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1 – 10 of 511This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these…
Abstract
Purpose
This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.
Design/methodology/approach
This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.
Findings
Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.
Practical implications
This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.
Social implications
Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.
Originality/value
The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.
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Nicola Castellano, Roberto Del Gobbo and Lorenzo Leto
The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on…
Abstract
Purpose
The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.
Design/methodology/approach
The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.
Findings
The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.
Practical implications
The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.
Originality/value
This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.
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Krishna Chauhan, Antti Peltokorpi, Rita Lavikka and Olli Seppänen
Prefabricated products are continually entering the building construction market; yet, the decision to use prefabricated products in a construction project is based mostly on…
Abstract
Purpose
Prefabricated products are continually entering the building construction market; yet, the decision to use prefabricated products in a construction project is based mostly on personal preferences and the evaluation of direct costs. Researchers and practitioners have debated appropriate measurement systems for evaluating the impacts of prefabricated products and for comparing them with conventional on-site construction practices. The more advanced, cost–benefit approach to evaluating prefabricated products often inspires controversy because it may generate inaccurate results when converting non-monetary effects into costs. As prefabrication may affect multiple organisations and product subsystems, the method used to decide on production methods should consider multiple direct and indirect impacts, including nonmonetary ones. Thus, this study aims to develop a multi-criteria method to evaluate both the monetary and non-monetary impacts of prefabrication solutions to facilitate decision-making on whether to use prefabricated products.
Design/methodology/approach
Drawing upon a literature review, this research suggests a multi-criteria method that combines the choosing-by-advantage approach with a cost–benefit analysis. The method was presented for validation in focus group discussions and tested in a case involving a prefabricated bathroom.
Findings
The analysis indicates that the method helps a project’s stakeholders communicate about the relative merits of prefabrication and conventional construction while facilitating the final decision of whether to use prefabrication.
Originality/value
This research contributes a method of evaluating the monetary and non-monetary impacts of prefabricated products. The research underlines the need to evaluate the diverse benefits and sacrifices that stakeholder face when considering production methods in construction.
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Jari Huikku, Elaine Harris, Moataz Elmassri and Deryl Northcott
This study aims to explore how managers exercise agency in strategic investment decisions (SIDs) by drawing on their knowledgeability of the strategic context. Specifically, the…
Abstract
Purpose
This study aims to explore how managers exercise agency in strategic investment decisions (SIDs) by drawing on their knowledgeability of the strategic context. Specifically, the authors address the role of position–practice relations and irresistible causal forces in this conduct.
Design/methodology/approach
The authors examine SID-making (SIDM) practices in four case organisations operating in highly competitive markets, conducting interviews with managers at various levels and analysing company documents. Drawing on strong structuration theory, the authors show how managerial decision makers draw upon their knowledge of organisational context when exercising agency in SIDs.
Findings
The authors provide insights into how SIDM behaviour, specifically agents’ conduct, is shaped by a combination of position–practice relations and the agents’ comprehension of their organisation’s context.
Research limitations/implications
The authors extend the SIDM literature by surfacing the issue of how actors’ conjuncturally-specific knowledge of external structures shapes the general dispositions they draw on in exercising agency in practice.
Originality/value
The authors extend the SIDM literature by surfacing the issue of how actors’ conjuncturally-specific knowledge of external structures shapes the general dispositions they draw on in exercising agency in practice. Particularly, the authors contribute to this literature by identifying irresistible causal forces and illuminating why actors might not resist in SIDM processes, despite having the potential to do so.
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Managers must make numerous strategic decisions in order to initiate and implement a business model innovation (BMI). This paper examines how managers perceive the management team…
Abstract
Purpose
Managers must make numerous strategic decisions in order to initiate and implement a business model innovation (BMI). This paper examines how managers perceive the management team interacts when making BMI decisions. The paper also investigates how group biases and board members’ risk willingness affect this process.
Design/methodology/approach
Empirical data were collected through 26 in-depth interviews with German managing directors from 13 companies in four industries (mobility, manufacturing, healthcare and energy) to explore three research questions: (1) What group effects are prevalent in BMI group decision-making? (2) What are the key characteristics of BMI group decisions? And (3) what are the potential relationships between BMI group decision-making and managers' risk willingness? A thematic analysis based on Gioia's guidelines was conducted to identify themes in the comprehensive dataset.
Findings
First, the results show four typical group biases in BMI group decisions: Groupthink, social influence, hidden profile and group polarization. Findings show that the hidden profile paradigm and groupthink theory are essential in the context of BMI decisions. Second, we developed a BMI decision matrix, including the following key characteristics of BMI group decision-making managerial cohesion, conflict readiness and information- and emotion-based decision behavior. Third, in contrast to previous literature, we found that individual risk aversion can improve the quality of BMI decisions.
Practical implications
This paper provides managers with an opportunity to become aware of group biases that may impede their strategic BMI decisions. Specifically, it points out that managers should consider the key cognitive constraints due to their interactions when making BMI decisions. This work also highlights the importance of risk-averse decision-makers on boards.
