[Meg O’Neill] 00:08 Hello and welcome to the Berkeley Technology Law Journal podcast. My name is Meg O’Neill and I am one of the editors of the podcast. Today we are excited to share with you a conversation between Berkeley Law LLM student Franco Dellafiori, and Professor Bertrall Ross. Professor ...

The post Berkeley Technology Law Journal Podcast: Will ChatGPT Tell Me How to Vote? Democracy & AI with Professor Bertrall Ross appeared first on Berkeley Technology Law Journal.

This paper proposes an efficient solution for personalised web directories recommendation using fast FCM+DQN. At first, web directory usage file obtained from given dataset is fed into the accretion matrix computation module, where visitor chain matrix, visitor chain binary matrix, directory chain matrix and directory chain binary matrix are formulated. In this, directory grouping is accomplished based on fast FCM and matching among query and group is conducted based on Kumar Hassebrook and Kulczynski similarity. The user preferred directory is restored at this stage and at last, personalised web directories are recommended to the visitors by means of DQN. The proposed approach has received superior results with respect to maximum accuracy of 0.910, minimum mean squared error (MSE) of 0.0206 and root mean squared error (RMSE) of 0.144. Although the system offered magnificent outcomes, it failed to order web directories in the form of highly, medium and low interested directories.

Mental illnesses are common among college students as well as their non-student peers, and the number and severity of these problems are increasing. It can be difficult to identify people suffering from mental illness and get the help they need early. So in this paper, the SqueezeR_MobileNet method is proposed. It performs feature fusion and early mental health prediction. Initially, outliers in the input data are detected and removed. After that, using missing data imputation and Z-score normalisation the pre-processing phase is executed. Next to this, for feature fusion, a combination of the Soergel metric and deep Kronecker network (DKN) is used. By utilising bootstrapping data augmentation is performed. Finally, early mental health prediction is done using SqueezeR_MobileNet, which is the incorporation of residual SqueezeNet and MobileNet. The devised approach has reached the highest specificity of 0.937, accuracy of 0.911 and sensitivity of 0.907.

This work demonstrates a hybrid deep learning (DL) model for lung cancer (LC) detection using CT images. Firstly, the input image is passed to the pre-processing stage, where the input image is filtered using a BF and the obtained filtered image is subjected to lung lobe segmentation, where segmentation is done using squeeze U-SegNet. Feature extraction is performed, where features including entropy with fuzzy local binary patterns (EFLBP), local optimal oriented pattern (LOOP), and grey level co-occurrence matrix (GLCM) features are mined. After completing the extracting of features, LC is detected utilising the hybrid efficient-ShuffleNet (HES-Net) method, wherein the HES-Net is established by the incorporation of EfficientNet and ShuffleNet. The presented HES-Net for LC detection is investigated for its performance concerning TNR, and TPR, and accuracy is established to have acquired values of 92.1%, 93.1%, and 91.3%.

An optimisation-based decision-making support is proposed in this study in the form of fuzzy-probabilistic programming, which can be used to solve integrated order allocation, production planning, and inventory management problems in fuzzy and probabilistic uncertain environments. The problem was modelled in an uncertain mathematical optimisation model with two objectives: maximising the expectation of production volume and minimising the expectation of total operational cost subject to demand and other constraints. The model belongs to fuzzy-probabilistic bi-objective integer linear programming, and the generalised reduced gradient method combined with the branch-and-bound algorithm was utilised to solve the derived model. Numerical simulations were performed to illustrate how the optimal decision was formulated. The results showed that the proposed decision-making support was successful in providing the optimal decision with the maximum expectation of the production volume and minimum expectation of the total operational cost. Therefore, the approach can be implemented by decision-makers in manufacturing companies.

This study addresses a critical chain project scheduling (CCPS) problem regarding the feeding buffer. The main contribution of this study lies in determining the critical chain when the feeding buffer is considered along with the project buffer, a less addressed issue in the critical chain literature. Using a mixed-integer nonlinear programming (MINLP) model, the critical chain of a project with no break-down and no overflow is found. Moreover, the impact of the feeding buffer on the criticality of activities is discussed. The problem is solved using the Lingo software package for validation in small-sized instances. Since the CCPS is known as an NP-hard problem, a genetic algorithm (GA) is also designed to solve large-scale instances. The algorithm's performance is confirmed using various project scheduling library test problems. Sensitivity analysis is implemented based on some crucial parameters, and the critical chain is analysed after conducting several experiments. It is shown how considering the feeding buffer makes different critical chains and how shortlisting activities and resources are optimally managed.

