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.

This paper studies a dual-channel supply chain consisting of one risk-averse manufacturer and one risk-averse retailer with stochastic demand. Herein, the manufacturer provides value-added services to enhance channel demand. First, the optimal pricing and service decisions of the channel members are investigated under different settings, i.e., the cooperative game, Bertrand game, and manufacturer Stackelberg (MS) game models. Second, the effects of channel members' risk aversion on optimal channel prices and expected utilities are analysed under the assumption that the manufacturer service is a decision variable and an exogenous variable, respectively. Third, sensitivity analysis and numerical simulation are performed to verify our propositions consistently and seek more managerial implications. The findings suggest that the manufacturer's value-added services in their direct channel will improve the direct price while decreasing the retail price. Consumers' channel loyalty degree has a great influence on the optimal price decisions and the performance of the channel members. The direct price increases while the retail price decreases in the manufacturer's value-added services. The retailer's risk aversion has a greater influence on price decisions than that of the manufacturer.

Lihan TONG,Weijia LI,Qingxia YANG,Liyuan CHEN,Peng CHEN, Vol.E107-D, No.11, pp.1472-1475
We present Ksformer, utilizing Multi-scale Key-select Routing Attention (MKRA) for intelligent selection of key areas through multi-channel, multi-scale windows with a top-k operator, and Lightweight Frequency Processing Module (LFPM) to enhance high-frequency features, outperforming other dehazing methods in tests.
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

Takahito YOSHIDA,Takaharu YAGUCHI,Takashi MATSUBARA, Vol.E107-D, No.11, pp.1458-1462
Accurately simulating physical systems is essential in various fields. In recent years, deep learning has been used to automatically build models of such systems by learning from data. One such method is the neural ordinary differential equation (neural ODE), which treats the output of a neural network as the time derivative of the system states. However, while this and related methods have shown promise, their training strategies still require further development. Inspired by error analysis techniques in numerical analysis while replacing numerical errors with modeling errors, we propose the error-analytic strategy to address this issue. Therefore, our strategy can capture long-term errors and thus improve the accuracy of long-term predictions.
Publication Date: 2024/11/01

Nat PAVASANT,Takashi MORITA,Masayuki NUMAO,Ken-ichi FUKUI, Vol.E107-D, No.11, pp.1453-1457
We proposed a procedure to pre-process data used in a vector autoregressive (VAR) modeling of a temporal point process by using kernel density estimation. Vector autoregressive modeling of point-process data, for example, is being used for causality inference. The VAR model discretizes the timeline into small windows, and creates a time series by the presence of events in each window, and then models the presence of an event at the next time step by its history. The problem is that to get a longer history with high temporal resolution required a large number of windows, and thus, model parameters. We proposed the local density estimation procedure, which, instead of using the binary presence as the input to the model, performed kernel density estimation of the event history, and discretized the estimation to be used as the input. This allowed us to reduce the number of model parameters, especially in sparse data. Our experiment on a sparse Poisson process showed that this procedure vastly increases model prediction performance.
Publication Date: 2024/11/01

Myung-Hyun KIM,Seungkwang LEE, Vol.E107-D, No.11, pp.1449-1452
White-box cryptographic implementations often use masking and shuffling as countermeasures against key extraction attacks. To counter these defenses, higher-order Differential Computation Analysis (HO-DCA) and its variants have been developed. These methods aim to breach these countermeasures without needing reverse engineering. However, these non-invasive attacks are expensive and can be thwarted by updating the masking and shuffling techniques. This paper introduces a simple binary injection attack, aptly named clear & return, designed to bypass advanced masking and shuffling defenses employed in white-box cryptography. The attack involves injecting a small amount of assembly code, which effectively disables run-time random sources. This loss of randomness exposes the unprotected lookup value within white-box implementations, making them vulnerable to simple statistical analysis. In experiments targeting open-source white-box cryptographic implementations, the attack strategy of hijacking entries in the Global Offset Table (GOT) or function calls shows effectiveness in circumventing run-time countermeasures.
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

Naoya NEZU,Hiroshi YAMADA, Vol.E107-D, No.11, pp.1408-1421
Modern memory devices such as DRAM are prone to errors that occur because of unintended bit flips during their operation. Since memory errors severely impact in-memory key-value stores (KVSes), software mechanisms for hardening them against memory errors are being explored. However, it is hard to efficiently test the memory error handling code due to its characteristics: the code is event-driven, the handlers depend on the memory object, and in-memory KVSes manage various objects in huge memory space. This paper presents MemFI that supports runtime tests for the memory error handlers of in-memory KVSes. Our approach performs the software fault injection of memory errors at the memory object level to trigger the target handler while smoothly carrying out tests on the same running state. To show the effectiveness of MemFI, we integrate error handling mechanisms into a real-world in-memory KVS, memcached 1.6.9 and Redis 6.2.7, and check their behavior using the MemFI prototypes. The results show that the MemFI-based runtime test allows us to check the behavior of the error handling mechanisms. We also show its efficiency by comparing it to other fault injection approaches based on a trial model.
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.

