Abstract Over the last years the use of big data became increasingly relevant also for macroeconomic topics and specifically the conduct and analysis of monetary policy. The aim of this paper is to provide a survey of these applications and the relevant methods. The rationale for doing so is twofold. First, there is no straightforward definition of “big data”. Since macroeconomics and monetary policy analysis has a long tradition in quite sophisticated and data-intensive empirical applications the nature of the innovation big data is indeed bringing to the field is reflected upon. Second, concerning statistical / empirical methods the analysis of big data necessitates the use of different tools relative to traditional empirical macroeconomics which are in some cases a complement to more traditional methods. Hence big data in monetary policy is not just the application of well-established methods to larger data sets.
{"title":"Big data in monetary policy analysis—a critical assessment","authors":"Alexandra Bogner, Jürgen Jerger","doi":"10.18559/ebr.2023.2.733","DOIUrl":"https://doi.org/10.18559/ebr.2023.2.733","url":null,"abstract":"Abstract Over the last years the use of big data became increasingly relevant also for macroeconomic topics and specifically the conduct and analysis of monetary policy. The aim of this paper is to provide a survey of these applications and the relevant methods. The rationale for doing so is twofold. First, there is no straightforward definition of “big data”. Since macroeconomics and monetary policy analysis has a long tradition in quite sophisticated and data-intensive empirical applications the nature of the innovation big data is indeed bringing to the field is reflected upon. Second, concerning statistical / empirical methods the analysis of big data necessitates the use of different tools relative to traditional empirical macroeconomics which are in some cases a complement to more traditional methods. Hence big data in monetary policy is not just the application of well-established methods to larger data sets.","PeriodicalId":41557,"journal":{"name":"Economics and Business Review","volume":"2005 1","pages":"27 - 40"},"PeriodicalIF":0.7,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88364857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The aim of this article is to critically review and evaluate two ESG-based investment strategies—divestment and engagement for alignment of investment portfolios with climate change mitigation goals of the United Nations. The article compares both approaches in terms of their effectiveness of decarbonization, using the case study method. First, the case on fossil fuels divestment by Harvard Management Company is analysed. The second case study discusses shareholder engagement endeavors by Engine No. 1 hedge fund and its investment in ExxonMobil. The findings indicate that divestment may have non-immediate impact on corporate behavior and carries political and legal retribution risks. Engagement, on the other hand, presents itself as a more plausible option as it takes less time to deploy and, therefore, can produce more immediate and impactful results. Nevertheless, both divestment and engagement can play mutually supportive roles in addressing climate change by the investment industry.
{"title":"Divest or engage? Effective paths to net zero from the U.S. perspective","authors":"Andrew Buks, Konrad Sobański","doi":"10.18559/ebr.2023.1.3","DOIUrl":"https://doi.org/10.18559/ebr.2023.1.3","url":null,"abstract":"Abstract The aim of this article is to critically review and evaluate two ESG-based investment strategies—divestment and engagement for alignment of investment portfolios with climate change mitigation goals of the United Nations. The article compares both approaches in terms of their effectiveness of decarbonization, using the case study method. First, the case on fossil fuels divestment by Harvard Management Company is analysed. The second case study discusses shareholder engagement endeavors by Engine No. 1 hedge fund and its investment in ExxonMobil. The findings indicate that divestment may have non-immediate impact on corporate behavior and carries political and legal retribution risks. Engagement, on the other hand, presents itself as a more plausible option as it takes less time to deploy and, therefore, can produce more immediate and impactful results. Nevertheless, both divestment and engagement can play mutually supportive roles in addressing climate change by the investment industry.","PeriodicalId":41557,"journal":{"name":"Economics and Business Review","volume":"24 1","pages":"65 - 93"},"PeriodicalIF":0.7,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74565257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The aim of the paper is to analyse the likely implications of Generative AI (GAI) on various aspects of business and the economy. Amid the rapid growth and maturing of Generative AI technologies such as Large Language Models (like ChatGPT by OpenAI) a rapid growth of both immediate and potential applications can be seen. The implications for the economy and industries of this technological shift will be discussed. The foreseeable scenarios for the level and types of adoption that GAI might achieve—from useful analytical tool, invaluable assistant to the white-collar workers of the world to being trusted with a wide array of business and life-critical decision making. Both disruptive and premium service opportunities are foreseen. For instance, general purpose models may provide quality service—such as copywriting—to overserved customers leaving human writers as the premium option. In this context, overserved customers would be those who would be satisfied with a non-human, potentially less creative content. On the other hand highly specialized models—specifically trained in a given domain and with access to proprietary knowledge can possibly provide a premium service over that provided by human experts. It is expected that some jobs will be replaced by new AI applications. However, new workplaces will emerge. Not only the obvious expert-level data scientist roles but also low grade, “model supervisors”—people training the models, assessing the quality of responses given and handling escalations. Lastly new cybercrime risks emerging from the rise of GAI are discussed.
