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Big Data has received great attention in academic literature and industry papers. Most of the experiments and studies focused on publishing results of big data technologies development, machine learning algorithms, and data analytics. To the best of our knowledge, there is not yet any comprehensive empirical study in the academic literature on big data technology acceptance. The statistical results of this model provide a compelling explanation of the relationships among the antecedent variables and the dependent variables. The analysis of the structural model reveals that the hypothesis tests are significant for 8 out of 12 path relationships.

This article introduces a new systemic theory called “The Three Dimensional Spiral of Sense”, applied to Identity and Professionalization. The epistemological mainstays of the theory are stressed here, a theory supported by more than 30 years of empirical research at CONICET (National Council of Scientific Research, Argentina), with individuals belonging to different populations, some of them covering periods of over 20 years (intra-generational studies), and others covering three generations in-line (inter-generational studies). This article presents the most specific theoretical frameworks, and it formulates the six disciplinary areas in which the new analysis of the social data was carried out: Education, Health, Science, Media, International Relations and Interculturality. The first area – Education – is dealt with through different levels (secondary level, tertiary level, University and PhD training. Here, we only make reference to the studies carried out, returning to some epistemological issues in this theory. The methodology used was quantitative (statistical analysis, a semi-structured survey) but mainly qualitative (hierarchical evocations, interviews). The approach was macro-micro-meso-macro, micro, not quite common yet. It consists of a kind of sui generis systemism which recovers relationships (links, back and forth) between individuals and contexts, without overlooking neither the former nor the latter, thus, avoiding any type of reductionism. Individuals, organizations and frameworks interplay and feedback themselves. The results, particularly the qualitative ones, show the rich interactions underlying the continuance or innovation processes, which favor or hinder the individuals’ development and identity in times of abrupt change; at the same time, these results reveal the need for Professionalization in emerging countries Fil: Aparicio, Miriam Teresita. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo; Argentina

In this article the elements of institutional environment development and strategies for solving the problem of stable functioning of social and economic sphere of life of society are considered, discussed and analyzed. The relevance of the study is an attempt to highlight the main indicators of modernization of the institutional environment of Ukraine. These indicators are: global index of the effectiveness of public administration, the index of incapacity of states, the index of global competitiveness, the index of economic freedom, the index of easiness of doing business, the index of acceptance of corruption. All these indicators are included in the process of formation of the institutional environment of Ukraine. The research is based on the methods of the empirical research:, observation, comparison, and, in addition, on the methods of content analysis. Results. The institutional environment provides the basis for the activity for the subjects of the socioeconomic system and also defines the vector of its development. The Global Competitiveness Index not only assesses the degree of competitiveness of a country, but also helps to see the real situation in the country. It shows its competitive advantages and problems that slow down economic development. The Index of Economic Freedom identifies the main problems of an optimal level of economic freedom. It shows that the higher the index of economic freedom leads to the higher level of welfare of the population. The Easiness of Doing Business Index assesses the «rules of the game». In other words, laws, regulations, and other government acts that have impact on reducing unprofitable and bankrupt businesses. This index, in addition, assesses the condition for facilitating for doing business in Ukraine. The Failed States Index helps to analyze Ukraine's ability to control the integrity of its territory, political, demographic, economic and social situation in the country The Corruption Perceptions Index is usually used in political and econometric studies to explain the effectiveness of government administration, economic development and the level of democracy in Ukraine. Moreover, it is used to reduce the corruption in government-business relations. The institutional environment in Ukraine continuously forming. This is why the analysis of some elements of the development of the institutional environment in Ukraine is necessary to identify the optimal strategy for solving the problem of stable functioning of social and economic sphere of life of society

Empirical research aims to establish generalizable claims from data. Such claims involve concepts that often must be measured indirectly by using indicators. Construct validity is concerned with whether one can justifiably make claims at the conceptual level that are supported by results at the operational level. We report a quantitative analysis of the awareness of construct validity in the software engineering literature between 2000 and 2019 and a qualitative review of 83 articles about human-centric experiments published in five high-quality journals between 2015 and 2019. Over the two decades, the appearance in the literature of the term construct validity increased sevenfold. Some of the reviewed articles we reviewed employed various ways to ensure that the indicators span the concept in an unbiased manner. We also found articles that reuse formerly validated constructs. However, the articles disagree about how to define construct validity. Several interpret construct validity excessively by including threats to internal, external, or statistical conclusion validity. A few articles also include fundamental challenges of a study, such as cheating and misunderstandings of experiment material. The diversity of topics discussed makes us recommend a minimalist approach to construct validity. We propose seven guidelines to establish a common ground for addressing construct validity in software engineering.

