Fine-grained Perspective Research Articles

Fine-Grained Job Salary Benchmarking with a Nonparametric Dirichlet Process–Based Latent Factor Model

As a key decision-making process in compensation and benefits (C&B) in human resource management, job salary benchmarking (JSB) plays an indispensable role in attracting, motivating, and retaining talent. Whereas the existing research mainly focuses on revealing the essential impacts of personal and organizational characteristics and economic factors on labor costs (e.g., C&B), few studies target optimizing JSB from a practical, data-driven perspective. Traditional approaches suffer from issues that result from using small and sparse data as well as from the limitations of linear statistical models in practice. Furthermore, there are also important technical issues that need to be addressed in the small number of machine learning–based JSB approaches, such as “cold start” issues when considering a brand-new type of company or job or model interpretability issues. To this end, we propose to address the JSB problem with data-driven techniques from a fine-grained perspective by modeling large-scale, real-world online recruitment data. Specifically, we develop a nonparametric Dirichlet process–based latent factor model (NDP-JSB) to jointly model the latent representations of both company and job position and then apply the model to predict salaries based on company and position information. Our model strengthens the usage of data-driven approaches in JSB optimization by addressing the aforementioned issues in existing models. For evaluation, extensive experiments are conducted on two large-scale, real-world data sets. Our results validate the effectiveness of the NDP-JSB and demonstrate its strength in providing interpretable salary benchmarking to benefit complex decision-making processes in talent management.Summary of Contribution: This paper bridges the cutting-edge machine learning techniques to their implementation in a practical operation research problem in human resources. We focus on optimizing the salary-matching work to help the companies to seek reasonable salaries for their positions by proposing a data-driven approach to capture hidden patterns from user and company profiles. The contributions of this work reside in both operation research and computing. We (1) formulate the JSB optimization problem and (2) solve it by developing a data-driven method along with an effective algorithm optimization. Moreover, the proposed methodology has strengths in addressing the issues of data sparseness and result interpretability.

Image‐level dataset synthesis with an end‐to‐end trainable framework

Dataset synthesis via virtual engines like Unity is attracting much more attention in recent years due to its low cost at obtaining ground-truth labels. For this kind of work, virtual environments are constructed within the engine to mimic the real-world, either with great manual efforts or learning-based methods. The latter shows superiority over the former when the target real-world scenes are changeable, from which the attributes of environments can be automatically adjusted based on the distribution difference between the synthetic and real-world datasets. However, the non-differentiability of whole pipeline hinders the efficiency of attribute optimization. To this end, this paper proposes to simulate synthetic datasets from a fine-grained perspective, such that the system can be trained at an end-to-end manner. Specifically, it is converted into an image-level data synthesis problem, and designs a constraint using the content loss between two images. As the rendering process of virtual engine is mathematically unknown, which blocks the back propagation of the gradients, a generative model is trained to approximate the engine. As a result, the whole framework becomes fully differentiable and the attributes can be optimized efficiently by gradient descent. Experimental result shows the efficiency of our method in obtaining useful synthetic training datasets. Besides, it is found that the image-level method enables to learn the potential distribution of real-world data, which is hard to be achieved by existing methods. As far as we know, it is the first attempt to finish this task with a differentiable process.

Family leadership, family involvement and mutuality HRM practices in family SMEs

In the present study we argue that a more fine-grained perspective, taking into account different situations of family members’ involvement and the actual implementation of HRM practices and policies, is needed to provide an accurate picture of family firms as employers. Our study is carried out on a unique sample of 849 family firms in Italy and focuses on the conditions and consequences of adopting mutuality-promoting HRM practices in the family business context.

Entrepreneurial bricolage with users and implications for firm performance

The entrepreneurial bricolage perspective offers a distinctive explanation for how entrepreneurs address resource constraints. Although entrepreneurial bricolage is commonly observed, its performance implication is far from clear. By noting the heterogeneity of individual bricolage behavior and shifting from a bundling perspective to a fine-grained perspective, our study introduces one type of bricolage behavior—open bricolage—and examines its performance outcome. Using data of software startups that build businesses on Steam, the dominant digital distribution platform for PC video games, we empirically studied the open bricolage behavior—opening a product to users for user involvement, in which the unconventional resource—the labor input by unpaid users—is activated. We found a positive effect of the opening-to-user-involvement behavior on firm market performance. This study contributes to the entrepreneurial bricolage perspective and the entrepreneurship literature at large.

