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This paper contends that the use of alternative constructs as Enterprise Risk Management (ERM) measures has made a partial contribution to the different and contradictory results provided by various empirical studies. It further argues that the comprehensive nature of Enterprise Risk Management implies that its adoption and implementation affect different aspects of firms and, therefore, the most appropriate construct to measure it should be equally comprehensive in order to capture all possible signals, outputs and effects from the features of a firm. Secondly, the specialised nature of banking operations and the associated risk necessitates risk measures that suit the peculiarities of the sector and in this study we have proposed banking sector specific ERM model. In this regard, we propose a comprehensive methodology and multifaceted approach to determining ERM measures for the banking sector, taking cognisance of the various components of ERM and the specific needs of the sector. In our proposed comprehensive methodology for determining ERM measures for the banking sector, we combined two important models: the ERM model for the banking sector and the CAMELS model for assessing the performance of banks. The integration of these two models provides a comprehensive and all-encompassing approach for determining ERM measures for the banking sector. Our model is novel and offers a significant contribution to the literature of bank risk management. Our model provides a comprehensive risk management framework for bank executives, bank risk managers, the board members of banks, central banks and the authorities responsible for financial sector stability.

International audience; Exloring the role of different types of investors on stock market is crticial since different types of investors react and behave differently when making investment decision. The role of investor behavior is a very important issue in an immature stock market like Vietnam stock market because the market is characterized by a large number of individual investors and low reporting standard. Institutional and foreign investors however play an influential role due to their large exposure and strong investment expertise. Clearly, examining the role of investor behavior and its impact on the stock market in Vietnam is an important topic in finance. A significant body of empirical research has shown that investor behavior is an essential factor to explain stock price that the classical financial theory cannot explain. This research examines the role of investor behavior in stock market by examining the relationship between investor behavior and stock return using the Vietnamese stock exchange data. We create a sentiment index using the principal components analysis (PCA). Consistent with the sentiment and stock return literature, the research shows a negative contemporaneous relationship between investor sentiment and market return.

The data surveying this study mines methods for extracting informative and interesting knowledge from learning student datasets. The amount of data stored in education big databases is growing at an alarming rate. As such, data processing and knowledge discovery techniques can be used to discover interesting relationships between different characteristics and assessments of students. Initially to propose a novel self-analysis model which performs analysis about the different big data learning services. From the result provided by the user, the service will be ranked and analyze about the knowledge from daily activity gathered by the learner. The proposed model performs analysis to provide efficient Educational Data Mining (EDM) E-Learning system based on machine learning to ranking the users and performance evaluation in their activities. The existing method does not consider the learner activities, ranking and it focus on single feature based classification so the system doesn’t provide a better classification result. These issues to overcome introduce an Extended Multi-Labeled Gradient Boosting (XMGB) improve the classification performance both of imbalanced dataset. The method initial stage apply the Adaptive Expectation-Maximization Mixture (AEMM) method cluster the data various group and extract the E-learning multiple feature set. In this method to compute the classification time and memory provide an efficient result. Then introduce an academic activity based weight score analysis model to evaluate the learner daily activates and exam score its help to the ranking and cluster the data groups. In this proposed method analysis result to evaluate the following performance matrix: Recall, precision, F measure, classification accuracy. In this simulation analysis of the proposed method XMGB are provide an efficient classification with less time complexity compared to other existing methods.

The COVID-19 pandemic is one of the most pressing issues at present. A question which is particularly important for governments and policy makers is the following: Does the virus spread in the same way in different countries? Or are there significant differences in the development of the epidemic? In this paper, we devise new inference methods that allow to detect differences in the development of the COVID-19 epidemic across countries in a statistically rigorous way. In our empirical study, we use the methods to compare the outbreak patterns of the epidemic in a number of European countries.

The scientific advancement in urbanised locations with the relation of the lower classes and discovery to Secularized culture is boosting the growth of food delivery applications and restaurant industries in India. The convenience of users is the primary objective aspect of such, while the second thing that these software's are also beneficial for the development of the market in eating and food supply, as the use of the online platform is a place where the restaurant owner is prepared to attract as many users as he can by allowing them to see the entire food menu in accordance with item name, price, photo, etc. Apps for food delivery have proven to be a big success in India as people are very well about all its operations and services. As one can order food for any type of occasion, it is one of the most comfortable and trusted options available. This helps to save time, resources, fuel and efforts for people, which is one of the key reasons why this industry is thriving and will tend to do so. Therefore, part of my initiative would be to review the theoretical background in various aspects of food sector apps, business models and various factors affecting adoption of food delivery apps that have never been addressed before.

Since 2020, the world is facing a huge pandemic crisis caused by an acute respiratory coronavirus syndrome. Beyond the impact on the population's health or on normal social interaction, the virus led to a huge economic slowdown, requiring the prompt EMU's authority's reaction. The current paper explores how the sovereign yield spreads of EMU countries with respect to German bonds were affected during the pandemic period. To this end, I employ dynamic panel methods, the Pooled Mean Group estimator of Pesaran et al. (1999) and the Dynamic Common Correlated Effects estimator of Chudik and Pesaran (2015), which accounts for heterogeneous effects across countries and the non-stationarity of spreads and of their determinants. The model uncertainty is studied with a Bayesian VAR (BVAR) approach. The results reveal that, in addition to fundamentals (economic growth, large public debt, inflation, financial instability, country's competitiveness and domestic investment), pandemic risk puts also substantial upward pressure on sovereign bond yields both, in the long-run and short-run on the selected period. Pandemic risk seems to raise yield spreads in the 14 EMU countries in the short-run, while disease mitigation measures reduce them in the long-run. Same negative effect of disease mitigation measures on yield spreads are also found with BVAR model. Results are relatively robust across different empirical methods and considered scenarios.

