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AbstractBlack box machine learning models are currently being used for high-stakes decision making in various parts of society such as healthcare and criminal justice. While tree-based ensemble methods such as random forests typically outperform deep learning models on tabular data sets, their built-in variable importance algorithms are known to be strongly biased toward high-entropy features. It was recently shown that the increasingly popular SHAP (SHapley Additive exPlanations) values suffer from a similar bias. We propose debiased or "shrunk" SHAP scores based on sample splitting which additionally enable the detection of overfitting issues at the feature level.
AbstractThis paper examines the influence of the six World Governance Indicators (WGIs), as defined by the World Bank, on the real GDP growth of five emerging markets, the BRICS (Brazil, Russia, India, China, and South Africa) countries, and three advanced economies, the United States, Germany and Japan. The analysis is based on a panel data set containing the six WGIs along with further macroeconomic variables (government debt, external debt, current account balance, trade balance, budget balance, foreign exchange rate and short-term interest rate), with annual data from 1996 to 2018. We find that regulatory quality has a positive impact on economic growth, an effect that remains stable across all robustness tests. This indicates that a sound regulatory environment stimulates economic growth. We also find a negative impact of rule of law on economic growth, but this effect is not robust. The literature, however, documents a negative effect from income to rule of law, expressing that higher income does not necessarily lead to a demand for better institutions. A principal component analysis on the WGIs shows that governance is diverse across countries, while stable over time. The first two PCs capture more than 95% of the WGIs variance and are able to cluster emerging and developed markets.
Shark incidents are rare and graphic events, and their
consequences can influence the behavior of beach users,
including bathers, to a great extent. These incidents can
be thought of as a fearsome risk that may lead decision
makers to overreact or respond with inaction. This paper
examines the reaction of recreational beach users, includ-
ing bathers, to changes in the risk of shark incidents. In
addition to valuing recreational visits to Durban Beach,
South Africa, we study the reaction of beach visitors to a
hypothetical scenario in which protective shark nets,
deployed in coastal waters to protect bathers, are to be
removed. To examine potential heterogeneity of the treat-
ment effect in a travel cost-contingent behavior model, we
develop a semiparametric multivariate Poisson lognormal
(MPLN) model to jointly analyze observed and stated visit
counts. Results show that removing protective shark nets
at Durban beach would decrease recreational visits by
more than 20%. Applying the semiparametric MPLN
model we further find that both the value of a recreational
visit and the predicted change in visitation rates vary as a
function of whether recreationists usually enter the water,
whether they have heard of previous shark incidents, and
their general risk attitude.
This paper examines empirically the importance of equity
preferences for the formation of international environmen-
tal agreements (IEA) for transboundary pollution control.
Although it has been shown theoretically that the existence
of equity preferences among countries considering an IEA
increases the chances for formation and stability of a coali-
tion, empirical assessments of such preferences have been
limited to climate change mitigation and single-country
studies. We consider the case of marine plastic pollution, of
which a large share consists of food and beverage con-
tainers, representing a transboundary pollution control
problem of increasing policy concern, with properties that
lead to distinct considerations for equity and the sharing of
abatement costs. We employ a coordinated choice experi-
ment in the United Kingdom and United States to assess
preferences for abatement-cost allocations in a marine plas-
tics IEA. Pairs of cooperating countries and the relative
allocation of abatement costs are varied experimentally.
Results show systematic aversion to both advantageous and
disadvantageous inequality with respect to abatement costs
but also that the relative strength of advantageous and dis-
advantageous inequality aversion differs across countries.
Across both countries, there is evidence that left-leaning
voters generally favor more equal international sharing of
abatement costs. Differences of these results from the case
of greenhouse gas emission reduction, and implications for
current efforts to establish a legally binding global treaty on
marine plastic pollution, are discussed.
In 2021, Portuguese politics was strongly affected by the Covid-19 pandemic. In this constraining context, the Portuguese population went twice to the polls in presidential and local elections. Moreover, the Portuguese government was the holder of the presidency of the Council of the European Union. Throughout the year, the Portuguese government negotiated with the political parties to get the 2022 budget approved. However, most opposition parties rejected the budget for different reasons. Therefore, the President of the Republic dissolved Parliament and announced early elections for 30 January 2022. Although it was a difficult year, Portugal was able to implement an aggressive vaccination programme, which led to a high vaccination coverage of 80 per cent of the population.