From Generative Linguistics to Categorial Grammars: Overt Subjects in Control Infinitives

AbstractThe present thesis lies at the interface of logic and linguistics; its object of study are control sentences with overt pronouns in Romance languages (European and Brazilian Portuguese, Italian and Spanish). This is a topic that has received considerably more attention on the part of linguists, especially in recent years, than from logicians. Perhaps for this reason, much remains to be understood about these linguistic structures and their underlying logical properties. This thesis seeks to fill the lacunas in the literature or at least take steps in this direction by way of addressing a number of issues that have so far been under-explored. To this end, we put forward two key questions, one linguistic and the other logical. These are, respectively, (1) What is the syntactic status of the surface pronoun? and (2) What are the available mechanisms to reuse semantic resources in a contraction-free logical grammar? Accordingly, the thesis is divided into two parts: generative linguistics and categorial grammar. Part I starts by reviewing the recent discussion within the generative literature on infinitive clauses with overt subjects, paying detailed attention to the main accounts in the field. Part II does the same on the logical grammar front, addressing in particular the issues of control and of anaphoric pronouns. Ultimately, the leading accounts from both camps will be found wanting. The closing chapter of each of Part I and Part II will thus put forward alternative candidates, that we contend are more successful than their predecessors. More specifically, in Part I, we offer a linguistic account along the lines of Landau’s T/Agr theory of control. In Part II, we present two alternative categorial accounts: one based on Combinatory Categorial Grammar, the other on Type-Logical Grammar. Each of these accounts offers an improved, more fine-grained perspective on control infinitives featuring overt pronominal subjects. Finally, we include an Appendix in which our type-logical proposal is implemented in a categorial parser/theorem-prover.Abstract prepared by María Inés Corbalán.E-mail: inescorbalan@yahoo.com.arURL: http://repositorio.unicamp.br/jspui/handle/REPOSIP/331697

Understanding design-based learning context and the associated emotional experience

Educational settings are emotional places where students experience diverse emotions in relation to academic activities and their outcomes. Emotions, in turn, greatly influence students’ learning process and engagement. Research on emotions in Design-Based Learning (DBL) has so far been coarse-grained examining how students evaluate their overall feelings towards the DBL project. As yet, little is known regarding how specific DBL activities influence students’ emotional experience. Therefore, we report a three-month field study of a DBL project involving 30 middle school students (aged 13–14) addressing dual research purposes: (1) to faithfully reconstruct the manner and sequence of activities during DBL from a fine-grained perspective; and (2) to examine the relationship between these activities and students’ emotional experience. This study used a mixed research method and collected multiple data sources, including experience sampling surveys, classroom observations, and interviews. The research outcomes in this study are multiple. First, this paper reveals detailed inspection regarding the types of task students performed, the strategies of shifting, and executing tasks during the process student experienced. Second, this paper identifies specific types of activities that have a significant positive or negative relationship with students’ emotional experiences. Derived from reported empirical evidence in the present study, this paper furtherly proposes an Activity-and-Affect model of DBL. This model provides a fine-grained description of DBL activity as continuous along three dimensions: task (design thinking process, project management, social interaction) task strategy (single-tasking vs. multitasking), and collaboration strategy (individual or group). Our analysis highlights the variability in how different DBL activities can be associated with different emotions.