Shuoyan LIU,Chao LI,Yuxin LIU,Yanqiu WANG, Vol.E107-D, No.11, pp.1468-1471
Escalators are an indispensable facility in public places. While they can provide convenience to people, abnormal accidents can lead to serious consequences. Yolo is a function that detects human behavior in real time. However, the model exhibits low accuracy and a high miss rate for small targets. To this end, this paper proposes the Small Target High Performance YOLO (SH-YOLO) model to detect abnormal behavior in escalators. The SH-YOLO model first enhances the backbone network through attention mechanisms. Subsequently, a small target detection layer is incorporated in order to enhance detection of key points for small objects. Finally, the conv and the SPPF are replaced with a Region Dynamic Perception Depth Separable Conv (DR-DP-Conv) and Atrous Spatial Pyramid Pooling (ASPP), respectively. The experimental results demonstrate that the proposed model is capable of accurately and robustly detecting anomalies in the real-world escalator scene.
Publication Date: 2024/11/01

Congcong FANG,Yun JIN,Guanlin CHEN,Yunfan ZHANG,Shidang LI,Yong MA,Yue XIE, Vol.E107-D, No.11, pp.1463-1467
Currently, an increasing number of tasks in speech emotion recognition rely on the analysis of both speech and text features. However, there remains a paucity of research exploring the potential of leveraging large language models like GPT-3 to enhance emotion recognition. In this investigation, we harness the power of the GPT-3 model to extract semantic information from transcribed texts, generating text modal features with a dimensionality of 1536. Subsequently, we perform feature fusion, combining the 1536-dimensional text features with 1188-dimensional acoustic features to yield comprehensive multi-modal recognition outcomes. Our findings reveal that the proposed method achieves a weighted accuracy of 79.62% across the four emotion categories in IEMOCAP, underscoring the considerable enhancement in emotion recognition accuracy facilitated by integrating large language models.
Publication Date: 2024/11/01

Keitaro NAKASAI,Shin KOMEDA,Masateru TSUNODA,Masayuki KASHIMA, Vol.E107-D, No.11, pp.1444-1448
To automatically measure the mental workload of developers, existing studies have used biometric measures such as brain waves and the heart rate. However, developers are often required to equip certain devices when measuring them, and can therefore be physically burdened. In this study, we evaluated the feasibility of non-contact biometric measures based on the nasal skin temperature (NST). In the experiment, the proposed biometric measures were more accurate than non-biometric measures.
Publication Date: 2024/11/01

Nan WU,Xiaocong LAI,Mei CHEN,Ying PAN, Vol.E107-D, No.11, pp.1433-1443
With the development of the Semantic Web, an increasing number of researchers are utilizing ontology technology to construct domain ontology. Since there is no unified construction standard, ontology heterogeneity occurs. The ontology matching method can fuse heterogeneous ontologies, which realizes the interoperability between knowledge and associates to more relevant semantic information. In the case of differences between ontologies, how to reduce false matching and unsuccessful matching is a critical problem to be solved. Moreover, as the number of ontologies increases, the semantic relationship between ontologies becomes increasingly complex. Nevertheless, the current methods that solely find the similarity of names between concepts are no longer sufficient. Consequently, this paper proposes an ontology matching method based on semantic association. Accurate matching pairs are discovered by existing semantic knowledge, and then the potential semantic associations between concepts are mined according to the characteristics of the contextual structure. The matching method can better carry out matching work based on reliable knowledge. In addition, this paper introduces a probabilistic logic repair method, which can detect and repair the conflict of matching results, to enhance the availability and reliability of matching results. The experimental results show that the proposed method effectively improves the quality of matching between ontologies and saves time on repairing incorrect matching pairs. Besides, compared with the existing ontology matching systems, the proposed method has better stability.
Publication Date: 2024/11/01

Qinghua WU,Weitong LI, Vol.E107-D, No.11, pp.1422-1432
Multi-focus image fusion involves combining partially focused images of the same scene to create an all-in-focus image. Aiming at the problems of existing multi-focus image fusion algorithms that the benchmark image is difficult to obtain and the convolutional neural network focuses too much on the local region, a fusion algorithm that combines local and global feature encoding is proposed. Initially, we devise two self-supervised image reconstruction tasks and train an encoder-decoder network through multi-task learning. Subsequently, within the encoder, we merge the dense connection module with the PS-ViT module, enabling the network to utilize local and global information during feature extraction. Finally, to enhance the overall efficiency of the model, distinct loss functions are applied to each task. To preserve the more robust features from the original images, spatial frequency is employed during the fusion stage to obtain the feature map of the fused image. Experimental results demonstrate that, in comparison to twelve other prominent algorithms, our method exhibits good fusion performance in objective evaluation. Ten of the selected twelve evaluation metrics show an improvement of more than 0.28%. Additionally, it presents superior visual effects subjectively.
Publication Date: 2024/11/01