{"title":"The rise of Generative AI and possible effects on the economy","authors":"T. Orchard, Leszek Tasiemski","doi":"10.18559/ebr.2023.2.732","DOIUrl":"https://doi.org/10.18559/ebr.2023.2.732","url":null,"abstract":"Abstract The aim of the paper is to analyse the likely implications of Generative AI (GAI) on various aspects of business and the economy. Amid the rapid growth and maturing of Generative AI technologies such as Large Language Models (like ChatGPT by OpenAI) a rapid growth of both immediate and potential applications can be seen. The implications for the economy and industries of this technological shift will be discussed. The foreseeable scenarios for the level and types of adoption that GAI might achieve—from useful analytical tool, invaluable assistant to the white-collar workers of the world to being trusted with a wide array of business and life-critical decision making. Both disruptive and premium service opportunities are foreseen. For instance, general purpose models may provide quality service—such as copywriting—to overserved customers leaving human writers as the premium option. In this context, overserved customers would be those who would be satisfied with a non-human, potentially less creative content. On the other hand highly specialized models—specifically trained in a given domain and with access to proprietary knowledge can possibly provide a premium service over that provided by human experts. It is expected that some jobs will be replaced by new AI applications. However, new workplaces will emerge. Not only the obvious expert-level data scientist roles but also low grade, “model supervisors”—people training the models, assessing the quality of responses given and handling escalations. Lastly new cybercrime risks emerging from the rise of GAI are discussed.","PeriodicalId":41557,"journal":{"name":"Economics and Business Review","volume":"68 1","pages":"9 - 26"},"PeriodicalIF":0.7,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86926805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This paper introduces and examines a novel realized volatility forecasting model that makes use of Long Short-Term Memory (LSTM) neural networks and the risk metric financial turbulence (FT). The proposed model is compared to five alternative models, of which two incorporate LSTM neural networks and the remaining three include GARCH(1,1), EGARCH(1,1), and HAR models. The results of this paper demonstrate that the proposed model yields statistically significantly more accurate and robust forecasts than all other studied models when applied to stocks with middle-to-high volatility. Yet, considering low-volatility stocks, it can only be confidently affirmed that the proposed model yields statistically significantly more robust forecasts relative to all other models considered.
{"title":"Forecasting realized volatility through financial turbulence and neural networks","authors":"Hugo Gobato Souto, A. Moradi","doi":"10.18559/ebr.2023.2.737","DOIUrl":"https://doi.org/10.18559/ebr.2023.2.737","url":null,"abstract":"Abstract This paper introduces and examines a novel realized volatility forecasting model that makes use of Long Short-Term Memory (LSTM) neural networks and the risk metric financial turbulence (FT). The proposed model is compared to five alternative models, of which two incorporate LSTM neural networks and the remaining three include GARCH(1,1), EGARCH(1,1), and HAR models. The results of this paper demonstrate that the proposed model yields statistically significantly more accurate and robust forecasts than all other studied models when applied to stocks with middle-to-high volatility. Yet, considering low-volatility stocks, it can only be confidently affirmed that the proposed model yields statistically significantly more robust forecasts relative to all other models considered.","PeriodicalId":41557,"journal":{"name":"Economics and Business Review","volume":"40 1","pages":"133 - 159"},"PeriodicalIF":0.7,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76192578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The aim of this paper is to discuss the role and impact of Generative Artificial Intelligence (AI) systems in higher education. The proliferation of AI models such as GPT-4, Open Assistant and DALL-E presents a paradigm shift in information acquisition and learning. This transformation poses substantial challenges for traditional teaching approaches and the role of educators. The paper explores the advantages and potential threats of using Generative AI in education and necessary changes in curricula. It further discusses the need to foster digital literacy and the ethical use of AI. The paper’s findings are based on a survey conducted among university students exploring their usage and perception of these AI systems. Finally, recommendations for the use of AI in higher education are offered, which emphasize the need to harness AI’s potential while mitigating its risks. This discourse aims at stimulating policy and strategy development to ensure relevant and effective education in the rapidly evolving digital landscape.