To capture the complexity and diversity of systems with both technical and social features, modelling methods are needed that similarly provide various tools and concepts. Study of developed methods shows that despite all of their advantages and strengths, there is a need for a method that with a holistic approach integrates perspectives, strengths and tools of the developed methods and models with different aspects of socio-technical systems. The main aim of the current study is to design a method for modelling complex socio-technical systems. To achieve this goal, it is necessary to design a method that is based on creativity and existing knowledge base. Therefore, design science research is used as a research strategy to design proposed method. For the first time, design science research in the field of operations research has been used to design a modelling method. This study also presents new tools and concepts for modelling socio-technical systems.

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.

The Republic of India has witnessed an enormous leap in financial transactions after a sudden demonetisation in 2016. The study represents an in-depth analysis of the factors influencing e-wallets usage post-COVID situation covering the National Capital Region. The scientifically collected data were subjected to Pearson's correlation to recognise the correlation amongst the selected e-wallets. The usage of e-wallets is observed mainly during recharge, UPI payments, and utility payments. Through psychometric response and interaction analysis, six factors were selected and examined for data distribution and stable observation using standard deviation and variance coefficient. The coefficient of variance for six factors was observed ≤ 1. The weight of the factors noted to be secured way (0.184), to take advantage of cashback (0.182), low risk of theft (0.169), fast service (0.1689), ease to use (0.156), and saves time (0.139) using principal component eigenvectors analysis. Freecharge and Tez wallets reveal a maximum 99.2% correlation.

Underpinned by stakeholder and agency theories, this study adopts a qualitative multiple-case study approach to explore and analyse various stakeholder interests and how they affect digitalisation in the health sector of a developing country (DC). The study's findings revealed that four key stakeholder interests - political, regulatory, leadership, and operational - affect digitalisation in the health sector of DCs. Further, the study found that operational and leadership interests were emergent and were triggered by some digitalisation initiatives, which included, inter alia, the use of new eHealth software and the COVID-19 vaccination exercise, which established new structures and worked better through digitalisation. Conversely, political and regulatory interests were found to be relatively enduring since they existed throughout the pre- and post-digitalisation eras. The study also unearthed principal-agent conflicts arising from technological, organisational and regulatory factors that contribute to the paradoxical outcomes of digitalisation in the health sector.

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.

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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Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach We introduce the reinforcement quantum annealing (RQA) scheme in which an intelligent agent interacts with a quantum annealer that plays the stochastic environment role of learning automata and tries to iteratively find better Ising Hamiltonians for the given problem of interest. As a proof-of-concept, we propose a […]

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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.

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Programmatic Ad Targeting Types This article delves into how programmatic advertising employs automated technology to target precise audiences effectively. It examines the different data types leveraged, the array of targeting techniques available, and approaches for gauging the success of a campaign. Key Takeaways Programmatic advertising automates ad buying using machine learning and workflow [...]

How Does Contextual Targeting in Programmatic Work? This article delves into contextual programmatic advertising, which strategically positions ads on web pages by analyzing the content to ensure that these advertisements are pertinent and considerate of privacy. Discover what this method entails and how it operates. Key Takeaways Contextual programmatic advertising combines the automation [...]

What is Programmatic OTT Advertising? OTT programmatic advertising revolutionizes how brands reach viewers on streaming platforms. Automating ad buying and leveraging real-time data offers precise audience targeting and enhanced campaign efficiency. This method stands out compared to traditional TV ads. In this article, we’ll break down what OTT programmatic advertising is, its key [...]

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. [...]

What is Programmatic TV Advertising? Programmatic TV advertising uses data and automated technology to buy and place TV ads more effectively. Unlike traditional methods relying on show ratings, it targets audience data, optimizing ad placements in real time. This introduction will explore what programmatic TV advertising is, its benefits, and steps to start [...]

AI in Programmatic Advertising AI in programmatic advertising automates and optimizes ad buying using advanced technology. This article explains how AI improves targeting, reduces costs, and boosts efficiency. You’ll learn about current trends, benefits, and real-world examples. Dive in to see how AI can transform your advertising strategies. Key Takeaways AI significantly enhances [...]

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. [...]

Cross-Device Targeting With Programmatic Ads Cross-device advertising allows advertisers to target users across multiple devices like phones, laptops, and TVs. This method improves ad targeting, user engagement, and campaign measurement. In this article, we’ll explain how cross-device advertising works and its benefits. Key Takeaways Cross-device advertising enables marketers to reach users across multiple [...]