Although many companies pursue agile projects, extant literature reveals a lack of research on project agility determinants. This study examines the project team characteristics' impact on project agility and success using cross-sectional survey data from 292 agile projects. Using agile principles and complex adaptive systems theory, we find that project team autonomy, team diversity, and client collaboration have significant positive relationships with project agility. Project agility, in turn, has a significant positive relationship to project success. We measure project success by on-time completion, on-budget completion, specifications' attainment, and success rating by the project sponsor, client, and project team members. We find that project team members' adaptive performance partially mediates the relationship between project agility and success. These results guide agile project managers while facilitating team members to independently schedule their work, determine effective work methods, and develop innovative solutions. Moreover, they help agile managers recruit team members with relevant, diverse skill sets, domain knowledge, and expertise. Agile project managers must emphasize client collaboration in requirements gathering, designing, testing, and project reviews.

The application of model transformations is a critical component in Model-Driven Engineering (MDE). To ensure the correctness of the generated models, these model transformations need to be extensively tested. However, during the regression testing of these model transformations, it becomes too costly to frequently run a large number of test cases. Test case prioritization techniques are needed to rank the test cases and help the tester during the regression testing to be more efficient. The objective is to rank the fault revealing test cases higher so that a tester can only execute the top ranked test cases and still be able to detect as many faults as possible in the case of limited budget and resources. The aim of this paper is to present a test prioritization approach for the regression testing of model transformations. The approach is based on exploiting the rule coverage information of the test cases. The paper presents an empirical study which compares several techniques introduced by our approach for prioritizing test cases. The approach is complemented with a tool that implements the proposed techniques and can automatically generate test case orderings.

The aim of this study is to figure out advantages and applications of infrared rays system in hospitals, at home and in industry. Methodology that authors use is empirical research and practices and experiences in infrared rays system applications in emerging markets such as Vietnam. Research Results find out that danger for infrared products users as it goes with high temperature, however there are many advantages such as kitchen stoves, temperature guns. Last but not least, infrared rays products has other applications in hospitals. Finally, we suggest there are more further researches on applications of infrared rays products in future. Risk management also need for infrared rays products applications.

Background The United Kingdom National Institute for Health and Care Excellence guidelines recommend that patients and professionals make shared decisions between surgery and stereotactic ablative radiotherapy (SABR) when treating early stage non-small cell lung cancer (NSCLC). Variation by centre suggests treatment decisions may be disproportionately influenced by clinician judgment and treatment availability rather than patient preference. This systematic review critically evaluates studies of patient and clinician preferences for treatment of early stage NSCLC. Methods Primary empirical research up to 30 April 2020 was identified from searches of MEDLINE, EMBASE, PsycInfo and Web of Science databases. Data extracted included: study characteristics and methods, preferences for NSCLC treatment and involvement in decision-making and risk of bias using the Mixed Methods Appraisal Tool. Findings were synthesized using descriptive data and narrative synthesis. Results 23 studies were included in the review; 18 measured patient preferences, 4 clinician preferences and 1 both clinician and patient preferences. Patients and clinicians were both most likely to prefer a collaborative role in treatment decisions. Most patients did not recall there being a choice between surgery or SABR options, and thus experienced minimal decisional conflict. Conclusions For professionals to support patients in making informed, value based decisions about NSCLC treatments, better quality evidence is needed of the clinical and quality of life trade offs for both surgery and SABR.

A fractionally integrated panel data model with a multi-level cross-sectional dependence is proposed. Such dependence is driven by a factor structure that captures comovements between blocks of variables through top-level factors, and within these blocks by non-pervasive factors. The model can include stationary and non-stationary variables, which makes it flexible enough to analyze relevant dynamics that are frequently found in macroeconomic and financial panels. The estimation methodology is based on fractionally differenced block-by-block cross-sectional averages. Monte Carlo simulations suggest that the procedure performs well in typical samples sizes. This methodology is applied to study the long-run relationship between energy consumption and economic growth. The main results suggest that estimates in some empirical studies may have some positive biases caused by neglecting the presence non-pervasive cross-sectional dependence and long-range dependence processes.

Karunanayaka and Tang (2017) introduced a class of compromise designs for estimating main effects under the baseline parameterization. Their approach is to add some runs to the basic one-factor-at-a-time design, and its implementation requires computer search except for the case of adding one run where a theoretical result is available. The reliance on computer search only allowed them to find the limited results from adding up to four runs to the basic one-factor-at-a-time design. In this paper, we provide a systematic construction of compromise designs, without computer search and without restriction on the run size and the number of factors. Closed-form expressions of the bias and efficiency criteria are obtained for the new designs. Both theoretical and empirical studies show that our designs outperform those of Karunanayaka and Tang (2017) saving small-sized problems.