This paper assesses the relationship between decision util-ity and experienced utility of recreational nature visits.The former is measured as the travel cost to reach that siteas routinely used by the travel cost method (TCM), andthe latter is operationalized through visit-related subjec-tive well-being (SWB). As such, the analysis is a test ofconvergent validity by examining whetherex anteTCM-based assessment of recreational value reflecting decisionutility corresponds to statedex postSWB, reflecting expe-rienced utility. It explores to what extent utility revealedby counts of nature visits are associated with self-reported,visit-related SWB relating to that same visited site. Theanalysis uses two existing datasets providing informationon (i) 3672 recreational visits to green/blue spaces inEngland over the course of four years and (ii) 5937 recrea-tional visits to bluespace sites across 14 European coun-tries over one year. Results show a positive associationbetween travel cost and visit-related SWB while control-ling for trip frequency and a large set of covariates,suggesting convergent validity of the two utility concepts.A breakdown by travel mode suggests this relationshiponly holds for trips involving motorized transport and isnot present for habitual, chore-like walking visits to therecreational site.
The COVID-19 pandemic has wreaked havoc across supply chain (SC) operations worldwide. Specifically, decisions on the recovery planning are subject to multi-dimensional uncertainty stemming from singular and correlated disruptions in demand, supply, and production capacities. This is a new and understudied research area. In this study, we examine, SC recovery for high-demand items (e.g., hand sanitizer and face masks). We first developed a stochastic mathematical model to optimise recovery for a three-stage SC exposed to the multi-dimensional impacts of COVID-19 pandemic. This allows to generalize a novel problem setting with simultaneous demand, supply, and capacity uncertainty in a multi-stage SC recovery context. We then developed a chance-constrained programming approach and present in this article a new and enhanced multi-operator differential evolution variant-based solution approach to solve our model. With the optimisation, we sought to understand the impact of different recovery strategies on SC profitability as well as identify optimal recovery plans. Through extensive numerical experiments, we demonstrated capability towards efficiently solving both small- and large-scale SC recovery problems. We tested, evaluated, and analyzed different recovery strategies, scenarios, and problem scales to validate our approach. Ultimately, the study provides a useful tool to optimise reactive adaptation strategies related to how and when SC recovery operations should be deployed during a pandemic. This study contributes to literature through development of a unique problem setting with multi-dimensional uncertainty impacts for SC recovery, as well as an efficient solution approach for solution of both small- and large-scale SC recovery problems. Relevant decision-makers can use the findings of this research to select the most efficient SC recovery plan under pandemic conditions and to determine the timing of its deployment.
Increased electricity consumption along with the transformations of the energy systems and interruptions in energy supply can lead to a blackout, i.e., the total loss of power in an area (or a set of areas) of a longer duration. This disruption can be fatal for production, logistics, and retail operations. Depending on the scope of the affected areas and the blackout duration, supply chains (SC) can be impacted to different extent. In this study, we perform a simulation analysis using anyLogistix digital SC twin to identify potential impacts of blackouts on SCs for scenarios of different severity. Distinctively, we triangulate the design and evaluation of experiments with consideration of SC performance, resilience, and viability. The results allow for some generalizations. First, we conceptualize blackout as a special case of SC risks which is distinctively characterized by a simultaneous shutdown of several SC processes, disruption propagations (i.e., the ripple effect), and a danger of viability losses for entire ecosystems. Second, we demonstrate how simulation-based methodology can be used to examine and predict the impacts of blackouts, mitigation and recovery strategies. The major observation from the simulation experiments is that the dynamics of the power loss propagation across different regions, the blackout duration, simultaneous unavailability of supply and logistics along with the unpredictable customer behavior might become major factors that determine the blackout impact and influence selection of an appropriate recovery strategy. The outcomes of this research can be used by decision-makers to predict the operative and long-term impacts of blackouts on the SCs and viability and develop mitigation and recovery strategies. The paper is concluded by summarizing the most important insights and outlining future research agenda toward SC viability, reconfigurable SC, multi-structural SC dynamics, intertwined supply networks, and cross-structural ripple effects.
The following thesis aims to explore the particular changes that occurred in user behavior on social media (Instagram, YouTube, Facebook) during the pandemic from March 2020 to present time, how these changes relate to user engagement rate and what impact these changes have on the online radio show industry from a perspective of emerging independent radio show series “Widows Radio”. For limitation purposes of the research, purposive sampling method is used to identify particular behaviors. As a result of the research, a list of changes of user behavior are identified, specific impact of these changes on user engagement rate is shown. On the base of the conducted research, several recommendations for the online radio company “Widows Radio” regarding their social media strategy are provided.