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.

In this inductive study, we shift the focus of stigma research inside organizational boundaries by examining its relationship with organizational identity. To do so, we draw on the case of Keystone, a social enterprise in the East of England that became stigmatized after it initiated a program of support for a group of migrants in its community. Keystone's stigmatization precipitated a crisis of organizational identity. We examine how the identity crisis unfolded, focusing on the forms of identity work that Keystone's leaders enacted in response. Interestingly, we show not only that the internal effects of stigmatization on identity can be managed, but also that they may facilitate unexpected positive outcomes for organizations.

'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.

We consider how social and psychological connections among CEOs explain the propensity for corporate leaders to distance themselves socially from journalists who engage in negative reporting about firm leadership at other companies, and we examine the consequences for the valence of journalists' subsequent coverage. Our theoretical framework suggests that journalists who have engaged in negative coverage of a firm's leadership and strategy are especially likely to experience distancing from other leaders who (i) have friendship ties to the firm's CEO, (ii) are demographically similar to the CEO on salient dimensions, or (iii) are socially identified with the CEO as a fellow member of the corporate elite. Our theory and findings ultimately suggest that, due to the multiple sources of social identification between CEOs, journalists who engage in negative coverage of firm leadership tend to experience social distancing from multiple CEOs, and such distancing has a powerful influence on the valence of journalists' subsequent reporting about firm leadership and strategy across all the firms that they cover. We also extend our theoretical framework to suggest how the effect of social distancing on the valence of journalists' coverage is moderated by the early and late stages of a journalist's career.

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

We advance a model that highlights contingent linkages between overconfidence and narcissism, entrepreneurial entry, and the successful realization of venture opportunities. Overall, our proposals point to a paradox in which entrepreneurs high in overconfidence and narcissism are propelled toward more novel venture contexts—where these qualities are most detrimental to venture success, and are repelled from more familiar venture contexts—where these qualities are least harmful, and may even facilitate venture success. To illuminate these patterns of misalignment, we attend to the defining characteristics of alternative venture contexts and the focal mechanisms of overconfidence and narcissism.

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.

This study looks at what happens to the collection of relationships (network) of service professionals during a role transition (promotion to a management role). Our setting is three professional service firms where we examine changes in relations of recently promoted service professionals (auditors, consultants, and lawyers). We take a comprehensive look at the drivers of two forms of network changes - tie loss and tie gain. Looking backward we examine the characteristics of the contact, the relationship, and social structure and identify which forces are at play in losing ties, revealing an overarching tendency for both cohesion and efficiency forces to play a role. Looking forward, we identify the effect of previous network structures that act as a "shadow of the past" and impact the quality of newly gained relations during the role transitions. Findings demonstrate that role transitions are not only influenced by a few key contacts but that the entire (extant) network of professional relationships shapes the way people reconfigure their workplace relations during a role transition.

Family firms are often portrayed as an important yet conservative form of organization that is reluctant to invest in innovation; however, at the same time, evidence shows that family firms are still flourishing and that many of the world's most innovative firms are indeed family firms. Our study contributes to disentangling this puzzling effect. We argue that family firms—owing to the family's high level of control over the firm, wealth concentration, and importance of non-financial goals—invest less in innovation but have an increased conversion rate of innovation input into output and, ultimately, a higher innovation output than non-family firms. Empirical evidence from a meta-analysis based on 108 primary studies from 42 countries supports our hypotheses. We further argue and empirically show that the observed effects are even stronger when the CEO of the family firm is a later-generation family member. However, when the CEO of the family firm is the firm's founder, innovation input is higher and, contrary to our initial expectations, innovation output is lower than that in other firms. We further show that the family firm-innovation input/output relationships depend on country-level factors, namely, the level of minority shareholder protection and the education level of the workforce in the country.

Drawing on expectancy violation theory, we explore the effects of anticipatory impression management in the context of acquisitions. We introduce impression offsetting, an anticipatory impression management technique organizational leaders employ when they expect a focal event will negatively violate the expectations of external stakeholders. Accordingly, in these situations, organizational leaders will announce the focal event contemporaneously with positive, but unrelated information. We predict impression offsetting will generally occur in the context of acquisitions, but also more frequently for specific acquiring firms and acquisitions that are more likely to lead to an expectancy violation. We also posit that offsetting will effectively inhibit observers' perceptions of events as negative expectancy violations by positively influencing shareholder reactions to acquisition announcements. Consistent with our hypotheses, in a sample of publicly traded acquisition targets, we find evidence for impression offsetting, in which characteristics of both acquirers and their announced acquisitions predict its frequency of use. We also find evidence that impression offsetting is efficacious; on average, it reduces the negative market reaction to acquisition announcements by over 40 percent, which translates into approximately $246 million in market capitalization.

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.