The Internet provides the most extensive platforms for information and knowledge seeking. Accordingly, investigating antecedents for online knowledge seeking becomes an important topic for managing knowledge-based work and innovation. Taking an integrative approach drawing on social cognitive theory, expectancy theory, and theory of reasoned action, this article examines the antecedents of online knowledge seeking with a sample of 246 professionals in 43 technical RD and 2) norms of knowledge sharing positively moderate the relationship between extrinsic motivation and online knowledge seeking. This article is one of the few that develop and empirically test an integrative model of antecedents for online knowledge seeking. The findings not only contribute to the knowledge seeking and knowledge management literatures, but also shed light on managing employee knowledge behaviors and learning via the means of the Internet.

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.

Theories can be represented as statistical models for empirical testing. There is a vast literature on model selection and multimodel inference that focuses on how to assess which statistical model, and therefore which theory, best fits the available data. For example, given some data, one can compare models on various information criterion or other fit statistics. However, what these indices fail to capture is the full range of counterfactuals. That is, some models may fit the given data better not because they represent a more correct theory, but simply because these models have more fit propensity - a tendency to fit a wider range of data, even nonsensical data, better. Current approaches fall short in considering the principle of parsimony (Occam’s Razor), often equating it with the number of model parameters. Here we offer a toolkit for researchers to better study and understand parsimony through the fit propensity of Structural Equation Models. We provide an R package (ockhamSEM) built on the popular lavaan package. To illustrate the importance of evaluating fit propensity, we use ockhamSEM to investigate the factor structure of the Rosenberg Self-Esteem Scale.

PurposeThis paper aims to examine the extent to which the cultural dimension of individualism/collectivism matters for international differences in climate change policy performance. This study postulates that individualistic societies, relative to their collectivistic counterparts, are more likely to address global climate change.Design/methodology/approachThe main hypothesis is tested using data for a world sample of up to 92 countries. To achieve causal inference, this study isolates exogenous sources of variation in individualistic cultures, based on blood distance to the UK and historical pathogen prevalence.FindingsThe core results suggest that individualistic countries are characterized by greater climate change policy performance. This study also finds evidence that individualism affects climate change policy adoption through enhancing governance and female political representation. Subnational analyses based on data from the World Values Survey indicate that survey participants with an orientation toward individualism tend to self-report positive attitudes to pro-environmental policies.Research limitations/implicationsThe main findings help improve the understanding of the deep origins of climate change policy performance, which is relevant for formulating policies that help mitigate the consequences of changing climate conditions.Originality/valueTo the best of the author’s knowledge, this paper is the first study to link cultural traits of individualism and climate change policy performance across countries.

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.

A theory-first paradigm tends to be the dominant approach in much academic marketing research. In this approach, a theory is borrowed, refined, or developed and then tested empirically. In this challenging-the-boundaries article, the authors make a case for an empirics-first approach. “Empirics-first” refers to research that (1) is grounded in (originates from) a real-world marketing phenomenon, problem, or observation, (2) involves obtaining and analyzing data, and (3) produces valid marketing-relevant insights without necessarily developing or testing theory. The empirics-first approach is not antagonistic to theory but rather can serve as a stepping-stone to theory. The approach lends itself well to today’s data-rich environment, which can reveal novel research questions untethered to theory. The present article describes the underlying principles of an empirics-first approach, which consists of exploring a domain purposefully without preconceptions. Using a rich set of published examples, the authors offer guidance on how to implement empirics-first research and how it can lead to valuable knowledge development. Advice is also offered to scholars on how to report empirics-first research and to reviewers and to editorial teams on how to evaluate it. The ultimate objective is to pave a way for the empirics-first approach to enter the mainstream of academic marketing research.