Recently, Social Media (SM)’s adoption as educational instruments in Higher Education (HE) has been gaining popularity among researchers and practitioners. Nonetheless it has been adopted to support various learning approaches such as Cooperative Learning (CL). Despite the growing research in this field, few studies have proposed a comprehensive SM integration framework as well as empirical studies to evaluate its effectiveness. This paper examines how SM is being used as a pedagogical tool in HE and identifies major factors that drives SM’s inclusion in HE classrooms. In addition, the paper investigates the effectiveness of a previously proposed integration framework which was developed to promote CL using a popular SM platform, LinkedIn. A survey was administered to collect data from students in different subjects at a university in Australia. Structural Equation Modelling (SEM) was adopted for data analysis using IBM’s SPSS26 and AMOS26. One of the notable results of this study is the significant positive relation found between the use of the adopted integration framework and CL, SM challenges’ mitigation, students’ intention to use SM and their learning process. The findings of this study have important implications for practice and pave the way for future empirical studies.

This study investigates the relationship between corporate social performance (CSP) and investment inefficiency in the Chinese stock market. Using the unique CSP ratings scores from the Rankins CSP Ratings (RKS), we find that socially responsible firms are more efficient in their investment. We further determine that the effect of CSP in reducing investment inefficiency is more pronounced in overinvestment scenarios. Moreover, we provide strong and robust evidence that CSP significantly improves investment efficiency in state-owned enterprises (SOEs). However, we do not find such evidence in non-SOEs. Our study highlights the important role of state ownership in the CSP-investment inefficiency relationship.

The COVID-19 pandemic that erupted in 2020 forced businesses across the world to adopt virtual meetings. With many people working from home, software platforms like Zoom and Teams became ubiquitous, but their widespread use also revealed many weaknesses and limitations. While technologies for virtual meetings have existed for decades, these technologies have advanced significantly in recent years, and today range from audioconference facilities to telepresence rooms with high-resolution video and sophisticated virtual presence features. The available alternatives differ significantly in costs, complexity and capabilities, and choosing the most effective technology for each meeting setting is not always easy. This is important, since after the pandemic, virtual meetings will move from being a necessity brought on by the pandemic to being a widely accepted alternative to traditional face-to-face meetings. Consequently, the questions of when and how to meet virtually will become even more significant. In this article, we describe a decision-making framework for choosing when and how to meet virtually, based on matching the appropriate communication capabilities with various meeting objectives and taking into account meeting size and duration. The framework is based on extensive empirical research conducted in partnership with several major U.S. and European companies.

Uncertainty surrounding the risk of developing and dying from Thrombosis and Thromobocytopenia Syndrome (TTS) associated with the AstraZeneca (AZ) COVID-19 vaccine may contribute to vaccine hesitancy. A model is urgently needed to combine and effectively communicate the existing evidence on the risks versus benefits of the AZ vaccine. We developed a Bayesian network to consolidate the existing evidence on risks and benefits of the AZ vaccine, and parameterised the model using data from a range of empirical studies, government reports, and expert advisory groups. Expert judgement was used to interpret the available evidence and determine the structure of the model, relevant variables, data to be included, and how these data were used to inform the model.The model can be used as a decision support tool to generate scenarios based on age, sex, virus variant and community transmission rates, making it a useful for individuals, clinicians, and researchers to assess the chances of different health outcomes. Model outputs include the risk of dying from TTS following the AZ COVID-19 vaccine, the risk of dying from COVID-19 or COVID-19-associated atypical severe blood clots under different scenarios. Although the model is focused on Australia, it can be easily adaptable to international settings by re-parameterising it with local data. This paper provides detailed description of the model-building methodology, which can used to expand the scope of the model to include other COVID-19 vaccines, booster doses, comorbidities and other health outcomes (e.g., long COVID) to ensure the model remains relevant in the face of constantly changing discussion on risks versus benefits of COVID-19 vaccination.

Many older patients with cancer depend on their family members for care and support and involve their family members in treatment decision-making in different stages of the cancer trajectory. Although family involvement is advocated in person-centered care, little is known about family involvement in decision-making specifically for older patients, and evidence-based strategies are scarce. The aim of this scoping review is to provide deeper understanding of factors influencing family involvement in treatment decision-making for older patients with cancer. Four databases were searched for quantitative-, qualitative- and mixed-method empirical studies describing factors influencing family involvement in treatment decision-making for older patients with cancer: PubMed, EMBASE, CINAHL and PsycINFO. Three independent researchers reviewed the papers for eligibility and quality and contributed to the data extraction and analysis. Twenty-seven papers were included, sixteen quantitative studies, nine qualitative studies and two mixed-method studies. Five categories of factors influencing family involvement emerged: 1) patient characteristics, 2) family member characteristics, 3) family system characteristics, 4) physician’s role and 5) cultural influences. These factors affect the level of family control in decision-making, treatment choice, decision agreement, and levels of stress and coping strategies of patients and family members. This review reveals a complex interplay of factors influencing family involvement in treatment decision-making for older patients with cancer that is rooted in characteristics of the family system. The findings underscore the need for development and implementation of evidence-based strategies for family involvement in treatment decision-making as part of patient-centered care for older patients with cancer.

This paper offers an alternative approach for engaging sociologically, ontologically, and politically with digital farming platforms. A challenge faced by any approach looking to upend intellectual conventions, especially ontological ones, lies in the question of representation, namely, how do we talk about digitization in novel theoretical ways using language rooted in more than two millennia of western human-centered thought? One way to deal with this challenge, the one adopted in this paper, involves decentering familiar terms and repurposing them. Thus, rather than organizing the argument around familiar terms like small and big data, I argue instead that we organize data-assemblages according to what they do, which makes this a political ontological project. To do this, I offer the relational predicate of weak data and strong data, suggesting that they are good to think with in this regard. The argument ultimately lands at a place for analyzing these platforms that offers the potential for critique and perhaps even a degree of optimism, as I suggest a framework for normatively evaluating these practices. The paper draws upon various empirical studies of digital agriculture conducted by the author, which include interviews with farmers, farm laborers, hacktivists, investors, and engineers from numerous countries and locales.

