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1 – 10 of over 1000Tanvir Alam Shahi Md. and Sarolta Somosi
The present study aims to provide a roadmap for meeting the carbon-free, green energy production target within the stipulated period while also considering climate targets through…
Abstract
Purpose
The present study aims to provide a roadmap for meeting the carbon-free, green energy production target within the stipulated period while also considering climate targets through a sustainable auctioning scheme.
Design/methodology/approach
The research outlines the opportunity to design auctions based on qualitative research, the impact of auctions on energy costs and thus the feasibility of suggested auctioning schemes based on country-specific empirical evidence and benefits.
Findings
The conclusions show that this may result in various advantages for emerging economies relating to technology-neutral site-specific auctions if designed according to state-specific socio-economic conditions.
Originality/value
The planned addition to the state-of-the-art in the renewable energy (RE) field of this paper is that it intends to bridge the gap between theory and practice. The analysis has concepts for research, practice and/or community. Thus, it can serve as a primary source of literature reference for those willing to learn more about the aspects of cost related to RE.
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As a financial policy, dividend policy significantly affects firm value. This chapter analyzes how stock prices react to dividend decisions. First, a dividend payment is an…
Abstract
As a financial policy, dividend policy significantly affects firm value. This chapter analyzes how stock prices react to dividend decisions. First, a dividend payment is an extraction of value; therefore, stock price theoretically drops by the dividend amount on the ex-dividend day. In practice, the price drop and the dividend magnitude are not equal because of tax clientele, short-term trading, and market microstructure. Investors are indifferent in trading stocks before and after stocks go ex-dividend if they obtain equal marginal benefits from the two trading times. The difference in tax rates on dividends and capital gains leads to the gap between the price drop and the dividend amount. Moreover, if transaction costs are considerable, investors have high incentives to short-sell stocks until they cannot obtain more profits. The final outcome of this short-term trading is the difference between the price drop and the dividend amount. Furthermore, market microstructure factors such as limit orders, bid-ask spread, and price discreteness also create this gap. Second, dividend announcements convey valuable information to outsiders. When firms announce increases (decreases) in dividends, their stock prices tend to increase (decrease). Third, dividend policy is negatively related to stock price volatility. This negative relationship is explained by duration effect, rate of return effect, arbitrage realization effect, and information effect. Empirical evidence for this relationship is found in many countries. Finally, dividend smoothing is also considered as a signal about firms' future earnings. Consequently, firms with stable dividends have higher market value. In other words, dividend stability has a positive effect on stock prices.
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Arthi R., Nayana J.S. and Rajarshee Mondal
The purpose of optimal protocol prediction and the benefits offered by quantum key distribution (QKD), including unbreakable security, there is a growing interest in the practical…
Abstract
Purpose
The purpose of optimal protocol prediction and the benefits offered by quantum key distribution (QKD), including unbreakable security, there is a growing interest in the practical realization of quantum communication. Realization of the optimal protocol predictor in quantum key distribution is a critical step toward commercialization of QKD.
Design/methodology/approach
The proposed work designs a machine learning model such as K-nearest neighbor algorithm, convolutional neural networks, decision tree (DT), support vector machine and random forest (RF) for optimal protocol selector for quantum key distribution network (QKDN).
Findings
Because of the effectiveness of machine learning methods in predicting effective solutions using data, these models will be the best optimal protocol selectors for achieving high efficiency for QKDN. The results show that the best machine learning method for predicting optimal protocol in QKD is the RF algorithm. It also validates the effectiveness of machine learning in optimal protocol selection.
Originality/value
The proposed work was done using algorithms like the local search algorithm or exhaustive traversal, however the major downside of using these algorithms is that it takes a very long time to revert back results, which is unacceptable for commercial systems. Hence, machine learning methods are proposed to see the effectiveness of prediction for achieving high efficiency.
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Christophe Haag and Marion Wolff
Little is known about what emotionally un(intelligent) CEOs really say to their close collaborators within the boardroom. Would the rhetoric content differ between an emotionally…
Abstract
Purpose
Little is known about what emotionally un(intelligent) CEOs really say to their close collaborators within the boardroom. Would the rhetoric content differ between an emotionally intelligent and an emotionally unintelligent CEO, especially during a crisis? This chapter aims to answer this question.
Study Design/Methodology/Approach
40 CEOs of large corporations were asked to deliver a verbal address to their board members in reaction to a vignette describing a critical situation for the company. Participants were provided with the Schutte self-report emotional intelligence (EI) test. The verbal content of CEOs' closed-door discourses was analyzed using Cognitive-Discursive Analysis (CDA) and, subsequently, Geometric Data Analysis (GDA).