It is a common view among philosophers of science that theoretical virtues (also known as epistemic or cognitive values), such as simplicity and consistency, play an important role in scientific practice. In this paper, I set out to study the role that theoretical virtues play in scientific practice empirically. I apply the methods of data science, such as text mining and corpus analysis, to study large corpora of scientific texts in order to uncover patterns of usage. These patterns of usage, in turn, might shed some light on the role that theoretical virtues play in scientific practice. Overall, the results of this empirical study suggest that scientists invoke theoretical virtues explicitly, albeit rather infrequently, when they talk about models (less than 30%), theories (less than 20%), and hypotheses (less than 15%) in their published works. To the extent that they are mentioned in scientific publications, the results of this study suggest that accuracy, consistency, and simplicity are the theoretical virtues that scientists invoke more frequently than the other theoretical virtues tested in this study. Interestingly, however, depending on whether they talk about hypotheses, theories, or models, scientists may invoke one of those theoretical virtues more than the others.

Most existing studies on next point-of-interest (POI) recommendation assume that users deliver certain check-ins over individual POIs. In reality, we typically obtain uncertain check-ins due to the presence of collective POIs, which are gathering places of multiple individual POIs (e.g., shopping malls). On one hand, such uncertain check-ins over collective POIs hinder more accurate next POI recommendation for users due to the transition vanishing issue; on the other hand, the presence of collective POIs poses the challenge for businesses to select which collective POIs to locate in due to complicated competition and cooperation relations between businesses. As such, these collective POIs bring an unprecedented opportunity and necessity on recommendation for both users and businesses. Therefore, we propose novel solutions of location service beneficial for users-businesses. For users, we propose the STSP equipped with category- and location-aware encoders, to deliver more accurate next POI prediction by fusing rich context features. Regarding businesses, we explore their competition and cooperation relations from check-in records, based on which we derive the living environment (LE) of a business. Insight on site selection for businesses is provided by exploiting the LE, aiming to bring in more profits. Extensive empirical studies demonstrate the efficiency of our solutions.

Given the high uncertainty in R&D work, a key concern of R&D managers is to understand how to fail faster by managing time and cost efficiently among their R&D projects. This study investigates how using a variety of Six Sigma tools can differentially affect R&D project time and cost performance. Our data are collected from 35 R&D projects in an industrial R&D center, and a total of 30 different Six Sigma tools were found to be used in varying frequencies. We analyze this problem in two phases. First, by analyzing the frequencies of tool use in the 35 projects through factor analysis, we find the application of Six Sigma tools can be guided by two distinct patterns of tool use: design-oriented tools (DT) and non-design-oriented tools (non-DT). DT include quality function deployment and failure modes and effects analysis (FMEA) for defining and understanding of the focal problems, while non-DT include Pareto chart and design of experiment for generating and testing solutions for the defined problems. Second, based on the two categories of tool use, we further analyze the effects of the two patterns of tool use on project duration and project cost. Results show that using a variety of DT is associated with faster project completion and non-DT lower project cost. In addition, the use of Six Sigma tools (both DT and non-DT) is associated with shorter durations and higher costs for complex projects but lower costs and longer durations for less complex projects. That is, different modes of trade-ff between time and cost performance can occur when project complexity is considered as a moderating factor. Overall, our findings demonstrate new insights into how using a variety of problem-solving tools affects R&D project cost and duration simultaneously.

Previous literature addressed organizations’ adaptiveness to ever-changing business environments and investigated the concept of agility. However, extant agility research primarily covers the corporate and project levels and is typically located in the information systems and operations management fields. Relatively little quantitative research in innovation management literature exists, and those studies approached the concept solely from an outcome perspective (i.e., increased adaptiveness) instead of elucidating how organizations should organize themselves to be agile (i.e., a capability perspective). This article addresses these shortcomings and adopts a capability perspective since no empirical studies have examined agile R&D units’ organization (ARDO). Drawing on dynamic capabilities theory, we develop a measure of ARDO, conceptualizing it as a second-order construct, consisting of six dimensions: a culture of agile values, customer integration, autonomy, an iterative work method, cross-functional capabilities, and flat hierarchies. We validated the measurement by conducting three studies to ensure content, structural, and nomological validity. We applied structural equation modeling on a sample of 175 R&D managers and cross validated our findings on different hierarchical levels via a sample of 454 R&D employees. The results confirm the second-order nature of the ARDO measure and provide evidence of its positive relationship with front-end success. We advance scarce quantitative research on agility's neglected capability perspective and contribute to the innovation management field by facilitating further empirical research on agile R&D units’ antecedents and outcomes in the context of physical product development.