In the contemporary era, large number of companies publish their business performance as a report on the Internet. To make them usable in larger numbers, these reports must be read and labeled. This is inefficient and expensive. This study supports this business need by automatically classifying four different categories of metadata of financial reports. For training of the used random forest classifier, an active and a passive learning strategy are contrasted. The results show a clear advantage of active learning for classifying whether a financial report contains consolidated enterprise data or not. For more complicated multi-class classifications, no advantage is shown so far under the applied active learning strategy, however, there is potential to develop an alternative more efficient strategy.
Despite backlash and public outrage in recent years, the long existing phenomenon of crunch time as a method to speed up the progress of a video game development project persists in the video game industry. Regardless of its reputation, employees of the video game industry
continue to work under the use of this industry practice. The previous research has examined the reasons for the use of crunch time, but there is much to be explored concerning the point of view of the crunching employees as individuals with agency. Examining their perception presents an opportunity to better understand crunch as a multifaceted phenomenon. This knowledge can then be used to develop alternatives to using crunch time. Therefore the objective of this thesis is to determine the positive and negative impacts of crunch as well as potential alternatives from the employees’ perspective.
This thesis’ research uses semi-structured expert interviews with six expert gameworkers. The interviews are evaluated through the method of qualitative structured content analysis. It becomes apparent how the negative and positive perceptions are closely intertwined and
depend on the differing contexts and experiences of crunch time. In the case of the interviewed experts it was found that in some circumstances, an improved motivation, mental state and team spirit was perceived. In particular when employing agile methodologies, an
increased quality and productivity became apparent. In general the ability to meet deadlines was seen as positive. Negative perceptions were more commonly observed, such as damage to the team dynamics and spirit, negative impacts on the employees’ physical and mental
health, social life, work life balance, and creative ability. Furthermore, the long term mindset shift normalizing crunch time was noticed. Other impacts include a negative influence on product quality and scheduling, as well as brain drain due to skilled gameworkers leaving the studio or industry. Suggested alternatives target the conscious adjustment of the projects’ resources, schedule, and scope, as well as increased resources and improved management thereof. Furthermore the employees see the need for clear quality standards, and for managers to be more aware of their importance as role models. Other suggestions include the use of workshops and agile methodologies.
Purpose: This study aims at investigating how nonprofit organisations can successfully and sustainably implement web analytics. It develops a framework based on existing literature as well as expert interviews and tests its applicability and practicability in a hands-on case study with the German nonprofit organisation Gemeinsam TECHO e.V..
Methodology: The research questions are explored through a qualitative approach via a literature review and semi-structured interviews. The author draws a convenience sample of seven German nonprofit organisations. The interviews are analysed through qualitative content analysis. Based on the results a first web analytics framework is developed which is then tested and reviewed in a practical application.
Findings: The outcome of the study is a framework with a general step-by-step plan for NPOs on how they can implement web analytics into their business.
Research limitations: The convenience sample as well as the focus on German nonprofit organisations are possible limitations. The framework should be further tested with nonprofit organisations in different settings and conditions: country, industry, etc. Another limitation is that during the practical application only a single pass of the framework was done. Additional testing of the framework with several iterations might uncover more suggestions for improvement.
Value: This paper seeks to propose guidelines that give valuable hints for nonprofit organisations looking to implement web analytics into their business. Some existing web analytics frameworks can be found in current scientific literature; however, little empirical evidence exists for the case of nonprofit organisations. This study provides a new viewpoint on the use of web analytics which may be of use for those working in the context of nonprofit organisations.
Keywords: web analytics, nonprofit organisations, NPOs, TECHO, marketing, data analytics, web analytics framework
The use of the Country-of-Origin (COO) effect is a relevant topic in the field of international marketing research and is gaining more attention in today’s global economy. Especially in the cosmetics industry, the origin of brands tends to be included within their marketing strategy. Therefore, this thesis examines how the COO effect is impacting the evaluation of international cosmetics brands with influential market players such as the USA, South Korea and France. Using a quantitative approach, the opinions of consumers in relation to the different COO dimensions and the importance of these, as well as possible influencing factors and the impact on behavior, was analyzed. Ultimately, significantly different ratings of each dimension and their importance were found. Similarly, the influencing factors involvement and familiarity might affect the evaluation and it is likely that the higher the COO effect evaluation, the higher is the purchase intention and the willingness to pay. The findings underline the role of the COO effect for internationally marketed cosmetics and theoretical and managerial implications can be drawn based on these.