Informal institutions and absorptive capacity: A cross-country meta-analytic study

The informal institutions construct has not been well understood in prior research. We contribute to the informal institutions literature by developing a generalized two-by-two conceptualization of institutions that juxtapose the dimension of behavioral uncertainty versus environmental uncertainty and the dimension of informal institutions versus formal institutions. In particular, we theorize informal institutions based on the extent to which they reduce behavioral uncertainty or environmental uncertainty, as well as their alignment with the formal institutional environment with respect to the two types of uncertainty. Leveraging this fourfold typology, we shed new light on the importance of informal institutional conditions in theories of firms’ absorptive capacity and performance. Using a cross-country meta-analytical approach, we find that a firm’s absorptive capacity is more positively related to performance outcomes in countries with informal institutions that reduce behavioral uncertainty and environmental uncertainty. Furthermore, the misalignment between informal and formal institutions with respect to behavioral uncertainty and environmental uncertainty weakens the relationship between absorptive capacity and performance outcomes. Overall, by re-emphasizing the fundamental role of informal institutions in reducing behavioral and environmental uncertainties, our typology offers a fine-grained perspective to conceptualize informal institutions and constitutes an opportunity to advance theory in informal institutions and international business.

Deep spatial–temporal structure learning for rumor detection on Twitter

The widespread of rumors on social media, carrying unreal or even malicious information, brings negative effects on society and individuals, which makes the automatic detection of rumors become particularly important. Most of the previous studies focused on text mining using supervised models based on feature engineering or deep learning models. In recent years, another parallel line of works, which focuses on the spatial structure of message propagation, provides an alternative and promising solution. However, these existing methods in this parallel line largely overlooked the temporal structure information associated with the spatial structure in message propagation. Actually the addition of temporal structure information can make the message propagations be classified from the perspective of spatial–temporal structure, a more fine-grained perspective. Under these observations, this paper proposes a spatial–temporal structure neural network for rumor detection, termed as STS-NN, which treats the spatial structure and the temporal structure as a whole to model the message propagation. All the STS-NN units are parameter shared and consist of three components, including spatial capturer, temporal capturer and integrator, to capture the spatial–temporal information for the message propagation. The results show that our approach obtains better performance than baselines in both rumor classification and early detection.

Optimization of the Product–Service System Configuration Based on a Multilayer Network

Product–service systems (PSS) accelerate the transition of value creation patterns for manufacturing industries, from product design and production to the delivery of overall solution integrating products and services. Existing PSS configuration solutions provide customers with preferable product modules and service modules characterized by the module granularity. Every service module is essentially a whole service flow. However, the performance of the PSS configuration solution is greatly influenced by service details. In summary, this paper studied the configuration optimization of product-oriented PSS using a fine-grained perspective. A multilayer network composed of (i) a product layer, (ii) a service layer, and (iii) a resource layer was constructed to represent the elements (product parts, service activities, resources) and relationships in PSS. Service activities selection and resource allocation were considered jointly to construct the mathematical model of PSS configuration optimization, thus enabling the calculation of optimizing objectives (time, cost, and reliability) under constraints closer to the actual implementation. The importance degree of service activity was considered to improve the performance of service activities with higher importance. Corresponding algorithms were improved and applied for obtaining the optimal solutions. The case study in the automotive industry shows the various advantages of the proposed method.

Improving multi-label chest X-ray disease diagnosis by exploiting disease and health labels dependencies

The widely used ChestX-ray14 dataset addresses an important medical image classification problem and has the following caveats: 1) many lung pathologies are visually similar, 2) a variant of multiple diseases including lung cancer, tuberculosis, and pneumonia are present in a single scan at the same time, i.e. multiple labels. Existing literature uses state-of-the-art deep learning models being transfer learned where output neurons of the networks are trained for individual diseases to cater for multiple disease labels in each image. However, most of them don’t consider the label relationship explicitly between present and absent classes. In this work we have proposed a pair of novel error functions that can be employed for any deep learning model, Multi-label Softmax Loss (MSML) and Correlation Loss (CorLoss), to specifically address the properties of multiple labels and visually similar data. Moreover, we provide a fine-grained perspective into this problem and use bilinear pooling as an encoding scheme to increase discrimination of the model. The experiments are conducted on the ChestX-ray14 dataset. We first report improvements using our proposed loss with various backbone networks. After that, we extend our experiments to prove the rich disparity being learned by the model with our proposed losses, which can be fused with other models to improve the overall performances.