Technological innovation, a complex and multidimensional construct, refers to innovations associated with an organization's operations, such as the introduction of new/improved products or processes. Scholars and practitioners recognize the importance of technological innovation in a firm's ability to obtain a sustainable competitive advantage. However, the empirical findings on the overarching relationship between technological innovation and firm performance remain inconclusive. Thus, in this article, we attempt to reconcile the mixed empirical results on the relationship by employing a multivariate meta-analytic methodology. We find that technological innovation has a significant and positive impact on a firm's performance. Furthermore, we find empirical support for the moderating influence of cross-cultural and institutional differences on the relationship. We observed a better performance outcome for innovation occurring in nations with a lower propensity to avoid uncertainty and collectivistic attitudes. In contrast, performance suffers when technological innovation occurs in nations that have stronger patent protection.

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.

Managers continuously make decisions on investing in projects that aim to enhance organizational performance. Poor project investment decision making results in lost benefits (rejecting a good project) and inefficiency (approving a suboptimal project). Whereas decisions regarding what project proposals to approve heavily rely on their stated goals, such goals are frequently unrealistic. This article aims at enhancing project investment decision making by extending decision comprehensiveness theory to the project goal-setting context. In an initial survey study, we found empirical support that a comprehensive goal-setting process enhances decision quality under uncertainty. In a second longitudinal study, we found that comprehensive goal setting further enhances project investment decision making above and beyond other established goal-setting dimensions (e.g., SMART goals), and eventually improves project performance. Last, we found that extensive consultation with key project stakeholders is the most effective comprehensive goal-setting practice. Based on research and practice, we describe what should comprise an effective goal-setting consultation process. This article extends goal-setting theory by suggesting that in addition to established characteristics, the goal-setting process under uncertainty should also be comprehensive.

Mobile apps provide new opportunities to people with disabilities to act independently in the world. Motivated by this trend, researchers have conducted empirical studies by using the inaccessibility issue rate of each page (i.e., screen level) to represent the characteristics of mobile app accessibility. However, there still lacks an empirical investigation directly focusing on the issues themselves (i.e., issue level) to unveil more fine-grained findings, due to the lack of an effective issue detection method and a relatively comprehensive dataset of issues. To fill in this literature gap, we first propose an automated app page exploration tool, named Xbot, to facilitate app accessibility testing and automatically collect accessibility issues by leveraging the instrumentation technique and static program analysis. Owing to the relatively high activity coverage (around 80%) achieved by Xbot when exploring apps, Xbot achieves better performance on accessibility issue collection than existing testing tools such as Google Monkey. With Xbot, we are able to collect a relatively comprehensive accessibility issue dataset and finally collect 86,767 issues from 2,270 unique apps including both closed-source and open-source apps, based on which we further carry out an empirical study from the perspective of accessibility issues themselves to investigate novel characteristics of accessibility issues. Specifically, we extensively investigate these issues by checking 1) the overall severity of issues with multiple criteria, 2) the in-depth relation between issue types and app categories, GUI component types, 3) the frequent issue patterns quantitatively, and 4) the fixing status of accessibility issues.

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.

To cope with discontinuous change, recent research advocates to implement ambidexterity in which firms pursue exploitative innovation and exploratory innovation simultaneously. While previous studies on the determinants of organizational ambidexterity focused primarily on the organizational characteristics and the behavior of top management teams, a growing body of research calls attention to the role of the paradoxical cognitive frame of top managers in managing organizational ambidexterity. This article examines whether CEO ambivalence—a paradoxical cognitive frame that accepts and accommodates the presence of both threats and opportunities of a strategic issue such as a crisis—drives or hinders organizational ambidexterity. Moreover, although both exploitation and exploration require the deployment of technological capability, whether technological capability inhibits or facilitates managerial cognition to adapt to environmental change remains a puzzle to be solved. Based on the theory of paradoxical cognitive frame, this article predicts that CEO ambivalence follows a U-shaped effect on organizational ambidexterity while technological capability amplifies the curvilinear effect. A longitudinal survey among 260 CEOs of Chinese firms provides empirical support to the hypotheses. The research findings contain rich theoretical and managerial implications.

