-administered online questionnaire. The conceptual model and hypotheses were analysed statistically, using SPSS to conduct reliability analysis, correlation analysis, and multiple regression analysis. The findings revealed that awareness of consequences, ascription of responsibility, and personal norms had a positive and significant influence on consumers’ intention to donate blood. Ascription of responsibility was the largest influencer of personal norms towards blood donation.

The health consequences of returning to work after retirement: Evidence from a Japanese longitudinal survey

Prolonged and active old age provides individuals with more chances to work again after full retirement. Returning to work is an increasingly common form of the retirement process and influences the sustainability of social security systems. Previous studies show a beneficial relationship between returning to work and health; however, little is known about the causal effect of returning to work on health. This study used data from men and women aged 50 and older (11,991 individuals) in the Longitudinal Survey of Middle-aged and Older Adults, conducted annually from 2005 to 2019 in Japan. The effects of three types of labor force transitions (continued work, full retirement, and return to work) on physical and mental health were examined. To obtain the causal effects, an instrumental variable approach was used by exploiting the Japanese pension reform and labor market settings as instruments. Compared with full retirement, returning to work showed significantly worse mental health but no significant difference in physical health. The negative effect of returning to work was pronounced among men, former nonmanual workers, and low-wealth individuals. Contrary to the findings in previous studies, insignificant or detrimental effects of returning to work were found in this study. The rigorous causal analysis adds new evidence to the literature. The findings provide important implications for labor and health policy in aging societies.

SME tax avoidance in the presence of informal competition from shadow economy

AbstractUsing the data for Indian SMEs, this paper examines in an emerging market setting whether firms with exposure to informal competition are more likely to avoid taxes. We use a sample of 9281 Indian firms that responded to the World Bank Enterprise Survey and find empirical evidence that supports this conjecture. We find that firms headquartered in states/provinces with better business environment and firms with more competitive position in the market are less likely to avoid taxes in response to informal competition. This paper also shows that firms that avoid taxes are more likely to invest in innovation and have more efficient operations than the firm that does not avoid taxes.

Atypicality: Toward an Integrative Framework in Organizational and Market Settings

Atypicality: Toward an Integrative Framework in Organizational and Market Settings

Configuration optimization of product-service system design requirements based on hesitant information axiom

Product-service system (PSS) has attracted attention of manufacturers to shift from product-providing to solution-providing, which is a marketable set of products and services. The existing researches emphasize the fulfillment of individualized customer requirements through different PSS configurations. The PSS planning phase is of high importance in generating conceptual schemes, which translates customer requirements (CRs) to design requirements (DRs). In this paper, a systematic decision-making approach based on QFD is put forward aiming to configure the PSS design requirements (DRs). To address the uncertainty and hesitancy in QFD modeling, a hesitant fuzzy linguistic term sets (HFLTSs) is applied to elicit the experts’ linguistic preferences in evaluating the importance of CRs and the relationships between CRs and DRs. To dealing with the group decision-making problems concerning the HFLTSs, the min-upper operator and the max-lower operator assemble the experts’ evaluation results into a linguistic interval, and then the numerical results can be obtained by using the 2-tuple linguistic representation model and the interval preference degree computation. A non-linear 0-1 programming model is proposed to select the target DRs’ specifications for maximizing customer satisfaction under cost constraint. In order to objectively determine the satisfaction degree of each optional specification of DR, the information axiom is introduced to construct the objective function via information content computation. To deal with the qualitative DRs, HFLTSs and information axiom are combined and hesitant information axiom (HIA) is proposed. Finally, a DRs optimization model is established using HIA and the imprecision method. A case study is carried out to demonstrate the effectiveness of the optimal PSS planning approach developed.

Assessing the impact of storage obligations on the EU gas market: An uncertainty analysis

The low gas filling levels in underground gas storages (UGSs) in the European Union (EU), and the subsequent price volatility and geopolitical tensions between Russia and the EU triggered the adoption of counterbalancing policy strategies in the EU. A specific measure was the mandatory refilling of UGSs at 90 % by 1 November of each year to ensure adequate gas stocks. This paper assesses the impact of storage obligations on natural gas prices and the welfare of EU consumers for two market settings: a tight gas market, as experienced at the end of 2021, and a loose gas market. We use a gas market model with detailed technological constraints and uncertain input parameters. The uncertain parameters are sampled via the Latin Hypercube sampling method. The main sources of uncertainty are the storage settings, the scarcity induced both on the supply and the demand side, and the alternative storage targets. The results point out economic benefits mainly on EU consumers, in terms of avoided expenditures. The consumer payment savings under tight market conditions range up to 17 % on average, while the impact of the policy remains more questionable in the absence of scarcity: under loose market conditions, evidence of benefits is provided for 60 % of the simulated scenarios.