We are living in the age of data, organizations are facing exponential growth in data. This growth
of data if not dealt with in the right approach will do more harm than good to the organization.
One of the most important aspects of data is data quality. Increased data quality ensures better
decision-making, thereby enabling companies to stay competitive in the market. Data quality
directly affects business decision-making, consequently having a direct effect on the KPIs of the
organization. So it is integral for growing organizations to gain an understanding of where they
stand with regards to their data quality to make improvements needed so data quality problems
won’t affect the organization's goals and KPIs. The topic of data quality is a topic that has been
addressed in numerous researches and from different aspects, there are many established and
interesting studies that have been done and resulted in different frameworks that are followed in
our days now and that shows how rich and important this topic is. The thesis aims to gain a more
precise understanding of how data quality can affect the ability of an organization to achieve the
set key performance indicators (KPIs). To do that this study will take a live example which is the
company of Uberall and do a case study on it, the situation of data quality of Uberall was
analyzed through interviews with different employees from different departments. This research
gives an insight into some precise data quality problems that Uberall is currently facing and
needs to address .
The aim of this paper is to understand the current state of financial development in Mexico. First, the paper introduces the Financial Development Index (FDI) which provides an overview of the situation throughout the world, and then the paper analyzes several proxies for each aspect of financial development, namely access, depth, efficiency, and stability.
This thesis proposes a framework for extracting crucial textual information from the U.S. Securities and Exchange Commission annual 10-K reports using multiple natural language processing techniques. Ongoing progress has been made on retrieving the summary or sentiment from financial text in recent years due to the large volume of data and advanced computing power. However, the combination of different techniques for generating insight from 10-K textual content has not yet been addressed in the literature. Therefore, the main study contribution is designing a framework that integrates text summarization, sentiment analysis, topic modeling, and regression to provide readers with several indicators to help them acknowledge the valuable information in 10-K reports more efficiently and effectively. The framework considers various models but focuses on transformer-based models because the literature review indicates that these models surpass the performance of others on most natural language processing tasks. As a result, the framework successfully condenses the report content by 88% into the most critical sentences with the sentiment and discussed topic. Finally, these extracted features are further used to predict future company growth that, in addition to the traditional quantitative metrics, can be a new reference for investors in making more comprehensive decisions.
This thesis focuses on the automatic classification of research methods used in scientific articles in the domain of Information Systems, and studies the effectiveness of a state-of-the-art long-document Transformer technique, Longformer, on the multi-label classification task.
In recent years, the task of automatically extracting knowledge from academic articles has become more and more popular. However, as far as the author knows, due to the limitations of the max input sequence length of Transformer models, there has not yet been a comprehensive study on the classification of research methods using Transformer models, which have made extraordinary achievements in various Natural Language Processing (NLP) fields. Therefore, this thesis establishes an artifact that uses a modified Transformer model, Lomgformer, which can proceed with long-sequence inputs, to discuss its effectiveness and possible limitations. Additionally, this thesis also discusses and evaluates the performance of other benchmark models, such as the traditional Transformer - BERT and RoBERTa, and a non-transfer-learning model based on the architecture of convolutional neural network (CNN), which has been proven to have good performance by previous researchers.
As a result, this thesis proves that the Longformer-based artifact can effectively improve the classification performance for the scientific articles, and surpasses all the other models, not only the traditional Transformer models but also the models presented in the literature from the previous researchers.
Abstract
A competitive design of a supply chain is achieved by considering all the variables that affect the entire system and their interaction in the decision-making process. In developing countries, where the supply chains are usually labour intensive and where institutional and infrastructural conditions for competitive supply chains are still setting, players should give importance to the strategy of joint forces to become stronger in national and international markets.
This research presents a framework for designing agricultural supply chains in developing countries based on simulation software and describing a collaborative model that supports joint investments, information exchange and coordination. The framework is tested using an illustrative case of a developing country agricultural supply chain. This case is analysed throughout the research to demonstrate how the proposed strategy may aid in the attempt to open possibilities for small actors to play in the competitive international marketplace.