The purpose of this article is to identify and evaluate the limitations and emerging trends of Six Sigma from the perspectives of Six Sigma experts. The authors developed an online global survey and deployed the survey to 1250 Six Sigma experts of which 307 experts responded. The article finds integration of Six Sigma with Big Data to be the topmost among Asian, South American, and African experts, whereas European and North American experts felt Six Sigma in Small and Medium Sized Enterprises and Micro-enterprises would be very beneficial. The manufacturing sector experts nominated the topmost emerging trend as Six Sigma in Small and Medium Sized Enterprises and Micro-enterprises to be very challenging and will be rewarding if implemented properly. In the service sector, the topmost emerging trend was the integration of Six Sigma with Big Data. However, public sector experts felt variance reduction should not be the only goal of Six Sigma implementation. The that master black belts perceived Six Sigma in Small and Medium Sized Enterprises and Micro-enterprises would be advantageous, whereas Black and Green Belts perceived Integration of Six Sigma with Big Data to be topmost emerging trend.

With the economic globalization, the relationship among all countries has become more and more closed (this phenomenon is often called “global village”). The wealth also flows more freely than that of before in the world. Due to this, there is a better improvement in our everyday life. But it also raises many livelihood issues such as the wealth gap. At present, the wealth gap problem has become one of the biggest serious problems in each country, especially, in the developing countries. Because of this, quantities of scholars and economists spare no effects to explore the dynamic relationship between wealth gap and economic growth. In this, this paper sets China who is the biggest developing country in the world with bigger wealth gap as an example. The annual time series data from 1980 to 2016 is applied to analyze the dynamic operating mechanism between wealth gap and economic growth in China. All datum are sourced from the National Bureau of Statistics of the People’s Republic of China. Using the vector error correction model to conduct an empirical analysis, the dynamic operating mechanism between them will be fully understood. Then, the results of this empirical study will provide some ideas as reference for China’s government to settle the wealth gap.

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.

Vision systems applied to drones, automatic vehicles, and robots have become an increasingly popular sensing method. However, vision sensors that make up these systems are vulnerable to malicious input attacks, which can lead to serious consequences. Prior work on attacking cameras of automatic vehicles shows that lasers can cause failure of camera-based functionalities, but it lacks analysis of the results and does not conduct experiments in the actual scenarios.In this paper, a laser-based attack on cameras and binocular vision sensors of drones is presented. First, we propose a threat model that describes how an adversary attacks the drone then perform feasibility analysis of the attack from theory and practice. Next, we design multi-variable experiments in the lab to systematically study the effectiveness of the attack, and further analyze how each variable affects the results. To get intuitive and fine-grained results, multidimensional image similarity is used to measure the effects. In particular, experiments in the actual scenarios are carried out, and results show that the attack can make obstacle avoidance, target recognition and tracking completely failed. Finally, lightweight countermeasures based on hardware and software are proposed to improve sensor resilience against the attack.

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 Behavioral Theory of the Firm suggests that performance below aspirations triggers problemistic search that can lead to risk taking. This prediction has received empirical support from most studies on the topic. However, this literature has typically focused on the internal determinants of firm search and risk-taking behavior and given little attention to the influences of social networks in which firms are embedded. To this end, we incorporate the network embeddedness perspective regarding firms’ network positions and their roles in firm decision making. We suggest that a firm’s search behavior is jointly directed by its performance feedback and network positions. Specifically, network brokerage and centrality play important yet distinct roles in guiding firm search behavior by differentially shaping the direction of problemistic search: high brokerage directs problemistic search to high-risk solutions, whereas high centrality directs problemistic search to low-risk solutions. Our theoretical predictions receive general empirical support based on analyses using longitudinal data from the Chinese venture capital industry. Our approach incorporates the crucial role of network structures into the problemistic search model and works toward building a problemistic search theory of the embedded firm.

This essay argues for the importance of formalizing public engagement efforts around bioethics as something we might call "bioethics communication," and it outlines the Johns Hopkins Berman Institute of Bioethics' plans for engaging in this effort. Because science is complex and difficult to explain to nonexperts, the field of science communication has arisen to meet this need. The field involves both a practice and a subject of empirical research. Like science, bioethics is also complex and difficult to explain, which is why the world needs bioethics communication. The authors are engaged in a brand-new effort to establish the sort of public bioethics efforts that would constitute bioethics communication, through a program which they call the Dracopoulos-Bloomberg iDeas Lab. The authors invite colleagues to experiment and learn with them as they invest in the development of bioethics communicators and their products.

Abstract. Marketers are increasingly relying on covert advertising tactics to persuade consumers. This empirical study selected the context of an emerging market to examine the effectiveness of product placements. Using an experimental design, we captured the affective and behavioral responses of young Indian adults. The results indicated that young adults are positive about product placements. The mediating role of brand attitude change in the relationship between attitude toward product placements and purchase intentions is established. The study provides valuable insights into the impact of execution factors on viewers’ responses. Implications and directions for future research are discussed based on the results of this study.