Yuan LI,Tingting HU,Ryuji FUCHIKAMI,Takeshi IKENAGA, Vol.E107-D, No.11, pp.1396-1407
1 millisecond (1-ms) vision systems are gaining increasing attention in diverse fields like factory automation and robotics, as the ultra-low delay ensures seamless and timely responses. Superpixel segmentation is a pivotal preprocessing to reduce the number of image primitives for subsequent processing. Recently, there has been a growing emphasis on leveraging deep network-based algorithms to pursue superior performance and better integration into other deep network tasks. Superpixel Sampling Network (SSN) employs a deep network for feature generation and employs differentiable SLIC for superpixel generation. SSN achieves high performance with a small number of parameters. However, implementing SSN on FPGAs for ultra-low delay faces challenges due to the final layer’s aggregation of intermediate results. To address this limitation, this paper proposes an aggregated to pipelined structure for FPGA implementation. The final layer is decomposed into individual final layers for each intermediate result. This architectural adjustment eliminates the need for memory to store intermediate results. Concurrently, the proposed structure leverages decomposed layers to facilitate a pipelined structure with pixel streaming input to achieve ultra-low latency. To cooperate with the pipelined structure, layer-partitioned memory architecture is proposed. Each final layer has dedicated memory for storing superpixel center information, allowing values to be read and calculated from memory without conflicts. Calculation results of each final layer are accumulated, and the result of each pixel is obtained as the stream reaches the last layer. Evaluation results demonstrate that boundary recall and under-segmentation error remain comparable to SSN, with an average label consistency improvement of 0.035 over SSN. From a hardware performance perspective, the proposed system processes 1000 FPS images with a delay of 0.947 ms/frame.
Publication Date: 2024/11/01

Zhigang WU,Yaohui ZHU, Vol.E107-D, No.11, pp.1385-1395
This article focuses on improving the BiSeNet v2 bilateral branch image segmentation network structure, enhancing its learning ability for spatial details and overall image segmentation accuracy. A modified network called “BiconvNet” is proposed. Firstly, to extract shallow spatial details more effectively, a parallel concatenated strip and dilated (PCSD) convolution module is proposed and used to extract local features and surrounding contextual features in the detail branch. Continuing on, the semantic branch is reconstructed using the lightweight capability of depth separable convolution and high performance of ConvNet, in order to enable more efficient learning of deep advanced semantic features. Finally, fine-tuning is performed on the bilateral guidance aggregation layer of BiSeNet v2, enabling better fusion of the feature maps output by the detail branch and semantic branch. The experimental part discusses the contribution of stripe convolution and different sizes of empty convolution to image segmentation accuracy, and compares them with common convolutions such as Conv2d convolution, CG convolution and CCA convolution. The experiment proves that the PCSD convolution module proposed in this paper has the highest segmentation accuracy in all categories of the Cityscapes dataset compared with common convolutions. BiConvNet achieved a 9.39% accuracy improvement over the BiSeNet v2 network, with only a slight increase of 1.18M in model parameters. A mIoU accuracy of 68.75% was achieved on the validation set. Furthermore, through comparative experiments with commonly used autonomous driving image segmentation algorithms in recent years, BiConvNet demonstrates strong competitive advantages in segmentation accuracy on the Cityscapes and BDD100K datasets.
Publication Date: 2024/11/01

One of the main tasks in tax administration is debt management. The main goal of this function is tax due collection. Statements are processed in order to select strategies to use in the debt management process to optimise the debt collection process. This work proposes to carry out a data mining process to predict debts of taxpayers with high probability of non-payment. The data mining process identifies high-risk debts using a survival analysis on a dataset from a tax administration. Three groups of tax debtors with similar payment behaviour were identified and a success rate of up to 90% was reached in estimating the payment time of taxpayers. The concordance index (C-index) was used to determine the performance of the constructed model. The highest prediction rate reached was 90.37% corresponding to the third group.

Product innovation has become a crucial part of the sustainability of the coconut agro-industry in Indonesia, covering upstream and downstream sides. To overcome this challenge, it is necessary to create several model stages using a hybrid method that combines machine learning based on multiple criteria decision making and mass balance analysis. The research case study was conducted in Tembilahan district, Riau province, Indonesia, one of the primary coconut producers in Indonesia. The analysis results showed that potential products for domestic customers included coconut milk, coconut cooking oil, coconut chips, coconut jelly, coconut sugar, and virgin coconut oil. Furthermore, considering the experts, the most potential product to be developed was coconut sugar with a weight of 0.26. Prediction of coconut sugar demand reached 13,996,607 tons/year, requiring coconut sap as a raw material up to 97,976,249.