The strategy comprises four components derived from the consulted literature and the multi- method simulation tool for supply chain optimisation anyLogistix. First, Greenfield Analysis to find the best location for processing facilities to consolidate and prepare the product for the shipment. Then, Simulation and Comparison experiments to analyse the solutions' behaviour and identify which one is the most suitable and advantageous in this partnership, aiming for the best profit and adequate lead times. Finally, a Sensitivity analysis to evaluate how changes in the main variables affect the selected configuration's performance and make informed decisions knowing the associated variability of the outcomes.
Keywords: Supply Chain; Agricultural Supply Chain; Supply Chain design; Collaboration in Supply Chains; Agricultural Supply Chain in developing countries.
The study of disruptions management and resilience connected to the current COVID-19
outbreak has become an essential field in supply chain management. Post-disruption recovery
analysis amid the pandemic outbreak is relevant for organizations to respond to disruptions and
create new growth opportunities. Therefore, the performance impact of COVID-19 in the
German food retail supply chain is investigated using a hybrid case-study and discrete-event
simulation model implemented in anyLogistix software. Potential improvements and supply
chain actions are provided in response to the identified challenges. Moreover, a framework with
strong recommendations for stabilization and recovery in a post-disruption environment is
created as a guideline for the main supply chain actors in the food retail business.
Lead calling is one of the foundational promotional activities that bring business the first human-to-human interaction with potential customers. A successful call can build the root of trust between the business and potential customers, which ultimately converts the call into sales. However, conducting lead calls faces big challenges as customers nowadays are being bombarded by sales calls due to the unsustainable calling strategy of many businesses. To conduct lead calls in a sustainable and effective way, this thesis builds a first touchpoint scheduling system based on the lead call history of the InsurTech company wefox Group. By applying a set of machine learning techniques, the system can generate a list of leads with the highest reaching probabilities for the period requested by call agents. Thus, leads are called at the timing that is convenient to answer, meanwhile, call agents can work more productively with a higher reach rate.
Exploratory data analysis is conducted to understand the relationship between the reach status and the customer data. 14 classifiers are applied to predict the reaching probability. Brier Skill Score, Average Precision Score, Precision-Recall Curve, Precision, Recall, fit time and a customized evaluation metric are selected to evaluate the classifiers’ performance. Four resampling methods, class weight adjustment and a meta-model to adjust the class threshold are used to deal with class imbalance. Bayesian Optimization is used for hyperparameter tuning. The combination of mutual information classifier and correlation feature selection approaches helps reduce the number of features needed.
The experiments show that LightGBM classifier outperforms the other classifiers with Brier skill score of 0.181, average precision of 0.487 and recall of 0.642. Feature importance indicates that besides the customer interaction record with business, macro factors such as unemployment rate, social assistance rate are also powerful features. Business value evaluation indicates that the system can improve the time effectiveness of call agents by 158%. 72% of records have no overlapping between actual non-reachable hours and predicted reachable hours.
Online Content Marketing Strategy and Execution for SMEs – a Case Study of Umwelt Taxi Bremen
(2021)
Abstract: Consulting research aims at mapping scientific and practical perspectives against each other. Therefore, this Thesis is considering theoretical knowledge as well as practical insights which were empirically conducted. The author aims at creating a first snapshot on the influence of the Corona pandemic on the business model of the German consulting industry. Based on guideline-based, semi-structured interviews, the research gap on this recent topic was closed. Also, the author provides practicians with implications.
Summary: Contact bans imposed by the Corona pandemic led to a reduced amount of business travels and an increase in remote consulting. Digitalisation within the consulting industry was already on the horizon but pushed. Acceptance of clients for remote consulting increased. It became apparent that many activities do not have to be carried out at the client site. Video conferencing and online communication tools made remote project execution possible.
However, not all consulting fields seem to be suitable for remote execution. These include mainly soft and conflict-ridden topics as well as group workshops. Although, different online formats have been tried, innovative formats overcoming this obstacle are still missing. Nevertheless, it is assumed that relevance of remote consulting will remain high and that a hybrid consulting world will emerge.
The long-time successful consulting industry is suffering from economic effects of the Corona pandemic. Some projects have been cancelled or down streamed. Remote networking and acquisition of new projects has become a challenge. Furthermore, clients are becoming more price sensitive. However, this does not seem to affect the entire German consulting industry or all client segments. In the long run, demand for consulting is also not expected to decrease.