{"title":"Challenges for higher education in the era of widespread access to Generative AI","authors":"K. Walczak, W. Cellary","doi":"10.18559/ebr.2023.2.743","DOIUrl":"https://doi.org/10.18559/ebr.2023.2.743","url":null,"abstract":"Abstract The aim of this paper is to discuss the role and impact of Generative Artificial Intelligence (AI) systems in higher education. The proliferation of AI models such as GPT-4, Open Assistant and DALL-E presents a paradigm shift in information acquisition and learning. This transformation poses substantial challenges for traditional teaching approaches and the role of educators. The paper explores the advantages and potential threats of using Generative AI in education and necessary changes in curricula. It further discusses the need to foster digital literacy and the ethical use of AI. The paper’s findings are based on a survey conducted among university students exploring their usage and perception of these AI systems. Finally, recommendations for the use of AI in higher education are offered, which emphasize the need to harness AI’s potential while mitigating its risks. This discourse aims at stimulating policy and strategy development to ensure relevant and effective education in the rapidly evolving digital landscape.","PeriodicalId":41557,"journal":{"name":"Economics and Business Review","volume":"17 1","pages":"71 - 100"},"PeriodicalIF":0.7,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84261407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract For most active investors treasury bonds (govs) provide diversification and thus reduce the risk of a portfolio. These features of govs become particularly desirable in times of elevated risk which materialize in the form of the flight-to-safety (FTS) phenomenon. The FTS for govs provides a shelter during market turbulence and is exceptionally beneficial for portfolio drawdown risk reduction. However, what if the unsatisfactory expected return from treasuries discourages higher bonds allocations? This research proposes a solution to this problem with Deep Target Volatility Equity-Bond Allocation (DTVEBA) that dynamically allocate portfolios between equity and treasuries. The strategy is driven by a state-of-the-art recurrent neural network (RNN) that predicts next-day market volatility. An analysis conducted over a twelve year out-of-sample period found that with DTVEBA an investor may reduce treasury allocation by two (three) times to get the same Sharpe (Calmar) ratio and overper-forms the S&P500 index by 43% (115%).
对于大多数积极的投资者来说，国库券提供了分散投资，从而降低了投资组合的风险。在风险升高的时候，政府的这些特征变得特别可取，这种风险以“逃向安全”(FTS)现象的形式出现。为政府提供的FTS在市场动荡期间提供了一个避难所，对减少投资组合的风险特别有益。然而，如果美国国债的预期回报不令人满意，导致投资者不愿增持债券，那该怎么办?本文提出了一种深度目标波动率股票-债券配置(Deep Target Volatility equity - bond Allocation, DTVEBA)方法来解决这一问题。该策略由最先进的循环神经网络(RNN)驱动，该网络可以预测第二天的市场波动。一项为期12年的样本外分析发现，使用DTVEBA，投资者可能会将国债配置减少两(三)倍，以获得相同的夏普(卡尔马)比率，并比标准普尔500指数高出43%(115%)。
{"title":"How to fly to safety without overpaying for the ticket","authors":"Tomasz Kaczmarek, Przemysław Grobelny","doi":"10.18559/ebr.2023.2.738","DOIUrl":"https://doi.org/10.18559/ebr.2023.2.738","url":null,"abstract":"Abstract For most active investors treasury bonds (govs) provide diversification and thus reduce the risk of a portfolio. These features of govs become particularly desirable in times of elevated risk which materialize in the form of the flight-to-safety (FTS) phenomenon. The FTS for govs provides a shelter during market turbulence and is exceptionally beneficial for portfolio drawdown risk reduction. However, what if the unsatisfactory expected return from treasuries discourages higher bonds allocations? This research proposes a solution to this problem with Deep Target Volatility Equity-Bond Allocation (DTVEBA) that dynamically allocate portfolios between equity and treasuries. The strategy is driven by a state-of-the-art recurrent neural network (RNN) that predicts next-day market volatility. An analysis conducted over a twelve year out-of-sample period found that with DTVEBA an investor may reduce treasury allocation by two (three) times to get the same Sharpe (Calmar) ratio and overper-forms