As part of local governments’ mandate to regulate for the “health, safety, and general welfare” of their residents, many have included a parkland dedication exaction on new development in their sub-division regulations. The rules governing the magnitude of the dedication were established in 1994 by the U.S. Supreme Court in Dolan v City of Tigard. The Court ruled there must be “rough proportionality” between a dedication exaction and the projected new demand from a development. The ruling requires a local jurisdiction to be proactive in quantifying the justification for the magnitude of a dedication it imposes, but the Court offered no guidance on how the quantification should be done. This study’s two objectives were: (i) to investigate the extent to which cities’ ordinances comply with the Supreme Court’s ruling, and (ii) to identify best practices among cities’ ordinances relating to operationalizing the “rough proportionality” principle. Parkland dedication ordinances were analyzed from 73 Texas cities, supplemented by insights from those of 29 large cities outside Texas. In 65 of the Texas ordinances where “rough proportionality” comparisons could be made, the analyses found percentage under-dedications ranging from 9% to 1,250%. In defiance of the Court’s ruling, almost two-thirds of the ordinances showed no evidence of using an empirical quantitative method to establish “rough proportionality.” Many of these ordinances provided a service level ratio, but it appeared to be arbitrarily determined. These findings are especially egregious in Texas, since state law requires that the quantification of “rough proportionality” be certified as being appropriate by a professional engineer. Three models of best practice that used empirical methods to derive rough proportionality and met the Supreme Court guidelines are identified, described, and illustrated. Under-dedication often reflects the reluctance of elected officials to antagonize the development community. Thus, four strategies are offered to facilitate their efforts to impose a substantive exaction that relieves the burden on taxpayers, while demonstrating sensitivity to any protests arising from members of the development community.

Real-world infrastructures modeled as complex networks have been popular topics in recent decades. As these infrastructures develop, becoming critical to support millions of people, their resilience in the face of disasters and attacks becomes an increasing concern to the engineers and stakeholders. Using a graph theory and complex network approach, we conducted an empirical study on the resilience of five metro, or subway systems selected globally. A multi-phase resilience framework was implemented from the resilience cycle concept, including preparedness, robustness, recovery, and adaptation. It also incorporated the historical flow data into the resilience analysis to account for user behavior impacts. The paper demonstrates a systematic comparison of five metro networks and explores trends in each resilience phase. Generally, diverse performances were observed among the five metro networks, with the Singapore metro showing the best resilience. It is robust to both random disruption and targeted attacks. Analysis suggests its advantage may come from both its moderate heterogeneous topology and the flow pattern, exhibiting the smallest average travel distance. Besides, the result highlights the significance of commuters' relocation behavior between neighboring stations in the recovery stage. For the adaptation from a long-term perspective, we developed a screening tool to investigate the impact of the network expansion on the existing part of network by mapping its relationship with the characteristics of the newly connected nodes. In general, the observation of the networks' different behaviors in flow-weighted analysis symbolizes that flow is necessary for consideration in the resilience design of real-world transportation systems.

Following the highly pervasive and effective use of agile methods at the team level, many software organisations now wish to replicate this success at the organisational level, adopting large-scale agile methods such as SAFe, Scrum-at-Scale, and others. However, this has proven significantly challenging. An analysis of the extant literature reveals a disparate set of studies across each individual method, with no cross-method comparison based on empirical evidence. This systematic literature review compares the main large-scale agile methods, namely SAFe, LeSS, Scrum-at-Scale, DAD, and the Spotify model. It is the first study to analyse and compare each of the method's principles, practices, tools, and metrics in a standardised manner. For each method, it presents not just the original method specifications but also all extensions and modifications to each method proposed by subsequent empirical research. It includes in this comparison not just commercial large-scale methods but also those that have been custom-built in organisations such as Nokia, Ericsson, and others. Based on the findings reported in this study, practitioners can make a more informed decision as to which commercial method or method component or, indeed, custom-built method is better suited to their needs. Our study reveals a number of theoretical and practical issues in the current literature, such as an emphasis on the practices of commercial frameworks at the expense of their underlying principles, or indeed any of the custom method. A set of challenges and success factors associated with the use of large-scale agile methods are identified. The study also identifies a number of research gaps to be addressed across methods.