Some long-term effects are still unclear. These include possible changes in the cost structure, impact of remote consulting on the work-life balance, and effort required to create remote consulting. Changes to the business model so far mainly concern the shift of key activities to the online world and the increased use of new channels. There seems to be a strong hope for a quick return to business as usual. However, there are signals indicating that further business model innovation may be necessary.
Keywords: Strategic Management, Business Model, Innovation, Consulting Industry, Corona Pandemic
Sraffian supermultiplier models, as well as Kaleckian distribution and growth models that make use of non-capacity creating autonomous demand growth to cope with Harrodian instability, have paid little attention to the financial side of autonomous demand growth as the driver of the system. Therefore, we link the issue of Harrodian instability in Kaleckian models driven by non-capacity creating autonomous demand growth with the associated financial dynamics. For a simple model with autonomous government expenditure growth, zero interest rates and no consumption out of wealth, we find that adding debt dynamics does not change the results obtained by Skott (2017) based on Lavoie's (2016) model without debt, each published in this journal. Hence, in this simple model, the long-run equilibrium is stable if Harrodian instability is not too strong and the autonomous growth rate does not exceed a maximum given by the long-run equilibrium saving rate. Introducing interest payments on government debt as well as consumption out of wealth into the model, however, changes the stability requirements: First, the autonomous growth rate of government expenditures should not fall short of the exogenous monetary interest rate. Second, this growth rate should not exceed a maximum given by the saving rate in the long-run equilibrium net of the propensity to consume out of wealth. Third, Harrodian instability may be almost as strong as in the simple model without violating long-run overall stability, particularly if the propensity to consume out of wealth is low. We claim that irrespective of the relevance or irrelevance of Harrodian instability, it is necessary to introduce financial variables into models driven by non-capacity creating autonomous demand in order to assess the long-run (in-)stability and sustainability of growth.
The dominant literature on the development of the EU's new economic governance regime suggests that it constitutes another step towards integration in the European fiscal policy framework. However, I argue that this limited view neglects the politics of labour that underlies European monetary integration. In the euro area competitiveness adjustment is promoted, which means in practice fostering and facilitating the confrontation of workers by employers in order to keep unit labour costs down. The new economic governance reforms consistently reinforced this policy-making logic. Its central innovation was the systematization and 'hardening' of the macroeconomic surveillance framework beyond fiscal policy, which created new competences at the EU level to intervene in national labour market policies, including wages. This is what is actually new about the new economic governance; marking a recent key moment in European monetary integration and reinforcing the politics of labour underlying it.
Scholars have suggested that design thinking and effectuation theory may enrich each other. However, to date, we lack deeper theorizing and empirical evidence to further advance this valuable discourse for the benefit of innovation management. Our qualitative study draws on 41 in-depth interviews with Australian designer-founders, with the aim to provide a theoretical perspective on and empirical insights into the relationship between the behavioral practices of design thinking and the cognitive principles of effectuation. The contributions are twofold. First, our study explains how design thinking practices enable designer-founders to enact the cognitive principles of effectuation. Uncovering these “entrepreneurial ways of designing” provides an explanation for the effectiveness of design thinking for entrepreneurial innovation and new venture creation. Second, our study sheds light on the ways in which designer-founders interpret effectuation principles through the professional values and norms embodied in design thinking. These “designerly ways of entrepreneuring” resemble particular, normative interpretations of effectual action. By doing so, our study offers empirical substantiation and theoretical elaboration of the ways in which design thinking functions as an approach for entrepreneurial innovation and new venture creation. Through shedding light on the “entrepreneurial ways of designing” and “designerly ways of entrepreneuring” exhibited by designer-founders, our research reveals the reciprocal relationship between design thinking and effectuation theory.
We contribute to the recent debates on demand and growth regimes in modern finance-dominated capitalism linking them to the post-Keynesian research on macroeconomic policy regimes. We examine the demand and growth regimes, as well as the macroeconomic policy regimes for the big four Eurozone countries, France, Germany, Italy and Spain, for the periods 2001–2009 and 2010–2019. First, our approach supports the usefulness of the identification of demand and growth regimes according to growth contributions of the main demand components and financial balances of the macroeconomic sectors. This allows for an understanding of the demand sources of growth, or stagnation, if there is a lack of demand, of how these sources are financed and of potential financial instabilities and fragilities. Second, when it comes to the macroeconomic policy drivers of demand and growth regimes, as well as their respective changes, we show that the exclusive focus on fiscal policies, as in the previous literature, is too limited and that it is the macroeconomic policy regime which matters here, i.e. the combination of monetary, fiscal and wage policies, as well as the open economy conditions.