Research growth in organisational behaviour research, has increased the importance of paying attention to anti-citizenship behaviours. The current research with the aim of quantitative combination, has examined the results of research in effect of underlying factors of organisational anti-citizenship behaviours using meta-analysis method and CMA2 software and 55 articles during the time period of 2000-2020. The results showed a positive significant link between underlying factors of organisational anti-citizenship behaviours and occurrence of these behaviours and this influence was 0.389, 0.338, 0514 and 0.498 (structural, organisational, managerial, employment and professional and socio-economic and cultural factors). The level of connection found relating to each four occurrences is '68 links, 49 links, 93 links and 71 links'. Findings indicate that minute attention has been paid to organisational anti-citizenship behaviours, especially to job and professional factors in research works. Research should be conducted to control and manage these behaviours more purposefully in organisations.

In Italy, the reimbursement provided to healthcare organisations for medical and surgical procedures is based on the diagnosis related group weight (DRGW), which is an increasing function of the complexity of the procedures. This makes the reimbursement an upper unlimited function. This model does not include the relation of the volume with the complexity. The paper proposes a mathematical model for the optimisation of the reimbursement by determining the optimal mix of volume/procedure, considering the relation volume/complexity and DRGW/complexity. The decreasing, linear, and increasing returns to scale have been defined, and the optimal solution found. The comparison of the model with the traditional approach shows that the proposed model helps the healthcare system to discern the quantity of the reimbursement to provide to health organisations, while the traditional approach, neglecting the relation between the volume and the complexity, can result in an overestimation of the reimbursement.

The objective of this study was to explore through the Model of Theory of Planned Behaviour the most important variables that influence the practice of physical and sports activity at home supported by virtual training in the context of the COVID-19 pandemic. A cross-study was proposed between countries from three continents, distributing the questionnaire in Spain (Europe), Pakistan (Asia), and Colombia (South America) to ensure a comprehensive study. The methodology of structural equations using partial least squares was used. The empirical exploratory study supported the hypotheses proposed, with the most important result that confinement due to the COVID-19 pandemic has been a factor causing the practice of physical and sports activity at home. This is one of the first studies to examine sports practice at home and the new context of sports practice that has generated disruptive technologies and the global crisis of the COVID-19 pandemic.

Recent innovations in additive manufacturing (AM) have proven its efficacy for not only the manufacturing industry but also the healthcare industry. Researchers from Cal Poly, San Luis Obispo, and California State University Long Beach are developing a model that will determine the optimal locations for additive manufacturing hubs that can effectively serve both the manufacturing and healthcare industries. This paper will focus on providing an overview of the healthcare industry's unique needs for an AM hub and summarise the specific inputs for the model. The methods used to gather information include extensive literature research on current practices of AM models in healthcare and an inclusive survey of healthcare practitioners. This includes findings on AM's use for surgical planning and training models, the workflow to generate them, sourcing methods, and the AM techniques and materials used. This paper seeks to utilise the information gathered through literature research and surveys to provide guidance for the initial development of an AM hub location model that locates optimal service locations.

Collaboration between various stakeholders is crucial for healthcare digitalisation and eHealth utilisation. Given that valuable outcomes can emerge from collaborative interactions among multiple stakeholders, exploring a quadruple helix (QH) approach to collaboration may be fruitful in involving the public sector, business, citizens, and academia. Therefore, this study aimed to explore stakeholder views on eHealth collaboration from a QH perspective using the grounded theory methodology. First, an inductive qualitative study involving all stakeholders in the QH was conducted. Subsequently, the findings were related to the actor-resource-activity (ARA) model of business relationships. The results emphasise the role of considering diverse perspectives on collaboration because digitalisation and eHealth require teamwork to benefit the end users within various settings. A model depicting the various aspects of the ARA model related to digitalisation in a healthcare QH setting is presented.

Background and Objective: In response to the challenges of poor mapping outcomes and susceptibility to obstacles encountered by indoor mobile vehicles relying solely on pure cameras or pure LiDAR during their movements, this paper proposes an obstacle avoidance method for indoor mobile vehicles that integrates image and LiDAR data, thus achieving obstacle avoidance for mobile vehicles. Materials and Methods: This method combines data from a depth camera and LiDAR, employing the Gmapping SLAM algorithm for environmental mapping, along with the A* algorithm and TEB algorithm for local path planning. In addition, this approach incorporates gesture functionality, which can be used to control the vehicle in certain special scenarios where “pseudo-obstacles” exist. The method utilizes the YOLO V3 algorithm for gesture recognition. Results: This paper merges the maps generated by the depth camera and LiDAR, resulting in a three-dimensional map that is more enriched and better aligned with real-world conditions. Combined with the A* algorithm and TEB algorithm, an optimal route is planned, enabling the mobile vehicles to effectively obtain obstacle information and thus achieve obstacle avoidance. Additionally, the introduced gesture recognition feature, which has been validated, also effectively controls the forward and backward movements of the mobile vehicles, facilitating obstacle avoidance. Conclusion: The experimental platform for the mobile vehicles, which integrates depth camera and LiDAR, built in this study has been validated for real-time obstacle avoidance through path planning in indoor environments. The introduced gesture recognition also effectively enables obstacle avoidance for the mobile vehicles.