Card fraud is an important issue and incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based approaches to detect fraudulent behavior from transaction records. But manually generating features needs domain knowledge and may lay behind the modus operandi of fraud, which means we need to automatically focus on the most relevant fraudulent behavior patterns in the online detection system. Therefore, in this work, we propose a spatial-temporal attention-based graph network (STAGN) for credit card fraud detection. In particular, we learn the temporal and location-based transaction graph features by a graph neural network first. Afterwards, we employ the spatial-temporal attention on top of learned tensor representations, which are then fed into a 3D convolution network. The attentional weights are jointly learned in an end-to-end manner with 3D convolution and detection networks. After that, we conduct extensive experiments on the real-word card transaction dataset. The result shows that STAGN performs better than other state-of-the-art baselines in both AUC and precision-recall curves. Moreover, we conduct empirical studies with domain experts on the proposed method for fraud detection and knowledge discovery; the result demonstrates its superiority in detecting suspicious transactions, mining spatial and temporal fraud hotspots, and uncover fraud patterns. The effectiveness of the proposed method in other user behavior-based tasks is also demonstrated. Finally, in order to tackle the challenges of big data, we integrate our proposed STAGN into the fraud detection system as the predictive model and present the implementation detail of each module in the system.

We report on a large-scale empirical study investigating the relevance of socio-technical congruence over key basic software quality metrics, namely, bugs and churn. In particular, we explore whether alignment or misalignment of social communication structures and technical dependencies in large software projects influences software quality. To this end, we have defined a quantitative and operational notion of socio-technical congruence, which we call socio-technical motif congruence (STMC). STMC is a measure of the degree to which developers working on the same file or on two related files, need to communicate. As socio-technical congruence is a complex and multi-faceted phenomenon, the interpretability of the results is one of our main concerns, so we have employed a careful mixed-methods statistical analysis. In particular, we provide analyses with similar techniques as employed by seminal work in the field to ensure comparability of our results with the existing body of work. The major result of our study, based on an analysis of 25 large open-source projects, is that STMC is not related to project quality measures -- software bugs and churn -- in any temporal scenario. That is, we find no statistical relationship between the alignment of developer tasks and developer communications on the one hand, and project outcomes on the other hand. We conclude that, wherefore congruence does matter as literature shows, then its measurable effect lies elsewhere., 29 pages, 21 figures. To appear in IEEE Transactions on Software Engineering

Innovation steps in complex products often go hand in hand with the selection of suppliers. The suppliers are involved in the development of innovative elements of products, supplementing the capabilities of companies. Strategic relationships are established with these suppliers, where the companies need them to be flexible and willing to adapt to changing demands. Flexibility is usually evaluated against a variety of indicators. However, a high score on flexibility does not guarantee cooperative suppliers. Companies also require power to get a supplier to be flexible. They need suppliers who are malleable to their changing needs that are an inevitable part of design processes. The empirical study at a leading European engine company reported in this paper shows that the relative power between companies and suppliers influences the responsiveness of suppliers to requests. This paper analyses malleability as a combination of flexibility and relative power. It unpacks power and flexibility into sets of measurable indicators and proposes a tool which assesses both the relative power between buyer and supplier and the supplier’s flexibility, to provide a malleability index, which indicates to what extent a supplier is suitable to work with.

The success of early-stage start-ups is strongly dependent on founders’ education. Yet, while scholars maintain that investors should consider founders' education an important signal of start-ups' prospects, mixed empirical findings suggest that more education does not always trigger positive investor reactions. Scholars have thus urged the development of new theoretical insights that may explain why and when education is beneficial to start-ups' emergence. Based on cognitive and social psychology literature, in this article, we evidence that different configurations of founders' education affect the ability of founding teams to obtain funding. The empirical support that we obtained from a sample of 1078 start-ups helps reconcile the contradictory findings of past research through a joint examination of two dimensions of founders' education that were previously only investigated separately—the level of education and the heterogeneity of educational backgrounds. Our focus on the group dynamics founding teams advances our understanding of the contribution of founders’ education onto early-stage start-ups' fund raising. In greater detail, educational level and educational heterogeneity positively affect the amount of funds raised, but their joint presence negatively moderates such a relationship. In doing so, we also provide start-uppers and policymakers with important information regarding how cofounders should group together when launching a start-up.