Findings
The results revealed that CEOs with low EI tend to evoke unpleasant emotions, talk about competition, and often blame some – or all – of the board members for their (poor) actions in comparison to CEOs with high or medium EI. In contrast, CEOs with high EI tend to use terms in relation to decision or realization and appear to be more cooperative than those with lower EI and were also ready to make decisions on behalf of team.
Originality/Value
Previous research has mainly focused on CEOs' public speeches. But the content of CEOs' speeches within the boardroom might noticeably differ from what they would say in a public address. The results of our exploratory study can serve CEOs as a basis toward improving their closed-door rhetoric during a crisis.
Research Limitations
It would be interesting to enlarge the size of our population in order to strengthen our statistical analyses as well as explore other cultural and linguistic environments and other channels through which emotions can be expressed (e.g., human face, gesture, vocal tone).
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The welcomed introduction of Fred Moseley to a 27-page excerpt from Marx's Economic Manuscript of 1867–1868 draws attention to the influence of turnover times on the formation of…
Abstract
The welcomed introduction of Fred Moseley to a 27-page excerpt from Marx's Economic Manuscript of 1867–1868 draws attention to the influence of turnover times on the formation of prices of production. This chapter discusses the profit-adjustment decomposition outlined by Marx in these pages where he tries to distinguish the influences of turnover time and capital composition on the formation of the prices of production. It provides an alternative decomposition based on Marx's analysis in the second volume of Capital and argues that these pages do not support Moseley's claim that prices of production are intended only to describe a long-run equilibrium condition. It therefore suggests considering the profit adjustment in relation to the dynamic formation of the general rate of profit throughout the equalization process.
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Antti Ylä-Kujala, Damian Kedziora, Lasse Metso, Timo Kärri, Ari Happonen and Wojciech Piotrowicz
Robotic process automation (RPA) has recently emerged as a technology focusing on the automation of repetitive, frequent, voluminous and rule-based tasks. Despite a few practical…
Abstract
Purpose
Robotic process automation (RPA) has recently emerged as a technology focusing on the automation of repetitive, frequent, voluminous and rule-based tasks. Despite a few practical examples that document successful RPA deployments in organizations, evidence of its economic benefits has been mostly anecdotal. The purpose of this paper is to present a step-by-step method to RPA investment appraisal and a business case demonstrating how the steps can be applied to practice.
Design/methodology/approach
The methodology relies on design science research (DSR). The step-by-step method is a design artefact that builds on the mapping of processes and modelling of the associated costs. Due to the longitudinal nature of capital investments, modelling uses discounted cashflow and present value methods. Empirical grounding characteristic to DSR is achieved by field testing the artefact.
Findings
The step-by-step method is comprised of a preparatory step, three modelling steps and a concluding step. The modelling consists of compounding the interest rate, discounting the investment costs and establishing measures for comparison. These steps were applied to seven business processes to be automated by the case company, Estate Blend. The decision to deploy RPA was found to be trivial, not only based on the initial case data, but also based on multiple sensitivity analyses that showed how resistant RPA investments are to changing circumstances.
Practical implications
By following the provided step-by-step method, executives and managers can quantify the costs and benefits of RPA. The developed method enables any organization to directly compare investment alternatives against each other and against the probable status quo where many tasks in organizations are still carried out manually with little to no automation.
Originality/value
The paper addresses a growing new domain in the field of business process management by capitalizing on DSR and modelling-based approaches to RPA investment appraisal.
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Christopher Amaral, Ceren Kolsarici and Mikhail Nediak
The purpose of this study is to understand the profit implications of analytics-driven centralized discriminatory pricing at the headquarter level compared with sales force price…
Abstract
Purpose
The purpose of this study is to understand the profit implications of analytics-driven centralized discriminatory pricing at the headquarter level compared with sales force price delegation in the purchase of an aftermarket good through an indirect retail channel with symmetric information.
Design/methodology/approach
Using individual-level loan application and approval data from a North American financial institution and segment-level customer risk as the price discrimination criterion for the firm, the authors develop a three-stage model that accounts for the salesperson’s price decision within the limits of the latitude provided by the firm; the firm’s decision to approve or not approve a sales application; and the customer’s decision to accept or reject a sales offer conditional on the firm’s approval. Next, the authors compare the profitability of this sales force price delegation model to that of a segment-level centralized pricing model where agent incentives and consumer prices are simultaneously optimized using a quasi-Newton nonlinear optimization algorithm (i.e. Broyden–Fletcher–Goldfarb–Shanno algorithm).
Findings
The results suggest that implementation of analytics-driven centralized discriminatory pricing and optimal sales force incentives leads to double-digit lifts in firm profits. Moreover, the authors find that the high-risk customer segment is less price-sensitive and firms, upon leveraging this segment’s willingness to pay, not only improve their bottom-line but also allow these marginalized customers with traditionally low approval rates access to loans. This points out the important customer welfare implications of the findings.