In my knowledge graph class yesterday we talked about the SPARQL query language and I illustrated it with DBpedia queries, including an example getting data about the movie Double Indemnity. I had brought a google assistant device and used it to compare its answers to those from DBpedia. When I asked the Google assistant “Who […]

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Ph.D. Dissertation Defense Modeling and Extracting Information about Cybersecurity Events from Text Taneeya Satyapanich 9:30-11:30 Monday, 18 November, 2019, ITE346? People now rely on the Internet to carry out much of their daily activities such as banking, ordering food, and socializing with their family and friends. The technology facilitates our lives, but also comes with […]

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Results using the reinforcement learning technique on two SAT benchmarks using a D-Wave 2000Q quantum processor showed significantly better solutions with fewer samples compared to the best-known quantum annealing techniques.

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This paper how to apply dynamic topic models to a set of cybersecurity documents to understand how the concepts found in them are changing over time.

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A new paper describing a system integrating a GDPR Ontology with blockchain to support checking data operations for compliance.

The post paper: Automating GDPR Compliance using Policy Integrated Blockchain appeared first on UMBC ebiquity.

Basics of the Adtech Ecosystem This guide delves into the intricacies of the adtech ecosystem, an elaborate mesh of platforms and technologies designed to facilitate and enhance the purchase and sale of digital advertising. Within this system, crucial elements such as ad servers, DSPs (Demand Side Platforms), SSPs (Supply Side Platforms), and ad [...]

Best Programmatic Advertising Strategies Looking to craft a successful programmatic advertising strategy? This guide will outline key steps like setting goals, identifying your audience, and leveraging technology to boost your campaigns. Key Takeaways Programmatic advertising automates the ad buying process using machine learning and data analytics, significantly increasing efficiency and enabling precise targeting. [...]

Amazon Ads Dashboard Overview Streamline your advertising strategy with the Amazon Advertising Dashboard. Learn how to monitor vital campaign metrics, create customized reports for deeper insights, and refine your tactics for maximum effectiveness. This article guides you through the dashboard's powerful tools, including customizable data widgets, advanced analytics, automated reporting, and seamless integration [...]

Best Connected TV Advertising Companies Curious about connected TV advertising companies? This article covers the top companies, their key features, and tips for choosing the best fit for your ad campaigns. Key Takeaways Connected TV (CTV) advertising allows personalized, data-driven ad delivery via internet-connected devices, significantly improving audience targeting compared to traditional TV. [...]

What is Biddable Media? Biddable media refers to digital advertising space purchased through real-time bidding. It’s a flexible and efficient way to ensure your ads reach the right audience. This article will cover the basics of biddable media and its benefits for your marketing strategy. Key Takeaways Biddable media operates through a real-time [...]

Social exchange theory is a broad theory that has been used to explain trust as an outcome of various exchange relationships, and research commonly presumes trust exists between exchange partners that have prior relationships. In this paper, we contribute to social exchange theory by isolating the trust outcomes of interorganizational exchanges from other outcomes emphasized by learning and knowledge-based perspectives, and by specifying important boundary conditions for the emergence of trust in interorganizational exchanges. We make such a theoretical contribution within the domain of strategic alliances by investigating the effects of previous alliance agreements, or prior ties, between the partnering firms. We find that prior ties generally lead to learning about a partner's anticipated behavioral patterns, which helps a firm predict when self-interested behavior may occur and know how to interact with the partner during the coordination and execution of the alliance tasks. By contrast, it is evident that the kind of trust emphasized in social exchange theory is not generally rooted in prior ties and only emerges from prior relationships under certain conditions. We discuss the implications of these findings for research on social exchange theory and for delineating the theory's domain of applicability.

Calls for greater diversity in management research, education and practice have increased in recent years, driven by a sense of fairness and ethical responsibility, but also because research shows that greater diversity of inputs into management processes can lead to greater innovation. But how can greater diversity of thought be encouraged when educating management students, beyond the advocacy of affirmative action and relating the research on the link between multiplicity and creativity? One way is to think again about how we introduce the subject. Introductory textbooks often begin by relaying the history of management. What is presented is a very limited mono-cultural and linear view of how management emerged. This article highlights the limits this view outlines for initiates in contrast to the histories of other comparable fields (medicine and architecture), and discusses how a wider, deeper and more engaged understanding of history can foster thinking differently.