In the current, increasingly dynamic environment, more attention is being devoted to ambidexterity (both explorative and exploitative learning), as it is beneficial to organizational functioning. Despite an abundance of research on organizational ambidexterity, there is a paucity of research examining team-level ambidexterity. In a cross-level empirical study, we examine a leader's dialectical thinking as an antecedent of team-level ambidexterity. We also incorporate collective team identification as a moderator in the study. Our findings support that a leader's dialectical thinking can help facilitate team ambidexterity that, in turn, improves employee performance. Those teams whose members have strong team identification and whose leader is high in dialectical thinking will reap the most benefit from team ambidexterity.

Ecological protection of grassland is an important part of the construction of ecological civilization in China. Grassland ecological compensation and reward policy is an important policy for grassland ecological protection in China. As the main participants in the grassland ecological compensation and reward policy, herders' behavioral response largely affects the effect of policy implementation. In this study, 550 herders in two regions with different compensation standards were selected as samples, and the comparative analysis of herdsmen's response to policy behavior and influence factors was carried out. The results showed that the grassland area owned by herders, ethnic group and perception of policies were important factors to affect the response of herdsmen's behavior in the two regions, but larger differences existed in the two regions. The grassland area and family size owned by herders are important factors that affect the response of herdsmen's behavior in Qinghai-Tibet Plateau; However, the ethnic group, the understanding of policy and family income are the important factors in the western desert area.

The Ethereum blockchain is a distributed database of transactions, where the Gas Oracles suggest the users the Gas price’s categories to get a transaction recorded. The paper explores the idea that the Gas Oracles are based on a data-centered model which does not provide users with a reliable prediction. We present an empirical study to test the reliability of the existing Gas Oracles from both the points of view of the Gas price predictions and the existing categories. The study reveals that the Gas Oracles’ predictions fail more often than advertised and shows that the Gas price categories do not correspond to the categories set by the users. Therefore we propose a user-oriented model for the Oracles’ Gas price prediction, based on two Gas price categories actually corresponding to the users’ interests and a new method to estimate the Gas price. The new method, performing the Poisson regression at smaller intervals of time, predicts the Gas price to pay with a lower margin of error when compared to the actual one. The predictions based on the user-oriented model thus provide the users with a more effective Gas price to set.

This paper examines volatility persistence in leverage loan market price series for Australia, Canada, Europe, Japan, Singapore, UK and USA in the presence of structural breaks. To the best of our knowledge, this is the first empirical study to examine volatility persistence in the leveraged loan markets. To this end, using fractional integration methods, the results indicate that both absolute and squared returns display long memory features, with orders of integration confirming the long memory hypothesis. However, after accounting for structural breaks, we find a reduction in the degree of persistence in the leveraged loan market. The evidence of persistence in volatility implies that market participants who want to make gains across trading scales need to factor the persistence properties of leveraged loan price series in their valuation and forecasting models since that will help improve long-term volatility market forecasts and optimal hedging decisions. post-print 759 KB

This paper contains a regression analysis of a database that merges the data of the Chinese Customs Database with the Chinese Industrial Enterprise Database. Empirical testing verifies, from the perspective of independent innovation, the impact of intermediate imports on export sophistication. The findings include three parts. First, intermediate imports contribute significantly and positively to manufacturing technological innovation, although the promotive role differs significantly with enterprise ownership, trade type, country of export destination and technological strengths. Second, although independent innovation has a direct promotive impact on export sophistication, it restrains the positive promotive role of intermediate imports on export sophistication, i.e., there is a negative moderation effect. Third, intermediate imports has a stronger promotive impact on enterprises at the nascence and growth stages, while independent innovation has a stronger promotive impact on enterprises at the growth and maturity stages.

Going back to the roots of modern business cycle modelling, the paper advances a macroeconomic model of a most elementary (and now largely forgotten) cycle mechanism within a growth framework. Essentially, it combines a nonlinear Harrodian instability for a business sentiment index with Harrod’s and Kaldor’s old idea that firms tend to reduce investment if they have already built up high capacities relative to their assessment of the normal market potential. For greater realism, a previous version is augmented by a delay in the capital growth rate and by in general three random shock processes. This makes it possible to estimate the model on quarterly US data of utilization and a certain capital ratio. The focus is on an inquiry into the model’s goodness-of-fit, for which the method of simulated moments is a fruitful technique. Here, 14 fairly ambitious summary statistics that the model should try to reproduce allow a detailed diagnosis of its merits and demerits. The results are sufficiently encouraging and form a sound basis for stepwise extensions of the model in future work, or for refinements in the estimation procedure.