the S&P500 index by 43% (115%).","PeriodicalId":41557,"journal":{"name":"Economics and Business Review","volume":"431 1","pages":"160 - 183"},"PeriodicalIF":0.7,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82868917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This study investigates the role of the country- and firm-level governance practices on the relationship between excess-cash and firm value in ASEAN-5 markets. Using the Generalized Method of Moment models and a sample of 578 firms from 2010 to 2020 the study finds that excess-cash reduces firm value, indicating high agency costs and low firm value. However, excess-cash motivated by managerial ownership, founder CEO, board independence, shareholder rights and creditor rights increase firm value while excess-cash due to managerial entrenchment and CEODuality reduce firm value. In the sub-sample analyses the study finds that entrenched managers and board size play a less effective role in wasting excess-cash in low-excess-cash firms while independent directors play a higher monitoring role in high-excess-cash firms. In addition, governance at the country-level is more effective than at the firm-level in improving the value of excess-cash in large firms. The study offers unique evidence on the relationship between excess-cash and firm value by integrating corporate governance practices at the firm- and country-levels. The research aids practitioners, academics, policymakers and investors in developing the best liquidity policies to enhance business performance.
{"title":"Corporate governance, excess-cash and firm value: Evidence from ASEAN-5","authors":"Tahir Akhtar","doi":"10.18559/ebr.2022.4.3","DOIUrl":"https://doi.org/10.18559/ebr.2022.4.3","url":null,"abstract":"Abstract This study investigates the role of the country- and firm-level governance practices on the relationship between excess-cash and firm value in ASEAN-5 markets. Using the Generalized Method of Moment models and a sample of 578 firms from 2010 to 2020 the study finds that excess-cash reduces firm value, indicating high agency costs and low firm value. However, excess-cash motivated by managerial ownership, founder CEO, board independence, shareholder rights and creditor rights increase firm value while excess-cash due to managerial entrenchment and CEODuality reduce firm value. In the sub-sample analyses the study finds that entrenched managers and board size play a less effective role in wasting excess-cash in low-excess-cash firms while independent directors play a higher monitoring role in high-excess-cash firms. In addition, governance at the country-level is more effective than at the firm-level in improving the value of excess-cash in large firms. The study offers unique evidence on the relationship between excess-cash and firm value by integrating corporate governance practices at the firm- and country-levels. The research aids practitioners, academics, policymakers and investors in developing the best liquidity policies to enhance business performance.","PeriodicalId":41557,"journal":{"name":"Economics and Business Review","volume":"8 1","pages":"39 - 67"},"PeriodicalIF":0.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87323950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The aim of the article is to evaluate the market reaction to the change of listing venue of companies moving from the alternative market to the regulated market of the Warsaw Stock Exchange. To do so, we investigated 71 switches, and their effect on market returns and liquidity. While the transfer itself creates a negative market reaction, the announcement of the transfer of a company and the institutional confirmation by the supervision of the company’s readiness for this transfer resulting from the approval of the prospectus creates positive market reactions. As a result of the transfer of companies there is an improvement in the liquidity of the shares. The empirical findings of the study could assist managers and investors in understanding the impact of stock exchange migration on returns and the liquidity of shares in the shorter and longer term.