Drawing on human capital theory, strategy scholars have emphasized firm-specific human capital as a source of sustained competitive advantage. In this study, we begin to unpack the micro-foundations of firm-specific human capital by theoretically and empirically exploring when employees perceive their skills to be firm-specific. We first develop theoretical arguments and hypotheses based on the extant strategy literature, which implicitly assumes information efficiency and unbiased perceptions of firm-specificity. We then relax these assumptions and develop alternative hypotheses rooted in the cognitive psychology literature, which highlights biases in human judgment. We test our hypotheses using two data sources from Korea and the United States. Surprisingly, our results support the hypotheses based on cognitive bias - a stark contrast to the expectations embedded within the strategy literature. Specifically, we find organizational commitment and, to some extent, tenure are negatively related to employee perceptions of the firm-specificity. We also find that employer provided on-the-job training was unrelated to perceived firm-specificity. These findings suggest that firm-specific human capital, as perceived by employees, may drive behavior in ways not anticipated by existing theory - for example, with respect to investments in skills or turnover decisions. This, in turn, may challenge the assumed relationship between firm-specific human capital and sustained competitive advantage. More broadly, our findings may suggest a need to reconsider other theories, such as transaction cost economics, that draw heavily on the notion of firm-specificity and implicitly assume widely shared and unbiased perceptions.

'What's going on?' Within the context of our critically-informed teaching practice, we see moments of deep learning and reflexivity in classroom discussions and assessments. Yet, these moments of criticality are interspersed with surface learning and reflection. We draw on dichotomous, linear developmental, and messy explanations of learning processes to empirically explore the learning journeys of 20 international Chinese and 42 domestic New Zealand students. We find contradictions within our own data, and between our findings and the extant literature. We conclude that expressions of surface learning and reflection are considerably more complex than they first appear. Moreover, developing critical reflexivity is a far more subtle, messy, and emotional experience than previously understood. We present the theoretical and pedagogical significance of these findings when we consider the implications for the learning process and the practice of management education.

Do firms learn from their failed innovation attempts? Answering this question is important because failure is an integral part of exploratory learning. In this study, we explore whether and under what circumstances firms learn from their small failures in experimentation. Building on organizational learning literature, we examine the conditions under which prior failures influence firms' R&D output amount and quality. An empirical analysis of voluntary patent expirations (i.e., patents that firms give up by not paying renewal fees) in 97 pharmaceutical firms between 1980 and 2002 shows that the number, importance, and timing of small failures are associated with a decrease in R&D output (patent count) but an increase in the quality of the R&D output (forward citations to patents). Exploratory interviews suggest that the results are driven by a multi-level learning process from failures in pharmaceutical R&D. The findings contribute to the organizational learning literature by providing a nuanced view of learning from failures in experimentation.

Power in business schools is ubiquitous. We develop individuals for powerfull positions. Yet, the way we deal with power is limited by our utilitarian focus, avoiding the visceral nature of power. In relation to this we address two questions business schools don't ask: what is the experiential nature of power? How are we teaching power? We use experiential, aesthetic developments on power in the social sciences to critique the rational-utilitarian stance on power found in business schools, drawing on the work of Dewey and French philosopher Levinas to treat power as a lived phenomenon. We overview and critique approaches to teaching power in business curricula informed by our own research on Executive MBA students learning through choral conducting. Taking an appreciative-positive stance, this research showed students developing new, non-rational, non-utilitarian understandings of power. They developed nuanced learning on the feeling, relationality and responsibility of exercising power. Coming out of this we argue for more experiential and reflexive learning methods to be applied to the phenomena of power. Finally we shine a reflexive light on ourselves and our 'power to profess', suggesting ways we can change our own practice to better prepare our students for the power they wield.

N/A -- no abstracts in FTEs I believe

Theories of status rarely address unearned status gain—an unexpected and unsolicited increase in relative standing, prestige or worth, attained not through individual effort or achievement, but from a shift in organizationally valued characteristics. We build theory about unearned status gain drawing from a qualitative study of 90 U.S.-based employees of a Japanese organization following a company-wide English language mandate. These native English-speaking employees believed that the mandate elevated their worth in the organization, a status gain they attributed to chance, hence deeming it unearned. They also reported a heightened sense of belonging, optimism about career advancement, and access to expanded networks. Yet among those who interacted regularly with Japanese counterparts, narratives also revealed discomfort, which manifested in at least two ways. These informants engaged in "status rationalization," emphasizing the benefits Japanese employees might obtain by learning English, and prevaricated on whether the change was temporary or durable, a process we call "status stability appraisal." The fact that these narratives were present only among those working closely with Japanese employees highlights intergroup contact as a factor in shaping the unearned status gain experience. Supplemental analysis of data gathered from 66 Japanese employees provided the broader organizational context and the nonnative speakers' perspective of the language shift. These findings expand our overall understanding of status dynamics in organizations, and show how status gains can yield both positive and negative outcomes.