Children with medical complexity have ongoing health needs that may require dependence on medical technologies. While hospital admissions are an important focus of care delivery for these children, a majority of the time they are cared for in their own homes. Parents' report feeling overwhelmed and stressed by the scope of their responsibilities, but they become sophisticated care providers and assume greater authority when providing their children's care at home. Communication, decision-making, and dealing with conflict with members of health care teams have been central concerns in parents' reports of their home health care experiences. The objective is to review literature on relational aspects of parent and home health care provider care practices for children with medical complexity receiving home health care services. A narrative review was conducted. A search of MEDLINE, EMBASE, EBM Reviews, PsychINFO, ERIC, and CINAHL databases for English language studies published since database inception was carried out. Eligible studies focused on relational aspects of parent and home health care provider care practices for children with medical complexity receiving home health care services. Nine empirical studies were selected for this review. Literature describes parents' and providers' experiences managing a child with medical complexity in the home and the effects for the family and the parent-health care provider relationship. Parents want to be actively involved in all aspects of care that affects their child and ultimately their family. Further investigation is needed to better understand relational aspects of parent-home health care provider care practices to support child/family health and well-being in the home setting.

Companies rely widely on coopetition, that is, cooperation with competitors, to foster their innovation. However, despite its increasing popularity, the impact of coopetition on innovation remains unclear. Different theories predict different results, and empirical evidence suggests that coopetition can either improve or reduce innovation performance, or even make no impact. The lack of consensus makes further exploration of this important relationship essential. Our study attempts to shed new light on the impact of sourcing from competitors on innovation, using a data-driven exploratory model that needs no prior specification of the direction and form of this relationship. We use a recently developed analytical method based on the preference disaggregation approach to analyze data from a sample of 112 firms operating in the petroleum, chemical, and pharmaceutical industries. We find that sourcing from competitors fosters the innovation performance of firms with financial constraints (small firms receiving no financial support from the government for innovation). In contrast, our results show that sourcing from competitors has a non-linear negative impact on large and small firms receiving such financial support, and that sourcing from competitors is much riskier for small firms in this category. Our findings contribute to the coopetition for innovation literature by highlighting that the relationship between sourcing from competitors and innovation performance depends on firm characteristics, i.e., size and financial capability. Our use of a preference disaggregation method confirms its value when studying relationships between variables where the conceptual and empirical evidence leaves this relationship unclear.

There is a gap in the literature of strategic investment decision making (SIDM) concerning the links between context variables, infrastructure and SIDM practices (or SIDM styles) in developing countries. This research aims to fill that gap. Survey data (N = 128) from the manufacturing companies listed on the Pakistan Stock Exchange was collected via an e-mailed questionnaire and visits to the companies. Data are analyzed with factor analysis and multiple regression analysis. The results reveal what we call four different SIDM styles (or emphases): managerial power-, financial analysis-, top management-, and strategy-based investing. These styles are considered to be novel perspectives on SIDM that have not been discussed in the previous literature. We find that high perceived profitability is linked only to financial analysis based investing style. Further, contextual variables, such as legislation or local gas shortfalls, have a significant impact on the SIDM style. The findings provide decision-makers and analysts with a comprehensive picture of strategic investment decisions (SIDs) in a developing market. For example, firms may benefit from analyzing their decision-making styles and their own SIDM context. There are no recent studies on SIDM practices with emphasis on a developing country specific local context issues. So, this research first fills the gap in the literature, by surveying the current SIDM practices in a developing country, using local context variables, and, by analyzing the different styles of SIDM. peerReviewed

How does sentiment in a pitch affect an entrepreneur’s fundraising outcomes? Although research suggests that negativity in entrepreneurial “pitches” to investors adversely impacts resource acquisition, there is a lack of empirical research showing whether, and to what extent, this is true. We study over 30,000 entrepreneurial loan requests from one of the largest loan marketplaces to understand how the sentiment in text-only pitches to investors affects fundraising. In contrast to prior literature, we find that negatively-worded pitches are funded faster than positively-worded ones. We also find that negatively-worded pitches result in lower interest rates to entrepreneurs. Finally, we find that negatively-worded loans default less, suggesting that the benefits of negativity are not one-sided but two-sided: negativity helps both resource seekers and resource providers. Collectively, the results from our study reveal how negativity can be beneficial in impression management and offer fresh insights for the psychological foundations of entrepreneurship.

This study investigates the factors that drive an innovation network formation comprising companies, government, and society, and the ways these actors contribute and collaborate within a network to develop technologies that have a social impact. A conceptual framework has been developed by combining literature-based arguments and insights from two cases of smart city innovation. This study demonstrates that the innovation network is driven by the activities of searching, acting, and convincing actors of an opportunity to develop smart city solutions. The findings also show that innovation networks emerge not solely from a business goal, but also from a social goal and can still generate business opportunities for companies. Therefore, innovation for smart cities specifically requires a new form of configuration (public–private and citizens’ participation), drivers (economic and social), and resources (technological and non-technological) in both its development and implementation. The analysis of the different configurations suggests more/less effective innovation.

This research is one of the few studies to investigate the mutual impact between residential segregation and the commuting outcomes of minority groups in the context of a developing country. Based on previous empirical studies that acknowledged the interactions between the built environment and travel behavior, we develop a Structural Equation Model to illustrate the multi-directional relationships between residential segregation, the built environment and commute outcomes. With survey data collected from migrant workers in 12 Chinese cities, we apply this model to estimate these complex mutual relationships. Our major findings include: 1) Both residential segregation and the built environment have significant causal impact on migrants' commute distance and duration. Distance from home to downtown has the strongest causal effect on commute outcomes, while residential segregation in urban villages ranks the second. 2) Residential sorting is observed. Commuting distance and duration significantly affects migrants' choice of built environment features. 3) However, commuting outcomes are not a major (or statistically significant) driver of migrant workers’ residential segregation in urban villages. In other words, their choice to reside in urban villages is inelastic to the commuting outcomes of these villages. Other factors such as housing affordability may be more influential. Therefore, demolishing urban villages under “urban renewal” policies will force migrants to relocate to more remote areas with worse job accessibility. These findings have important implications for recently proposed urban renewal policies that are bringing large-scale changes in China. They also offer important insights for other developing countries that are experiencing rapid urbanization. Finally, the framework of this study can be generalized and applied to other countries to examine similar urban issues.