Market power in California's water market

AbstractWe estimate market power in California's surface water market. Market power may distort the potential welfare gains from water marketing. We use a Nash‐Cournot model and derive a closed‐form solution for the extent of market power in a water market setting. We then use this solution to estimate market power in a newly assembled dataset on California's water economy. We show that, under the assumptions of the Nash‐Cournot model, market power in this thin market is limited.

Determinants of Cash Distribution Options in South African Listed Firms: An Empirical Analysis of Earnings, Company Size, and Economic Value Added

The purpose of this study was to examine the determinants of cash distribution options by critically considering the effects of earnings, dividends, firm size, and economic value added. The distribution of cash dividends to shareholders serves as a basic means by which shareholders receive returns on their investments, so it is essential to examine share repurchases alongside dividends to enhance management’s efforts in maximising shareholder value. This study utilised panel data from 52 companies listed on the Johannesburg Security Exchange (JSE) that engaged in open market share repurchases for at least 2 years between 2000 and 2019. The data were extracted from the IRESS database. The panel data regression model was fitted with the ordinary least squares (OLS), difference generalised moment method (Diff-GMM), system generalised moment method (Sys-GMM), and least-squares dummy variable correction estimator (LSDVC). The findings revealed that there was a positive and significant relationship between the earnings per share and the payoff flexibility, implying that there was an inherent flexibility of repurchases as a payout option in the sampled firms. Additionally, the study revealed a significant negative relationship between the firm size, economic value added, and payoff flexibility. This suggests that larger companies tend to distribute a lower proportion of their earnings as share repurchases and opt for higher cash dividends instead. The implications of these findings provide financial managers with valuable insights into the role of share repurchases as a cash distribution choice. By recognising share repurchases as a viable option, financial managers can enhance their efforts to create and maximise shareholder value, particularly in emerging market settings. This evidence should encourage financial managers to recognise share repurchases more as a distribution choice, diffusing the tension regarding share repurchases replacing the payment of cash dividends and some doubt that they may not possess attributes complimentary to cash dividends. The study recommended relevant academic, industry, and policy implications in the South African context.

Evolutionary Game Analysis of Copyright Protection for NFT Digital Works Considering Collusive Behavior

The non-fungible tokens trading of digital content works, as an emerging business model, has rapidly developed while also posing challenges to current copyright protection. The NFT infringement incidents in recent years have exposed many issues, such as lack of government regulation, imperfect copyright protection mechanisms, and illegal profits from service platforms. Considering the collusive behavior during the NFT minting process, this study uses evolutionary game theory to model a game composed of three populations: digital content creators; NFT service platforms; and government regulatory agencies. We derived and analyzed the replication dynamics of the game to determine the evolutionary stability strategy. In addition, combined with numerical simulations, we also analyzed the impact of individual factors on the stability of system evolution. This study identifies that the incentives and fines set by the government must be above a certain threshold in order for game results to develop toward an ideal equilibrium state, and the government can try to improve the efficiency of obtaining and updating market information and set dynamic punishment and reward mechanisms based on this. This study also found that excessive rewards are not conducive to the government fulfilling its own regulatory responsibilities. In this regard, the government can use information technology to reduce the cost of regulation, thereby partially offsetting the costs brought about by incentive mechanisms. In addition, the government can also enhance the governance participation of platforms and creators to improve the robustness of digital copyright protection by strengthening media construction and cultivating public copyright awareness. This study helps to understand the complex relationship between NFT service platforms, digital content creators, and government regulatory authorities and proves the practical meaning of countermeasures and suggestions for improving government digital copyright regulations.