{"title":"Stock returns and liquidity after listing switch on the Warsaw Stock Exchange","authors":"Dorota Podedworna-Tarnowska, Daniel Kaszyński","doi":"10.18559/ebr.2022.4.6","DOIUrl":"https://doi.org/10.18559/ebr.2022.4.6","url":null,"abstract":"Abstract The aim of the article is to evaluate the market reaction to the change of listing venue of companies moving from the alternative market to the regulated market of the Warsaw Stock Exchange. To do so, we investigated 71 switches, and their effect on market returns and liquidity. While the transfer itself creates a negative market reaction, the announcement of the transfer of a company and the institutional confirmation by the supervision of the company’s readiness for this transfer resulting from the approval of the prospectus creates positive market reactions. As a result of the transfer of companies there is an improvement in the liquidity of the shares. The empirical findings of the study could assist managers and investors in understanding the impact of stock exchange migration on returns and the liquidity of shares in the shorter and longer term.","PeriodicalId":41557,"journal":{"name":"Economics and Business Review","volume":"192 1","pages":"111 - 135"},"PeriodicalIF":0.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79653710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. L. Angosto-Fernández, Victoria Ferrández-Serrano
Abstract The aim of the paper is to analyse the impact of the new coronavirus on financial markets. The sample comprises returns from 80 countries, across all regions and incomes for the period known as the first wave. By combining event study methodology and time series analysis of new COVID-19 cases it is found that the negative price effect is widespread but unequal across regions. It is also noted that the distribution of the impact is also uneven with a high concentration in the week after the first local case but especially in the weeks around the pandemic declaration. Finally, it has been shown at different levels how the markets most affected by the crisis are not necessarily the most sensitive to the virus.
{"title":"World capital markets facing the first wave of COVID-19: Traditional event study versus sensitivity to new cases","authors":"P. L. Angosto-Fernández, Victoria Ferrández-Serrano","doi":"10.18559/ebr.2022.4.2","DOIUrl":"https://doi.org/10.18559/ebr.2022.4.2","url":null,"abstract":"Abstract The aim of the paper is to analyse the impact of the new coronavirus on financial markets. The sample comprises returns from 80 countries, across all regions and incomes for the period known as the first wave. By combining event study methodology and time series analysis of new COVID-19 cases it is found that the negative price effect is widespread but unequal across regions. It is also noted that the distribution of the impact is also uneven with a high concentration in the week after the first local case but especially in the weeks around the pandemic declaration. Finally, it has been shown at different levels how the markets most affected by the crisis are not necessarily the most sensitive to the virus.","PeriodicalId":41557,"journal":{"name":"Economics and Business Review","volume":"17 1","pages":"5 - 38"},"PeriodicalIF":0.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79819526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Although talent is considered imperative for gaining a competitive advantage, talent management programs’ effectiveness is unknown. It is believed that consensus on a strong theoretical underpinning for identifying talent and its general definition is yet to be achieved among academia and practitioners. This lack of integration and agreement on a single definition among scholars lead to more confusion which inhibits the advancement of talent management scholarship. The notion also requires renewed attention in the post-pandemic era because everything may not go back to normal as pre-pandemic. This study addresses the gap and focuses on reviewing the existing scholarship on talent definitions and its conceptualization in one place. The study also aims to present the potential implications of talent definition on talent management practices. Among the various implications discussed, it is argued that a single approach to talent definition makes the company vulnerable as it is not using the full potential of talent management. Finally, based on this in-depth review, the study will highlight potential critical research areas towards which the scholarship of talent may be extended.
{"title":"Who is talent? Implications of talent definitions for talent management practice","authors":"A. Skuza, Habte G. Woldu, Shawn Alborz","doi":"10.18559/ebr.2022.4.7","DOIUrl":"https://doi.org/10.18559/ebr.2022.4.7","url":null,"abstract":"Abstract Although talent is considered imperative for gaining a competitive advantage, talent management programs’ effectiveness is unknown. It is believed that consensus on a strong theoretical underpinning for identifying talent and its general definition is yet to be achieved among academia and practitioners. This lack of integration and agreement on a single definition among scholars lead to more confusion which inhibits the advancement of talent management scholarship. The notion also requires renewed attention in the post-pandemic era because everything may not go back to normal as pre-pandemic. This study addresses the gap and focuses on reviewing the existing scholarship on talent definitions and its conceptualization in one place. The study also aims to present the potential implications of talent definition on talent management practices. Among the various implications discussed, it is argued that a single approach to talent definition makes the company vulnerable as it is not using the full potential of talent management. Finally, based on this in-depth review, the study will highlight potential critical research areas towards which the scholarship of talent may be extended.","PeriodicalId":41557,"journal":{"name":"Economics and Business Review","volume":"174 1","pages":"136 - 162"},"PeriodicalIF":0.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78509470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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