This article presents a new way of implementing corporate social innovations (CSI) through cross-collaboration, by analyzing the integration of these two constructs, which are usually studied separately, offering an actual perspective to address persisting social problems while reaching business results. Our findings are based on case studies conducted with key executives and consultants from social enterprises, nonprofits, multinational corporations, large enterprises, and B-Corps from Brazil, USA, Canada, Iceland, and Sweden to compare different regions with different social problems using Mirvis and Googins (2017) framework to analyze the integration of CSI and cross-collaboration in corporate strategies. Results allow us to propose a new spectrum between social and commercial business models and their relationships with governments and civil society, demonstrating that the action of all stakeholders is already taking place in different ways according to the realities of each region, promoting impactful results that are beneficial for business and society.

Many studies have found a positive association between liberal political views and support for environmental protection activities even though they require greater involvement of the state in market economies. However, such a conclusion is contradicted by empirical studies on pro-environmental activities with regard to the theory of planned behavior. On the one hand, subjective norms (including the expectations of public authorities) are usually found to be insignificant or very weak drivers of ecological behavior. On the other hand, self-efficacy (individual attitudes and beliefs in one’s capabilities) is perceived as a leading factor. This inspired us to explore the long-term effects of localism on environmental quality in Poland. Because liberalism and conservatism exist in their ‘pure’ forms, Poland is very well suited to such a study. The aim of the article is to assess the long-term impact of local and liberal orientations (with conservatism as the reference), as reflected by electoral decisions on the quality of the environment. First, the current state of the environment is measured over four dimensions (air, water, soil, and biodiversity), and the effects of environmental policies (including spatial dependence) are estimated. Subsequently, the treatment effects on the environment when a particular political orientation is adopted are estimated using multi-valued treatment effect analyses. The study confirms that local orientation is conducive to long-term environmental care. Moreover, greater coordination of environmental policies at different levels is recommended, focusing on the negative spatial dependence of local environmental expenditure.

Confusion in previous studies over how and when cognitive diversity results in higher or lower performance has led to an examination of the influence of a range of moderator variables. Our study provides new theoretical insights into this field of research by extending the categorization–elaboration model (CEM) through an examination of the interaction effects of multiple convergent demographic diversities on information elaboration, team processes and creativity. Based on comparative fit and an in-group projection model of social categorization theory, the moderating influence of demographic faultlines and subgroup imbalance are also investigated. Using hierarchical regression analysis (HRA), empirical support from 453 team members nested within 91 teams across five organizations was found for the mediating role of team information elaboration on the relationship between cognitive diversity and team creativity. Furthermore, both demographic faultlines and subgroup balance were found to moderate the relationship between cognitive diversity and team information elaboration. Our study provides additional clarity concerning processes underlying the positive effects of cognitive diversity on team performance.

Objective : To examine the role of explainability in machine learning for healthcare (MLHC), and its necessity and significance with respect to effective and ethical MLHC application. Study Design and Setting : This commentary engages with the growing and dynamic corpus of literature on the use of MLHC and artificial intelligence (AI) in medicine, which provide the context for a focused narrative review of arguments presented in favour of and opposition to explainability in MLHC. Results : We find that concerns regarding explainability are not limited to MLHC, but rather extend to numerous well-validated treatment interventions as well as to human clinical judgment itself. We examine the role of evidence-based medicine in evaluating inexplicable treatments and technologies, and highlight the analogy between the concept of explainability in MLHC and the related concept of mechanistic reasoning in evidence-based medicine. Conclusion : Ultimately, we conclude that the value of explainability in MLHC is not intrinsic, but is instead instrumental to achieving greater imperatives such as performance and trust. We caution against the uncompromising pursuit of explainability, and advocate instead for the development of robust empirical methods to successfully evaluate increasingly inexplicable algorithmic systems.

Auditing is a collective process conducted by a professional accounting team with a wide range of skills and experience. Audit work is labor-intensive, and audit team diversity, particularly gender diversity, is considered to have both auditing and economic effects. However, there is scant empirical research on the way that audit engagements are conducted and the role of audit team leaders (ATLs), specifically female ATLs, in the audit effort, because they and other audit team members mainly develop audit strategies in practice during the planification period. A hand-collected private data set was gathered from Spanish small and medium-sized audit firms from 2001 to 2015. After controlling for endogeneity, the analysis shows a more efficient audit effort by female ATLs and their audit teams when task complexity increases. Furthermore, the findings show a masculinization process by female ATLs when their experience rises, which may be interpreted as intrinsic motivation for future promotions. Additional analyses also reveal differences in female ATLs’ behavior when there are shorter audit firm tenures and larger audit firm sizes. Overall, our empirical evidence is in line with the idea that gender diversity is positive for audit efficiency. Our study responds to calls from regulators to consider audit team attributes as a relevant factor in the quality of audit quality practices.