Driving Forces of Employees’ Entrepreneurial Intentions - Leadership Style and Organizational Structure

Research Question: The purpose of this paper is to explore how leadership style and organizational structure characteristics influence employees’ intentions to start their own entrepreneurial ventures, ideas or projects, within an organizational setting. Motivation: The main goal is to learn how to prevent innovativeness declining in a traditional organization and make internal environment friendly for entrepreneurial initiatives and for employees with propensity to develop new ideas, aiming to create sustainable competitive position. Idea: The paper explores how identified leadership variables, initiative, pioneer, proactive behaviour and ability to communicate vision affect employees’ entrepreneurial intentions. We also analyze the organizational structure impact, testing organic design, level of centralization and formalization. For measuring employees’ entrepreneurial intentions, we use a previously validated scale and measurements, innovation, risk propensity and autonomy. Data: The data are collected using a questionnaire on a random sample of 208 respondents employed in nineteen Serbian companies. Tools: A quantitative study methodology was designed and implemented, appropriate statistical methods performed, including correlation and linear regression analyses. Findings: We have found evidence that appropriate leadership style has a positive influence on employees' entrepreneurial intentions. More specifically, there is a positive correlation between the leader’s initiative, pioneer and proactive behaviour, and the employees’ intentions to start innovative entrepreneurial ventures. The employees’ willingness to act autonomously is affected by leader’s initiative and proactive attitude, but also by ability to clearly communicate the vision. The study also reveals that organic organizational structure, decentralization and low formalization have a positive influence on employees' entrepreneurial intentions. Contribution: The study contributes to a significant degree by filling gaps in knowledge base, revealing new perspectives about relevance of both leadership style and organizational structure for encouraging organizational entrepreneurship. The analysis also provides a more fine-grained perspective about characteristics of organizational design and improves understanding of the employees’ actions depending on authority delegation, procedures and overall flexibility of organizational structure. Practical implications represent guidelines for practitioners as to how to set up structure and adjust leadership style in order to foster entrepreneurship among employees.

Reinterpreting shopping in the Enlightenment: retail practices, consumer experiences, governance

ABSTRACTThis new special issue on consumption and shopping practices in the eighteenth-century, takes further steps in reinterpreting the Enlightenment period in north-western Europe. The five papers assembled here will take the reader in new and often exciting directions with a shared concern to uncover under-studied source material and a willingness to explore and thematize shopping and consumption from a fine-grained and detailed historical perspective. The commodity culture of the Enlightenment will be analyzed to discover how it was embodied, enacted and perceived in its own time and context. In what follows, the reader will be introduced to toy- and bookshops in Enlightenment England; the role of provincial fairs; the way illicit goods were instrumental in tying consumer and retailers together in late eighteenth century Sweden; and, finally, learn more about subsequent phases of regulation and deregulation of the Viennese food markets around 1800. The following articles make the important implicit claim that shopping cultures in the Enlightenment worked ‘differently’, according to other cultural value schemes, conventions and norms than our own. These often-hidden cultural contexts remain in dire need to be resuscitated by uncovering new sources, doing careful methodological analysis, and by creatively connecting new findings to already existing knowledge.

The Lexile Leap: Consequences for Foundational Skills and Accountability Achievement

Foundational skills are important to reading success. Additionally, U.S. Core Reading Standards recommend text complexity grade-bands extending from second- through twelfth-grade as a measure of reading growth. The idea means that students must increase their reading skills as they progress across grades and that text complexity offers a reasonable metric for gauging such growth. A challenge with the text-complexity bands is both their breadth and their large increase between third- and fourth-grade. We present a text-complexity heuristic mapping growth in foundational skills across the elementary grades that provides a more fine-grained perspective for tracking reading development. We next present empirical evidence supporting the heuristic, which shows that students who reach foundational skill criteria by the end of third-grade are much more likely to achieve end-of-year reading proficiency than peers who are less-than-proficient readers.