Building on prior work showing that abusive supervision is a reaction to subordinates' poor performance, we develop a self-control framework to outline when and why supervisors abuse poor performing subordinates. In particular, we argue poor performing subordinates instill in supervisors a sense of hostility towards the subordinate, which in turn leads to engaging in abusive supervision. Within this self-control framework, poor performance is more likely to lead to abusive supervision when (a) the magnitude of the hostility experienced is higher (e.g., for those with a hostile attribution bias), or (b) the translation of hostility into abusive supervision is unconstrained (e.g., for those who are low in trait mindfulness). In two experimental studies with full-time supervisors where we manipulated the independent variable (Study 1) and the mediator (Study 2), and in a multi-wave and multi-source field study with data collected from supervisor-subordinate teams (50 supervisors and 206 subordinates) at two time points (Study 3), we found overall support for our predictions. Implications for how to reduce the occurrence of abusive supervision in the workplace are discussed.

If partnering with local firms is an intuitive strategy with which to mitigate uncertainty in foreign ventures, then why don't organizations always partner with local firms, especially in uncertain settings? We address this question by unbundling the effects of uncertainty in foreign ventures at the venture and country levels. We contend that, while both levels increase the need for partnering with local firms in foreign ventures, country-level uncertainty increases the difficulty of partnering with local firms and decreases the likelihood of such partnerships. We also posit that experiential learning helps firms manage the two types of uncertainty, and thereby reduces the need for partnering—yet, experience in the host country makes partnering more feasible and increases the likelihood of such partnerships. To test our hypotheses, we conceptualize the decision to partner with a local firm in a foreign venture as a multilayered decision, and model it accordingly. Using a global sample of venture capital investments made between 1984 and 2011, we find support for the distinct effects of venture- and country-level uncertainty as well as for corresponding levels of experiential learning. These findings have implications for the literature on cross-border venture capital investment and international business in general.

While many studies explain how social science theories shape social reality, few reflect critically on how such theories should shape social reality. Drawing on a new conception of social welfare and focusing on financial regulation, we assess the performative effects of theories on public policy. We delineate how research that focuses narrowly on questions of efficiency and stability reinforces today's technocratic financial regulation that undermines social welfare. As a remedy, we outline how future management research can tackle questions of social justice and thereby promote an inclusive approach to financial regulation that better serves social welfare.

Firms play a crucial role in furthering social welfare through their ability to foster stakeholders' contributions to joint value creation, i.e., value creation that involves a public-good dilemma due to high task and outcome interdependence - leading to what economists have labeled the 'team production problem'. We build on relational models theory to examine how individual stakeholders' contributions to joint value creation are shaped by stakeholders' mental representations of their relationships with the other participants in value creation, and how these mental representations are affected by the perceived behavior of the firm. Stakeholder theory typically contrasts a broadly-defined 'relational' approach to stakeholder management with a 'transactional' approach based on the price mechanism - and has argued that the former is more likely to contribute to social welfare than the latter. Our theory supports this prediction for joint value creation, but also implies that the dichotomy on which it is based is too coarse-grained: there are three distinct ways to trigger higher contributions to joint value creation than through a 'transactional' approach. Our theory also helps explain the tendency for firms and their stakeholders to converge on 'transactional' relationships, despite their relative inefficiency in the context of joint value creation.

Most research on creativity describes it as a net positive: producing new products for the organization and satisfaction and positive affect for creative workers. However, a host of anecdotal and historical evidence suggests that creative work can have deleterious consequences for relationships. This raises the question: how does creativity at work impact relationships at home? Relying on work-family conflict and resource allocation theory as conceptual frameworks, we test a model of creative behaviors during the day at work and the extent to which employees spend time with their spouses at home in the evening, using 685 daily matched responses from 108 worker-spouse pairings. Our results reveal that variance-focused creative behaviors (problem identification, information searching, idea generation) lead to a decline in time spent with spouse at home. In contrast, selection-focused creative behaviors (idea validation) lead to an increase in time spent with spouse. Further, openness to experience moderates these relationships. Overall, the results raise questions about the possible relational costs of creative behaviors at work on life at home.