Originality/value
This qualitative study contributes to the literature on decision-making by revealing key cognitive group biases in strategic decision-making. This study also enriches the behavioral science research stream of the BMI literature by attributing a critical influence on the quality of BMI decisions to managers' group interactions. In addition, this article provides new perspectives on managers' risk aversion in strategic decision-making.
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The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that…
Abstract
Purpose
The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.
Design/methodology/approach
Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.
Findings
The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.
Research limitations/implications
The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.
Originality/value
The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.
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Walter Leal Filho, Laís Viera Trevisan, João Henrique Paulino Pires Eustachio, Izabela Simon Rampasso, Rosley Anholon, Johannes Platje, Markus Will, Federica Doni, Muhammad Mazhar, Jaluza Maria Lima Silva Borsatto and Carla Bonato Marcolin
This study aims to investigate how sustainability and ethics are being addressed both by the literature and companies. Furthermore, it seeks to identify the specific strategies…
Abstract
Purpose
This study aims to investigate how sustainability and ethics are being addressed both by the literature and companies. Furthermore, it seeks to identify the specific strategies that these companies use to foster ethical behaviour and promote sustainability in their business operations.
Design/methodology/approach
The study entails a bibliometric analysis and a set of case studies from a sample of companies working in different industry sectors. Based on these tools, it analyses whether – and how – enterprises are placing an emphasis on sustainability and ethics as part of their businesses. In addition, the selected companies' unethical practices or socially irresponsible corporate activities were investigated and presented.
Findings
The findings suggest that using an ethics perspective can be a valuable tool in improving the accuracy and correctness of business decision-making. In addition, the paper has identified the fact that sustainability standards can be used to improve customer satisfaction as many important issues are addressed. Finally, the paper highlights the importance of ethical considerations when designing and implementing sustainability standards at enterprises and the need for regulatory guidance in this regard.
Originality/value
The paper addresses the need for studies on how sustainability and ethics are being discussed by both the literature and companies. The paper presents some elements that can be used as possible corporate indicators for a wider implementation of sustainability and ethics objectives in enterprises.
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Higher educational institutions, such as universities of applied sciences, have a significant role in promoting progress towards a sustainable future as defined by the United…
Abstract
Purpose
Higher educational institutions, such as universities of applied sciences, have a significant role in promoting progress towards a sustainable future as defined by the United Nations (UN) sustainable development goals (SDGs). This paper aims to identify how the UN SDGs are featured in master’s theses set in work–life contexts.
Design/methodology/approach
Using a descriptive review and content analysis, this study identified the number of SDGs appearing in 31 master’s theses. Sustainable development (SD) and corporate social responsibility were reflected using the approaches and models in the literature. Finland’s eight objectives for committing to SD were used to examine the commitments made by the business school of the university of applied sciences to achieve Agenda 2030.
Findings
Emphasising the value of higher education for SD, this study found that SDGs three, eight and 12 appeared most frequently in the theses. Sustainable and responsible dimensions reflected several issues concerning both the worlds of business and industry among the firms and organisations investigated by the master’s degree students in the business school at the Jyväskylä University of Applied Sciences.
Practical implications
This research holds practical and pedagogical value, serving to encourage master’s and PhD students to further explore research on SDGs and to shape public policy.
Originality/value
Sustainability was looked at in a new way as investigated by the theses. Ways to integrate the SDGs into management degree programmes and conduct research in the fields of business administration, tourism and hospitality management were identified.
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The paper aims to examine the role played by property tax in influencing strategic decisions regarding marital separation and divorce in Italian municipalities.
Abstract
Purpose
The paper aims to examine the role played by property tax in influencing strategic decisions regarding marital separation and divorce in Italian municipalities.
Design/methodology/approach
The empirical analysis is conducted on a sample of 6,458 Italian municipalities by applying the ordinary least squares (OLS) and instrumental variables (IVs) approaches.
Findings
The estimation results show a small increase in marital separations and divorces as the difference between the municipal secondary and primary home tax rate increases. Specifically, an increase of 1‰ in the property tax rate differentials is accompanied by an increase of six marital separations and four divorces per 1,000 inhabitants.
Research limitations/implications
The main limitation of the analysis is that the strategic behavior of the married couple is inferred from econometric analysis with data aggregated at the municipal level. To investigate this phenomenon more precisely, it would be useful to have individual data collected by surveys on strategic divorce decisions due to property tax incentives.
Originality/value
This study contributes to the scant existing literature on the tax incentives for strategic divorce. It is the first study to empirically investigate the effects of property tax on separation and divorce decisions by investigating the Italian context. In Italy, a property tax was introduced in 1993, encouraging “false” divorces by spouses with a second home since the tax on the secondary home was set at a rate higher than that on the primary residence. Moreover, there were no tax deductions and no additional tax breaks on the secondary home, while they were established on the primary one. Higher property taxes and the absence of tax breaks on the secondary home may have encouraged a strategic behavior whereby many married couples filed for false separation and divorce in order to recover part of property tax rebates.
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Ruchi Kejriwal, Monika Garg and Gaurav Sarin
Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both…
Abstract
Purpose
Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.
Design/methodology/approach
The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.
Findings
Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.
Originality/value
This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.
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