Teachers interacting with students: an important (and potentially overlooked) domain for academic development during times of impact

ABSTRACTThis article is situated in the context of an intensified discourse within which academic developers are being asked to provide evidence of impact, and argues that theoretical models currently used are imprecise and fail to capture the variation in outcomes from professional development activities. Through reference to previous research on how teachers' thinking can be described in relation to various thinking-zones, and how teachers notice and respond to signs of student learning, we suggest a more fine-grained perspective. Furthermore, we argue that improved models for design and evaluation of professional development might prove crucial for academic development as a profession.

Nonlinear dynamics model for social popularity prediction based on multivariate chaotic time series

In online social network, it is of great significance in the popularity perception of social topics. Aiming to deeply analyze the nonlinear dynamics mechanism in the propagation process of social topics, the chaotic characteristics of information dissemination are thoroughly explored in this paper, and Bayesian estimation theory is employed to integrate the cross-platform factors affecting the topic popularity. Firstly, the time series of topic popularity from different platforms are redefined. And Principal Component Analysis (PCA) method is introduced to quantize the redefined time series and receive the main components affecting popularity. Secondly, this paper focuses on specific topics and performs small-data method to calculate the maximum Lyapunov exponent, which can be found that there are chaotic characteristics in time series of topic popularity. Then, the main components affecting popularity are reconstructed based on the phase space reconstruction theory, and the chaotic attractor is recovered in the high-dimensional reconstructed phase space. Smoothly, the evolution regularities and properties of complex system are restored to perceive the propagating situation of topic popularity. At the same time, the novel and fused phase space is obtained by using the Bayesian estimation theory to optimally fuse multiple variables in the same high-dimensional space. Finally, RBF (Radial Basis Function) neural network is employed to predict the fused popularity due to its strong ability to approximate nonlinear functions. Experiments show that the prediction approach can not only merge topic popularity from multiple social platforms, but also can improve the prediction accuracy to some degree. In the meanwhile, the developing trend of topics can be perceived from a more fine-grained perspective.

Creativity enables sustainable development: Supplier engagement as a boundary condition for the positive effect on green innovation

Despite the increasing awareness of business and academic research toward creative thinking, the investigation of its uptake by industry is still limited. Supplier engagement attracts considerable interest as a means to improve green innovation. Previous research studies in an inter-firm relationship have addressed the impact of green innovation on sustainability performance. While some studies have concluded that creative thinking can lead to sustainable performance outcomes, others have questioned the positive influence of creative thinking on innovation. This study takes a top-up perspective on ways to stimulate creative thinking with supplier engagement. Relying on the social capital theory and its wider contextualization in relation with the creativity, the authors argue that supplier engagement levers for driving green innovation and facilitates the sustainable development. We aim to advance understanding of the role of creativity, along with how and why creativity is important for the development of green innovation practices for a sustainable future. This study provides a more finegrained perspective by developing a theoretical model. Contributions from this study include research propositions to help explain and predict why and how firm resources become more important when combined with the creative thinking. Creative thinking is a fast-growing topic among the global community for the way that it enables sustainable development initiatives. Creativity boosts environmental, social and economic performance by enabling the development of green products and processes. Creative thinking has become an important tool driving green innovation within organizations and plays a critical role in enhancing sustainability performance. This study provides novel insights into how to organize creative abilities for sustainable development and provide actionable research direction and policy implications for both practitioners and academicians.

AC-Net: Assessing the Consistency of Description and Permission in Android Apps

With Android applications (apps) becoming increasingly popular, there exist huge risks lurking in the app marketplaces as most malicious software attempt to collect users' private information without their awareness. Although these apps request users' authorization for permissions, the users can still face privacy leakage issues due to their limited knowledge in distinguishing permissions. Thus, accurate and automatic permission checking is necessary and important for users' privacy protection. According to previous studies, analyzing app descriptions is a helpful way to examine whether some permissions are required for apps. Different from those studies, we consider app permissions from a more fine-grained perspective and aim at predicting the multiple correspondent permissions to one sentence of app description. In this paper, we propose an end-to-end framework for assessing the consistency between descriptions and permissions, named Assessing Consistency based on neural Network (AC-Net). For evaluation, a new dataset involving the description-to-permission correspondences of 1415 popular Android apps was built. The experiments demonstrate that AC-Net significantly outperforms the state-of-the-art method by over 24.5% in accurately predicting permissions from descriptions.