We develop and test a theoretical model that explores how individuals appraise different types of stressful job demands and how these cognitive appraisals impact job performance. The model also explores how charismatic leaders influence such appraisal and reaction processes, and by virtue of these effects, how leaders can influence the impact of stressful demands on their followers' job performance. In Study 1 (n = 74 U.S. Marines), our model was largely supported in hierarchical linear modeling analyses. Marines whose leaders were judged by superiors to exhibit charismatic leader behaviors appraised challenge stressors as being more challenging, and were more likely to respond to this appraisal with higher performance. Although charismatic leader behaviors did not influence how hindrance stressors were appraised, they negated the strong negative effect of hindrance appraisals on job performance. In Study 2 (n = 270 U.S. Marines) charismatic leader behaviors were measured through the eyes of the focal Marines, and the interactions found in Study 1 were replicated. Results from multilevel structural equation modeling analyses also indicate that charismatic leader behaviors moderate both the mediating role of challenge appraisals in transmitting the effect of challenge stressors to job performance, and the mediating role of hindrance appraisals in transmitting the effect of hindrance stressors to job performance. Implications of our results to theory and practice are discussed. Keywords: stress, leadership, job performance, multilevel modeling

This study explores the hybridization of field-level logics. We define hybridized logics as rules of action, interaction, and interpretation that integrate the goals of previously incompatible logics through material forms, practices, and governance arrangements. Through an inductive study of the wind energy field in Colorado, we find that a hybridized logic emerged through a process in which organizational responses to logic incompatibility drove shifts in the relationship between logics and organizations. Compromise and framing efforts unintentionally initiated a process of logic hybridization by catalyzing proponents of the subordinate logic to contest the dominant logic and alter the balance of power in the field. Hybrid organizations then emerged to establish, legitimize, and embed a new set of inter-linked frames, practices, and arrangements that integrated previously incompatible logics. Our findings suggest that the hybridization of field-level logics is a complex process in which organizational actions and field-level conditions recursively influence each other over time.

After new ventures have exhausted the limited financial resources of founders, family, and friends, they often pursue initial external capital. To secure investment, entrepreneurs can signal about their venture's latent potential by aligning themselves with reliable third parties. Such affiliations affirm the new venture's legitimacy and provide substantive benefits in the form of mentoring, access to resources, and ongoing monitoring. However, early stage financing is an especially "noisy" signaling environment owing to the large number of startups seeking funding, many of which will not survive. The real value of third party affiliations in this context resides in their ability to unlock the potential of other more pedestrian signals, such as the entrepreneur's characteristics and actions that might otherwise go unnoticed. We borrow from the sensemaking literature to explain how third party affiliation signals disambiguate signals with multiple possible interpretations so that potential investors interpret them positively. Findings support our theory that a startup's characteristics and actions are signals that remain relatively unnoticed unless a startup combines them with a third party affiliation that enhances the signal's value, thus increasing the likelihood of receiving external capital.

Accumulated knowledge on organizational justice leaves little reason to doubt the notion that organizational members benefit when leaders adhere to interpersonal justice rules. However, upon considering how justice behaviors influence subordinates' cognitive processes, we predict that interpersonal justice has a surprising, unintended negative consequence. Supervisors who violate interpersonal justice rules trigger subordinates to search for reasons why their supervisors are threatening them, causing subordinates to be more attuned to supervisors' individual characteristics and therefore unlikely to use stereotypes when evaluating them. In contrast, supervisors who adhere to interpersonal justice rules allow subordinates to divert attention away from them, leading subordinates' judgments of their supervisors to be influenced by stereotypes. Consistent with these predictions, in a survey we found that minority supervisors faced bias relative to Caucasian supervisors when supervisors adhered to—but not when they violated—interpersonal justice rules. We replicated this effect in an experiment and established that it is explained by an alternating pattern of stereotype activation and inhibition: participants viewed minority supervisors to be more deceitful than Caucasians when supervisors adhered to—but not when they violated—interpersonal justice rules. We then conducted exploratory analyses and identified one factor (unit size) that mitigates this troubling pattern.

The power of great novelists' storytelling is demonstrated by their ability to shape social attitudes, beliefs, and behaviors, and even to make life more worth living. However, although narrative pedagogical methods are widely employed in business education, and there are literature-focused electives, business seems to be too busy to require students to read novels. Novels may be perceived to be too long to generate an immediate return on investment. Few great novels are about business, and fewer still are set in a business environment relevant to the economic and technological context of the 21st century. The ones that are, however, are worth the investment, as they just might turn our business students into better business people. This novel claim builds upon the widely accepted thesis that narrative pedagogy cultivates better business people and increasing scientific evidence of the benefits of reading great novels. It goes further to suggest that great novels might belong as part of the core ethics requirement in that the form and quality of a narrative determines its enduring, ethical effectiveness. Particularly, novels distinctively explore the intersection of what to do and how to live that management education needs to develop better persons and more responsible professionals.