Mental health disparities among racial and ethnic minorities (R&EM) are well documented. Some of the variables driving these disparities are limited care availability, difficulty accessing services, and attitudinal barriers. Although no single approach will eliminate all these obstacles, the use of technology to provide mental health services represents a paradigmatic shift in care delivery that could reduce unmet mental health need. Despite increasing evidence supporting the feasibility and efficacy of behavioral intervention technologies (BITs), such evidence is more limited among R&EM. For BITs to truly reduce disparities in care, these interventions need to overcome common barriers to treatment that disproportionally affect R&EM. This article reviews the empirical support of different BIT modalities with R&EM. We then provide informed clinical recommendations for the use of BITs with these groups, as well as a case example illustrating these guidelines. We conclude this article by discussing future directions that can inform the development and refinement of BIT approaches for R&EM.

Carefully considering the Capability Maturity Model Integration (CMMI) Level 2 specific practice to “define project life cycle phases” (SP 1.3) is a key requirement, particularly when it comes to small and medium-sized software development organizations. This is a necessary step to help these organizations get nearer to achieving CMMI Level 2 certification. In this paper, we, therefore, report on our latest empirical study that recently explored both the perceptions and experiences of practitioners about SP 1.3 implementation. During our research, we visited three firms and carried out three in-depth interviews. We developed a SP 1.3 model using the different experiences and opinions of practitioners regarding SP 1.3 implementation. The four essential stages of this model are plan, design, review, and update/rework. Practitioners will receive advice in this model about implementing SP 1.3 effectively.

Environmental sustainability has gained more importance in recent time to ensure socio-economic sustainability via healthy environment. The current decade has witnessed the rise of empirical research in the domain of ecological footprint, which has become a major scholarly area among environmental researchers. However, several key factors determining ecological footprint have been inadequately dealt with in the existing body of knowledge. The current research endeavour explores the association between economic complexity, human capital, renewable energy, urbanization, economic development, and ecological footprint in 20 countries divided into two panels (leading ten economic complex and renewable energy-consuming countries). This study applied panel data estimators, for instance, fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS) and the canonical cointegrating regression (CCR) long-run estimators to the period from 1980 to 2017. The results reveal that economic complexity, economic development, and urbanization increase the ecological footprint in both panels. Human capital and renewable energy induce ecological footprint, while renewable energy increases ecological footprint in the first panel. Based on empirical evidence, the study recommends the governments of these countries promote production processes that are friendlier to the environment and introduce greener technologies in exports for a drastic reduction in environmental pollution.

Many consumers have become disillusioned with globalization and harbor anxieties about its various aspects. Yet there is scant empirical research on the subject, and little effort has been made to define antiglobalization from a consumer perspective. Building on Appadurai’s (1990) theoretical typology of global forces—ethnoscapes, mediascapes, technoscapes, finanscapes, and ideoscapes—we develop a multidimensional scale for operationalizing ‘antiglobalscapes’, specifically, consumers’ trepidations about each of these. We investigate how antiglobalscapes operate within a nomological net of ingroup/outgroup orientations that are used to segment consumer markets: cosmopolitanism, xenocentrism, identification with global consumer culture, and consumer ethnocentrism. Factor analyses revealed six antiglobalscape dimensions, representing apprehensions for five forces, with finanscapes splitting into two. Regressions revealed that the orientations differentially predicted the expression of each antiglobalscape. We provide researchers with a tool to understand consumers’ multifaceted globalization apprehensions, and show that their intensity is a function of the four consumer orientations.

Discrimination has a negative impact on minority group members’ wellbeing, particularly immigrants. Yet, empirical research investigating coping mechanisms against perceived discrimination among immigrants remains scarce. The present study examined the association between perceived ethnic discrimination and eudaimonic (psychological and social) wellbeing among immigrants by assessing the role of intergroup (downward and upward) and intrapersonal (temporal) comparisons as coping mechanisms in this association. Using a representative sample comprising five foreign-born immigrant groups (N = 1250) in Spain, we found that immigrants tend to engage in more favorable downward and upward intergroup comparisons in response to perceived discrimination, but lower temporal intrapersonal comparisons. Downward intergroup comparison was related to lower psychological wellbeing and upward intergroup comparison was associated with higher psychological wellbeing. Temporal intrapersonal comparison was predominately predictive of psychological and social wellbeing. We further discuss specific indirect effects and the important roles of intergroup and intrapersonal comparisons for immigrants’ wellbeing.

The continuous debate and research related to High Frequency Trading emphasises the importance of performing analysis on topics associated with the interaction of traders with different trading speeds, and the effects of this interaction on price efficiency and market quality. The aim of this paper is directly related to the analysis on how information diffusion may affect order submission decisions of traders, with different trading speeds and how these may affect price efficiency or market quality. The incorporation of information in submitted orders evidently affects prices, but it may also affect market quality for different reasons e.g., the use of low-quality information, or the fact that slower traders may be considering information that has already been incorporated in the market prices by faster traders, hence leading to the mispricing of the fair value. This paper provides findings on the above-mentioned topics, based on a literature review - both theoretical or of empirical studies (which are scarcely available) and leads to the conclusion that High Frequency Traders could be treated as natural “insider traders” leading to significant price efficiency, as understood by the Efficient Market Hypothesis.