Dimensional reduction of solvency contagion dynamics on financial networks

Modelling systems with networks has been a powerful approach to tame the complexity of several phenomena. Unfortunately, the large number of variables to take into consideration often makes concrete problems difficult to handle. Methods of dimensional reduction are useful tools to rescale a complex network down to a low-dimensional effective system and thus to capture its global dynamical features. Here we study the application of the degree-weighted and spectral reduction methods to an important class of dynamical processes on networks: the propagation of credit shocks within an interbank network, modelled according to the DebtRank algorithm. We introduce an effective version of the dynamics, characterized by functions with continuous derivatives that can be handled by the dimensional reduction. We test the reduction methods against the full dynamical system in different interbank market settings: homogeneous and heterogeneous networks generated from state-of-the-art reconstruction methods as well as networks derived from empirical e-MID data. Our results indicate that, for proper choices of the bank default probability, reduction methods can provide reliable estimates of systemic risk in the market, with the spectral reduction better handling heterogeneous networks. Finally, we provide new insights on the nature and working principles of dimensional reduction methods.

Light robust co-optimization of energy and reserves in the day-ahead electricity market

To accommodate the stochasticity of variable renewable energy sources (VRES) while efficiently dispatching generation resources and procuring adequate reserves, previous research proposed co-optimizing energy and reserves in the day-ahead (DA) using various uncertainty-based mechanisms. However, the co-optimized markets based on these mechanisms exhibit implementation limitations related to their high computational burden, complex customized solution algorithms, and over-conservative solutions. To address these shortcomings, this paper proposes a practical light robust optimization (LR) approach for the DA co-optimization of energy and reserves. The method results in a linear market clearing mechanism that easily enables the control of the robustness level of the solution through a tunable conservativeness parameter. In addition, the paper explores three different formulations for specifying the system reserve requirements considering, namely, fixed reserve requirements (LRF1), variable reserve requirements based on system uncertainty (LRF2), and a combined approach (LRF3). The formulations integrate the uncertainty from VRES in the market setting using a new bid format called uncertainty bid. The three formulations are then compared using a case study. The numerical results show the effects of the variation of the conservativeness parameter and the reserve requirements on the total socio-economic welfare (SEW), dispatched energy quantities, anticipated activation costs, and procured reserves. Moreover, the analyses showcase that sizing reserves based on system uncertainty (in LRF2) results in a 27%–61% decrease in reserve procurement costs when compared with LRF1, while the combined approach (in LRF3) results in a better performance than LRF2 in terms of reserve activation costs, with costs 61%–263% lower than in LRF2.

Anatolian hemp (Cannabis sativa L.) cultivation, processing, and production

Cannabis sativa L. has been grown in Anatolia since ancient times and commercial cultivation persists on a limited scale today. Valued for its durable fiber and nutritious seed, hemp was an important subsistence crop in many rural households. Throughout the Ottoman Empire hemp cordage, fabrics and oakum were maritime necessities and internationally traded commodities. During the twentieth century commercial hemp fiber and seed production levels fluctuated due to regulatory and market forces, eventually leading to a steady decline commencing in the 1980s. Hemp production in Turkey only survives today in specialized market settings, although recently production has increased. Historical data largely gleaned from Turkish language sources is presented along with personal communications and field observations.

Uncertainty-Aware Trading of Congestion and Imbalance Mitigation Services for Multi-DSO Local Flexibility Markets

The design of Local Flexibility Markets (LFMs) for energy and reserve dispatch of Renewable Distributed Energy Resources (RDERs) has recently been a topic of wide research. However, in an scenario with high penetration of RDERs deployed among different Distribution System Operators (DSOs) jurisdictions, further operational requirements concerning congestion and imbalance mitigation services may lay down. In this context, the relationship between capacity and energy products and the uncertainty management scheme becomes essential for procuring of RDERs flexibility. This paper proposes an uncertainty-aware Multi-DSO LFM. This market setting uses flexibility products to mitigate congestions and imbalances among different DSOs. First, capacity products hold back the flexibility of the RDERs in anticipation of contingencies. Then, energy products are activated within each time slot if the event finally occurs. LFM is solved in a coordinated and decentralised fashion using the properties of the Alternating Direction Method of Multipliers (ADMM), preserving participants' privacy. Uncertainty of RDERs and energy events duration are modelled using chance-constraint linear optimisation. The proposed methodology has been tested in a case study based on a realistic dataset and radial distribution systems.

Global sustainable aviation fuel industry research report 2023-2033: Catalyzing change – SAF market set to disrupt the aviation industry

Global sustainable aviation fuel industry research report 2023-2033: Catalyzing change – SAF market set to disrupt the aviation industry