Businesses and governments are becoming increasingly concerned about the resilience of supply chains and calling for their review and stress testing. In this conceptual essay, we theorize a human-centred ecosystem viability perspective that spans the dimensions of resilience and sustainability and can be used as guidance for the conceptualization of supply chain resilience analysis in the presence of long-term crises. Subsequently, we turn to the technological level and present the digital supply chain twin as a contemporary instrument for stress testing supply chain resilience. We provide some implementation guidelines and emphasize that although resilience assessment of individual supply chains is important and critical for firms, viability analysis of intertwined supply networks and ecosystems represents a novel and impactful research perspective. One of the major outcomes of this essay is the conceptualization of a human-centred ecosystem viability perspective on supply chain resilience.
Exiting the COVID-19 pandemic: after-shock risks and avoidance of disruption tails in supply chains
(2021)
Entering the COVID-19 pandemic wreaked havoc on supply chains. Reacting to the pandemic and adaptation in the “new normal” have been challenging tasks. Exiting the pandemic can lead to some after-shock effects such as “disruption tails.” While the research community has undertaken considerable efforts to predict the pandemic’s impacts and examine supply chain adaptive behaviors during the pandemic, little is known about supply chain management in the course of pandemic elimination and post-disruption recovery. If capacity and inventory management are unaware of the after-shock risks, this can result in highly destabilized production–inventory dynamics and decreased performance in the post-disruption period causing product deficits in the markets and high inventory costs in the supply chains. In this paper, we use a discrete-event simulation model to investigate some exit strategies for a supply chain in the context of the COVID-19 pandemic. Our model can inform managers about the existence and risk of disruption tails in their supply chains and guide the selection of post-pandemic recovery strategies. Our results show that supply chains with postponed demand and shutdown capacity during the COVID-19 pandemic are particularly prone to disruption tails. We then developed and examined two strategies to avoid these disruption tails. First, we observed a conjunction of recovery and supply chain coordination which mitigates the impact of disruption tails by demand smoothing over time in the post-disruption period. Second, we found a gradual capacity ramp-up prior to expected peaks of postponed demand to be an effective strategy for disruption tail control.
Recently, a number of structured funds have emerged as public-private partnerships with the intent of promoting investment in renewable energy in emerging markets. These funds seek to attract institutional investors by tranching the asset pool and issuing senior notes with a high credit quality. Financing of renewable energy (RE) projects is achieved via two channels: small RE projects are financed indirectly through local banks that draw loans from the fund’s assets, whereas large RE projects are directly financed from the fund. In a bottom-up Gaussian copula framework, we examine the diversification properties and RE exposure of the senior tranche. To this end, we introduce the LH++ model, which combines a homogeneous infinitely granular loan portfolio with a finite number of large loans. Using expected tranche percentage notional (which takes a similar role as the default probability of a loan), tranche prices and tranche sensitivities in RE loans, we analyse the risk profile of the senior tranche. We show how the mix of indirect and direct RE investments in the asset pool affects the sensitivity of the senior tranche to RE investments and how to balance a desired sensitivity with a target credit quality and target tranche size.
Adoption of carbon regulation mechanisms facilitates an evolution toward green and sustainable supply chains followed by an increased complexity. Through the development and usage of a multi-choice goal programming model solved by an improved algorithm, this article investigates sustainability strategies for carbon regulations mechanisms. We first propose a sustainable logistics model that considers assorted vehicle types and gas emissions involved with product transportation. We then construct a bi-objective model that minimizes total cost as the first objective function and follows environmental considerations in the second one. With our novel robust-heuristic optimization approach, we seek to support the decision-makers in comparison and selection of carbon emission policies in supply chains in complex settings with assorted vehicle types, demand and economic uncertainty. We deploy our model in a case-study to evaluate and analyse two carbon reduction policies, i.e., carbon-tax and cap-and-trade policies. The results demonstrate that our robust-heuristic methodology can efficiently deal with demand and economic uncertainty, especially in large-scale problems. Our findings suggest that governmental incentives for a cap-and-trade policy would be more effective for supply chains in lowering pollution by investing in cleaner technologies and adopting greener practices.