In recent years, Python has experienced explosive growth in adoption, particularly among open source projects. While Python's dynamically-typed nature provides developers with powerful programming abstractions, that same dynamic type system allows for type-related defects to accumulate in code bases. To aid in the early detection of type-related defects, type annotations were introduced into the Python ecosystem (i.e., PEP-484) and static type checkers like mypy have appeared on the market. While applying a type checker like mypy can in theory help to catch type-related defects before they impact users, little is known about the real impact of adopting a type checker to reveal defects in Python projects. In this paper, we study the extent to which Python projects benefit from such type checking features. For this purpose, we mine the issue tracking and version control repositories of 210 Python projects on GitHub. Inspired by the work of Gao et al. on type-related defects in JavaScript, we add type annotations to test whether detects an error that would have helped developers to avoid real defects. We observe that 15% of the defects could have been prevented by mypy. Moreover, we find that there is no significant difference between the experience level of developers committing type-related defects and the experience of developers committing defects that are not type-related. In addition, a manual analysis of the anti-patterns that most commonly lead to type-checking faults reveals that the redefinition of Python references, dynamic attribute initialization and incorrectly handled Null objects are the most common causes of type-related faults. Since our study is conducted on fixed public defects that have gone through code reviews and multiple test cycles, these results represent a lower bound on the benefits of adopting a type checker. Therefore, we recommend incorporating a static type checker like mypy into the development workflow, as not only will it prevent type-related defects but also mitigate certain anti-patterns during development.

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.

This study explores the evaluation of (im)politeness, which is made by a hearer (or a beneficiary). Although discursive researchers advocate the importance of the evaluation of (im)politeness made by a hearer in politeness research, empirical studies on the evaluation of (im)politeness are still limited. The non-linguistic aspect has not been much researched in previous politeness studies. This study tries to fill these gaps in politeness research, by focusing on evaluation of (im)politeness from the non-linguistic perspective. Among many other things, the evaluation of attentiveness (demonstrator’s preemptive response to a beneficiary’s verbal/non-verbal cues or situations surrounding a beneficiary and a demonstrator, which takes the form of offering) is focused on in this study, making cross-cultural (Japanese and American) and cross-generational (university students and their parents’ age groups) comparisons. The data were collected through a questionnaire, which consisted of six situations, and interviews. 298 people (Japanese university students, Japanese parents and American university students) served as the participants. The results of the questionnaire data show that there were significant differences among the participants in the evaluation of attentiveness in two situations, those of the interview data revealing that most participants evaluated attentiveness positively, except in two situations. This study contributes to further understanding of (im)politeness from the perspective of attentiveness with cross-cultural and cross-generational differences as well as similarities.

This article examines the results of a contrastive empirical study of conventional indirect requests in Peninsular and Uruguayan Spanish. The results reveal pragmatic similarities at the level of the linguistic encoding of utterances with both, Peninsular and Uruguayan Spanish speakers showing a negative correlation between (in)directness and social distance. The less familiar the interlocutors are with each other, the more likely it is for their requests to be realised indirectly. On the other hand, pragmatic differences were found in the tentativeness conveyed by the requests in these two language varieties. Uruguayan Spanish requests were more tentative than those in Peninsular Spanish. This tentativeness was achieved through a more frequent and more varied use of external modifications and a much higher incidence of internal modificating devices of the downgrading type.

For means of communication, persuasion is a natural and critical part of conveying a message. Data visualizations, being means of communication themselves, are used as rhetorical instruments, but how they persuade has yet to be fully understood. Based on George Campbell’s rhetorical theory, this paper presents the results of an empirical study testing the effectiveness of appeals to emotion through proximity techniques – the contextual framing of a visualization. The findings indicate that people feel greater interest towards a topic when the visualized data are more relevant to them, and that data representing events closer in time are more affecting.

As a software system evolves, its architecture tends to decay, leading to the occurrence of architectural elements that become resistant to maintenance or prone to defects. To address this problem, engineers can significantly benefit from determining which architectural elements will decay before that decay actually occurs. Forecasting decay allows engineers to take steps to prevent decay, such as focusing maintenance resources on the architectural elements most likely to decay. To that end, we construct novel models that predict the quality of an architectural element by utilizing multiple architectural views (both structural and semantic) and architectural metrics as features for prediction. We conduct an empirical study using our prediction models on 38 versions of five systems. Our findings show that we can predict low architectural quality, i.e., architectural decay, with high performanceeven for cases of decay that suddenly occur in an architectural module. We further report the factors that best predict architectural quality.