Empirical studies have shown that Federal Open Market Committee (FOMC) announcements affect the exchange rate. Because of different policy targets, the exchange rate response to specific events may vary. Based on the theory of directional change (DC), this study analyzes the effects of specific FOMC announcements on exchange rates, specifically examining the continuous effects using a variable for DC volatility and high-frequency data. We find that, in contrast to variables for volatility based on time-series data, our variable for DC volatility is sensitive to the effects of specific FOMC announcements on exchange rates. Specifically, the exchange rate has a strong response to announcements of an expansionary monetary policy and a weaker response to a tighter or unchanged policy. This study presents a new explanation for the effects of FOMC announcements on exchange rates. Moreover, our variable for DC volatility has the potential to be used for measuring the impact of other events on financial markets.

Proponents of the agile approach to project management describe this new way of working as more efficient, more productive, liberating, and one in which the team has greater control. We examine the experience of implementing agile practices within project management through nine agile teams in the financial services sector. Using grounded theory analysis of data from semi-structured interviews, several thematic categories emerge including accountability, team-based perceptions of their place in the organization, human factors in agile teams, and technical factors with the implementation of agile practice. As a result of these findings, we propose a framework describing how IT project teams experience accountability arrangements when transitioning into agile practices. The accountability experience is a result of the fit between the teams’ perception of how they are viewed as agile teams in their organizations, their experience of technical practices of agile implementation, and their experience of interaction and exchanges in their teams. We conclude with a call towards recognizing the dissonance between different expectations of accountability and a call for further empirical research into this phenomenon.

Renewable energy plays an important role in the modern economic growth paradigm. As a perpetual source, solar-based renewable energy has the ability to reduce CO2 emissions, which has been neglected in prior empirical studies. We have analyzed the asymmetric association between solar energy consumption and CO2 emissions in the top ten solar energy-consuming countries (Australia, Germany, Japan, Spain, Italy, USA, South Korea, UK, France, and China). Using data from 1991 to 2018, a novel methodology, ‘Quantile-on-Quantile (QQ)’, is applied. The results explore the mode of how quantiles of solar energy consumption asymmetrically affect the quantiles of CO2 emissions by providing an adequate framework to comprehend the overall dependence structure. The empirical findings demonstrate that solar energy consumption reduces CO2 emissions at different quantiles for all selected countries except France. The overall relationship is stronger at higher quantiles of CO2 emissions for various countries. The outcomes suggest that the intensity of asymmetric relationship in solar energy-CO2 emissions nexus differs with countries that need individual caution and attention for governments in formulating the policies connected to solar energy and the environment. Our empirical evidence also emphasizes that solar energy should be integrated for sustainable growth and environmental quality.

Over the past few decades there has been an increasing concern towards environmental issues and its resulting impact on living beings. Government and other active environmental groups are putting pressure on business to follow practices that are sustainable and environment friendly. The aim of this research is to analyse an integrated model in the content of Green Products using Structural Equation Modelling. The study aims at establishing the relationship of religiosity on environmental concern, and of environmental concern, functional value, social value, and generativity on willingness to pay for green products. The data of 240 respondents was analysed. The findings reflect that willingness to pay for green products was positively impacted by environmental concern, social value, functional value and generativity and that religiosity have a significant impact on environmental concern. The presented research can help the marketers in understanding consumer buying behaviour for green products. The study also suggests avenues for future research.

The challenge of food waste in the global foodservice sector is significant, and novel approaches are required for its minimization. These approaches can be grounded on the principles of industrial symbiosis which promote inter-sectoral collaboration for better resource efficiency. Foodservices can partner with farmers for food waste collection and its subsequent use as fertilizer or animal feedstock. Anecdotal evidence of industrial symbiosis adopted by foodservices and farmers exists, but no empirical research has been undertaken to understand the determinants of broader industry adoption of industrial symbiosis and how these determinants could be reinforced. This study explores the potential of industrial symbiosis to minimize food waste in commercial foodservices in Russia. By interviewing foodservice providers and farmers, the study demonstrates willingness of selected stakeholders to uptake industrial symbiosis as a means of food waste recovery, but also as an opportunity to reinforce social and network capital of foodservice operators and farmers. To encourage broader industry uptake, policy-makers should build inter-sectoral capacity by connecting foodservices and farmers. Not only will this minimize food waste but can also build more resilient and responsive food supply chains.

Children with attention-deficit/hyperactivity disorder experience significant academic, social, and behavioral impairments in elementary school settings. Although psychopharmacologic treatments can improve symptomatic behaviors, these rarely are sufficient for enhancing school performance. Thus, medication should be supplemented by one or more school interventions, including behavioral strategies, academic interventions, behavioral peer interventions, organizational skills training, and self-regulation strategies. Although all of these school interventions have been found effective, classroom behavioral strategies, organizational skills training, and self-regulation strategies have the strongest empirical support. Clinicians should collaborate with school mental health professionals to encourage implementation of effective school interventions across school years.

Many empirical studies suggest that the realized values of highest-ranked decision alternatives tend to be systematically lower than estimated, causing the decision-maker to experience post-decision disappointment. This systematic overestimation of value has been explained by a systematic bias in the alternatives’ estimated values resulting from, e.g., a behavioural disposition towards overoptimism or even strategic misrepresentation. Nevertheless, even if these estimates are unbiased, the value of the selected alternative is likely to be overestimated due to selection bias. In this paper, we build models for measuring the shares of systematic and selection biases in generating post-decision disappointment, and develop approaches for estimating these models from data which contains the estimated values of all alternatives but the realized values of selected alternatives only. Results obtained from applying these models to real data on 5,610 transportation infrastructure projects suggest that out of the total cost overrun of $2.77 billion, only ca. 24% can be attributed to systematic bias.