Supply chain viability (SCV) is an emerging concept of growing importance in operations management. This paper aims to conceptualize, develop, and validate a measurement scale for SCV. SCV is first defined and operationalized as a construct, followed by content validation and item measure development. Data have been collected through three independent samplings comprising a total of 558 respondents. Both exploratory and confirmatory factor analyses are used in a step-wise manner for scale development. Reliability and validity are evaluated. A nomological model is theorized and tested to evaluate nomological validity. For the first time, our study frames SCV as a novel and distinct construct. The findings show that SCV is a hierarchical and multidimensional construct, reflected in organizational structures, organizational resources, dynamic design capabilities, and operational aspects.
The findings reveal that a central characteristic of SCV is the dynamic reconfiguration of SC structures in an adaptive manner to ensure survival in the long-term perspective. This research conceptualizes and provides specific, validated dimensions and item measures for SCV. Practitioner directed guidance and suggestions are offered for improving SCV during the COVID-19 pandemic and future severe disruptions.
Are Real Estate Investment Trust considered a safe investment choice in times of financial shock?
(2021)
This paper focuses on real estate investment trust (REIT), one specific asset class in the real estate sector, and intends to answer the following research question: are real estate investment trusts a safe choice for investors, particularly during financial shocks? This study re-examines the relationship between public-traded U.S. REIT and other asset classes by implementing statistical dependence analysis and OLS regression to determine whether and how they follow other markets in times of ambiguity. This study's final results indicate that public-traded U.S. REIT investment should not be considered a safe investment during crisis times because it follows the equity market.
The debate over a European Green Deal has contributed to emphasize the need to reduce environmental cost and the urgency of decarbonizing the road transport sector. Therefore, the thesis operationalizes the materialist state theory to distinguish between competing hegemony projects in the struggle over the EU’s mobility transition, in order to identify the driving forces in the European road transport sector. By empirically analyzing the power relations behind the Energy Taxation Directive and the CO2 emissions for passenger cars, the results indicate two dominant hegemony projects that are contesting the implementation of the green transition: 'the authoritarian neoliberal project' and 'the green capitalist project'. None of these projects will be enough to decarbonize the transport sector without shifting emissions and environmental costs elsewhere, nor will either project move away from a car-dependent system.
The coronavirus (COVID-19) outbreak shows that pandemics and epidemics can seriously wreak havoc on supply chains (SC) around the globe. Humanitarian logistics literature has extensively studied epidemic impacts; however, there exists a research gap in understand-ing of pandemic impacts in commercial SCs. To progress in this direction, we present a systematic analysis of the impacts of epidemic outbreaks on SCs guided by a structured lit-erature review that collated a unique set of publications. The literature review findings sug-gest that influenza was the most visible epidemic outbreak reported, and that optimization of resource allocation and distribution emerged as the most popular topic. The streamlining of the literature helps us to reveal several new research tensions and novel categorizations/classifications. Most centrally, we propose a framework for operations and supply chain management at the times of COVID-19 pandemic spanning six perspectives, i.e., adap-tation, digitalization, preparedness, recovery, ripple effect, and sustainability. Utilizing the outcomes of our analysis, we tease out a series of open research questions that would not be observed otherwise. Our study also emphasizes the need and offers directions to advance the literature on the impacts of the epidemic outbreaks on SCs framing a research agenda for scholars and practitioners working on this emerging research stream.
Combinations of art and products are a classic and current topic. Examples like the collaboration of the Medici with artists in the Renaissance or the logo development of Chupa Chups by Salvador Dali are historical examples. The BMW art cars by Jeff Koons and Cao Fei or the art‐based special editions of Louis Vuitton bags are current best practices. All these cases expected a positive impact of art on the product or brand evaluation. This spillover effect was coined “art infusion effect” by Hagtvedt and Patrick. This, as well as further studies on the art infusion effect, are predominantly concerned with classical fine arts. However, despite an observable increase of urban arts‐brands collaborations, the effects of these have not been researched. Our study determines that graffiti and street art are perceived by consumers as art. To confirm the art infusion effect for urban art, a laboratory experiment was conducted. The presence of urban art has a favourable influence on the evaluation of products. These results replicate and extend the findings of Hagtvedt and Patrick. As drivers of the urban art infusion effect, we also identify two additional drivers: the fit between the art and the product and the “lifestyle perception”.