Originality/value
Substantively, to the best of the authors’ knowledge, this paper is the first to empirically investigate the profitability of analytics-driven segment-level (i.e. discriminatory) centralized pricing compared with sales force price delegation in indirect retail channels (i.e. where agents are external to the firm and have access to competitor products), taking into account the decisions of the three key stakeholders of the process, namely, the consumer, the salesperson and the firm and simultaneously optimizing sales commission and centralized consumer price.
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Omid Soleymanzadeh and Bahman Hajipour
The purpose of this study is to address why managers enter the excessive market. A comparison of the facts and perceptions of entrants relative to success in the market shows that…
Abstract
Purpose
The purpose of this study is to address why managers enter the excessive market. A comparison of the facts and perceptions of entrants relative to success in the market shows that many entrants are confident about the viability of their businesses and enter the market. Accordingly, the authors simulate market entry decisions to detect behavioral biases.
Design/methodology/approach
The authors adapted the entry decisions simulation method, which is supported by the theoretical foundations of signal detection theory (SDT) and signaling theory. The simulation model is implemented on the Anaconda platform and written in Python 3.
Findings
The results of this study suggest that overestimation relates to excess market entry. Also, the proportion of excess entry under difficult conditions is always higher than under easy conditions.
Practical implications
This research helps managers and firms think about their and their competitors' abilities and evaluate them before entering the market. Policymakers and practitioners can also design programs such as experiential learning to help entrants assess their skills.
Originality/value
So far, no research has investigated the role of overconfidence under different market conditions. Accordingly, this study contributes to the current market entry literature by disentangling the debate between absolute and relative confidence and by considering the role of task difficulty.
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You Wu, Xiao-Liang Shen and Yongqiang Sun
Social media rumor combating is a global concern in academia and industry. Existing studies lack a clear definition and overall conceptual framework of users' rumor-combating…
Abstract
Purpose
Social media rumor combating is a global concern in academia and industry. Existing studies lack a clear definition and overall conceptual framework of users' rumor-combating behaviors. Therefore, this study attempts to empirically derive a typology of rumor-combating behaviors of social media users.
Design/methodology/approach
A three-phase typology development approach is adopted, including content analysis, multidimensional scaling (MDS), interpreting and labeling. Qualitative and quantitative data collection and analysis methods are employed.
Findings
The elicited 40 rumor-combating behaviors vary along two dimensions: high versus low difficulty of realization, and low versus high cognitive load. Based on the two dimensions, the 40 behaviors are further divided into four categories: rumor-questioning behavior, rumor-debunking behavior, proactive-appealing behavior, and literacy enhancement behavior.
Practical implications
This typology will serve as reference for social media platforms and governments to further explore the interventions to encourage social media users to counter rumor spreading based on various situations and different characteristics of rumor-combating behaviors.
Originality/value
This study provides a typology of rumor-combating behaviors from a novel perspective of user participation. The typology delves into the conceptual connotations and basic forms of rumor combating, allowing for a comprehensive understanding of the complete spectrum of users' rumor-combating behaviors. Furthermore, the typology identifies the similarities and the differences between various rumor-combating behaviors, thus providing implications and directions for future research on rumor-combating behaviors.
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Dejan Živkov, Marina Gajić-Glamočlija and Jasmina Đurašković
This paper researches a bidirectional volatility transmission effect between stocks and exchange rate markets in the six East European and Eurasian countries.
Abstract
Purpose
This paper researches a bidirectional volatility transmission effect between stocks and exchange rate markets in the six East European and Eurasian countries.
Design/methodology/approach
Research process involves creation of transitory and permanent volatilities via optimal component generalized autoregressive heteroscedasticity (CGARCH) model, while these volatilities are subsequently embedded in Markov switching model.
Findings
This study’s results indicate that bidirectional volatility transmission exists between the markets in the selected countries, whereas the effect from exchange rate to stocks is stronger than the other way around in both short-term and long-term. In particular, the authors find that long-term spillover effect from exchange rate to stocks is stronger than the short-term counterpart in all countries, which could suggest that flow-oriented model better explains the nexus between the markets than portfolio-balance approach. On the other hand, short-term volatility transfer from stock to exchange rate is stronger than its long-term equivalent.
Practical implications
This suggests that portfolio-balance theory also has a role in explaining the transmission effect from stock to exchange rate market, but a decisive fact is from which direction spillover effect is observed.
Originality/value
This paper is the first one that analyses the volatility nexus between stocks and exchange rate in short and long term in the four East European and two Eurasian countries.
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