Balancing Stakeholder Expectations in Sustainability Management: The Rinker Center Project at Stetson University

The Marshall and Vera Lea Rinker Welcome Center is a US$7.634 million budgeted new building that was opened in August 2016 to house various administrative and student-related functions at Stetson University in Florida. Al Allen, the University’s Vice President of Facilities Management, was the point person entrusted with not only completing the project on time and on budget but also ensuring that the building received external certification that validated its sustainability standards. Allen’s task was not an easy one because he had to address the often-conflicting demands of the project’s stakeholders, many of whom, although well meaning, had no knowledge of how the building’s architectural details related to sustainability. Freeman’s [1] idea of stakeholders affecting an organization’s actions come into play in this case. This paper examines Allen’s path as the project leader and the choices that he confronts via the lens of stakeholder theory as it impacts a sustainability initiative. As a result of engaging in the analysis of this case, the reader should have a fine-grained perspective on how organizations confront the trade-offs between the financial and the sustainability sides of a decision, particularly when these sides may not be completely convergent.

The career-horizon problem in capital investments for lone-founder and long-tenure acquirer CEOs in their final career stage

In this study, we apply organizational identification theory to enrich our knowledge of the career-horizon problem when CEOs are approaching retirement. The extant literature suggests that the closer a CEO is to retirement, the more likely she or he is to avoid long-term firm investments. Focusing on capital investments, we argue that the distinctive organizational identification with the firm of lone-founder CEOs and long-tenure acquirer CEOs can moderate the likelihood that the closer a CEO is to retirement, the more likely she or he is to avoid capital investments. We test and validate our hypotheses on a sample of CEOs in S&P 1500 non-financial firms between 1999 and 2010. This article contributes to the literature on CEO career horizons by providing a new and more fine-grained perspective on the important question of how different types of CEOs consider capital investments and the future of their firms as they approach retirement.

Making Sense of Adopted Children's Internal Reality Using Narrative Story Stem Techniques: A Mixed-Methods Synthesis.

Background: Extant research on adopted children has consistently shown that early adverse experiences confer vulnerability to myriad developmental problems, which may be mitigated by the “natural intervention” of adoption itself and/or by treatment efforts. Narrative Story Stems Techniques (NSSTs) have been used in research and clinical practice to assess adopted children's developmental profiles in middle childhood. However, no study to date has systematically reviewed this body of literature.Objectives: This paper presents a systematic review of research using NSSTs to make sense of adopted children's internal reality (i.e., perceptions, experiences, and representations), in terms of exploring theoretical perspectives as well as critically synthesizing findings and discussing implications.Methods: State-of-the-art PRISMA guidelines were followed throughout, resulting in the identification of 18 records, comprising six qualitative, 10 quantitative, and two mixed-methods primary papers, reporting on seven unique studies. All records were assessed with regard to methodological quality. Data were extracted and synthesized narratively using an integrated design for mixed-methods synthesis.Results: The findings suggest that, although NSST research with adopted children is still in its infancy, there is relatively robust evidence supporting the use of these techniques to assess and track developmental change in adopted children's attachment representations. In this regard, the non-verbal (aside from the verbal) approach to children's complex internal reality, as well as a more fine-grained (aside from a categorical or dimensional) perspective on children's NSST responses, are highlighted as particularly valuable in tailoring treatment to a particular child's needs and vulnerabilities. Moreover, several promising avenues for future research and clinical application of NSSTs, including the extension to affect-regulatory and mentalizing perspectives, may further our knowledge and understanding of, and thus treatment efforts toward, these often vulnerable children. However, these findings should be interpreted with caution, due to the limited number of studies characterized by considerable methodological heterogeneity.Conclusions: In light of the findings of the present review, we strongly advocate future studies using NSSTs in theoretically and empirically consistent ways, in order to gain a better understanding of adopted children's internal reality in terms of attachment representations, affect-regulatory strategies, and mentalizing processes, and to track changes therein.