Aim/Purpose: The rise of virtual communication technologies and hybrid work contexts has brought significant changes to leadership dynamics, highlighting the need for effective management of teams operating in both face-to-face and virtual settings, known as hybrid teams. Background: This systematic review examines leadership models utilized in face-to-face and virtual teams, factors contributing to leadership emergence in these contexts, and effective strategies for leading hybrid teams. Methodology: In this study, three scientific databases were searched, resulting in the retrieval of 1,707 studies. These studies were then subjected to a review process following the PRISMA guidelines, ultimately leading to the inclusion of 15 research contributions in the final review. Contribution: Given the results, key strategies for practitioners include the development of strong communication skills, providing constructive feedback, and implementing efficient remote management techniques. Findings: The findings emphasize three prominent leadership models – transformational leadership, leader-member exchange (LMX), and shared leadership – all of which play crucial roles in hybrid team settings. Personality factors drive leadership emergence in face-to-face settings, while virtual settings benefit more from task-related behaviors. Recommendation for Researchers: This review informs researchers seeking to enhance leadership efficacy in modern group settings, aiding leaders in navigating the complexities of hybrid team environments.

Aim/Purpose: The paper discusses various factors contributing to disagreements, such as differing experiences, perspectives, and historical narratives, leading to disagreements within families and societies. It explores how beliefs, values, and biases feed into disagreements, with confirmation bias affecting decision-making and the media. Cultural values also play a role, showcasing conflicts between meritocracy and inclusivity in ethical decision-making. Haidt's Moral Foundations Theory highlights differences in value priorities between Western and Eastern societies. The impact of Western values like rationalism, freedom, and tolerance, under threat from Marxist illiberalism on campuses, is dis-cussed. The text also delves into disinformation, emotions in warfare, and the use of fake information and images for propaganda purposes. The need for diligent reporting to avoid spreading disinformation is emphasized, given its potential to create misconceptions and harm diplomatic relations.

Aim/Purpose: The aim of the present study is to investigate how pre-game superstition and anxiety can drive the consumption and purchase of sports products and objects by triathletes. Methodology: We tested our hypotheses via a cross-sectional study on a sample of N=124 triathletes. Contribution: The originality of our work stands in the provision of empirical evidence on the role of superstition and anxiety in characterized consumer decision-making of triathletes. Theoretically and practically, our results can extend our knowledge of the role of cognitive factors in consumer behaviors among athletes. Findings: The results of the Structural Equation Modelling provided evidence of our hypothesized relationship between pre-game anxiety and superstition, and cognitive biases. Pre-game anxiety increases the level of incidence of specific cognitive biases characterized by intuitive and implicit thinking, while superstition leads to more rational and personal cognitive biases, which affect their purchasing of sports products before games and competitions.

Aim/Purpose: In this paper we propose a framework identifying many of the unintended consequences of information technology and posit that the increased complexity brought about by IT is a proximate cause for these negative effects. Background: Builds upon the three-world model that has been evolving within the informing science transdiscipline. Methodology: We separate complexity into three categories: experienced complexity, intrinsic complexity, and extrinsic complexity. With the complexity cycle in mind, we consider how increasing complexity of all three forms can lead to unintended consequences at the individual, task and system levels. Examples of these consequences are discussed at the individual level (e.g., deskilling, barriers to advancement), the task level (e.g., perpetuation of past practices), as well as broader consequences that may result from the need to function in an environment that is more extrinsically complex (e.g., erosion of predictable causality, shortened time horizons, inequality, tribalism). We conclude by reflecting on the implications of attempting to manage or limit increases of complexity. Contribution: Shows how many unintended consequences of IT could be attributed to growing complexity. Findings: We find that these three forms of complexity feed into one another resulting in a positive feedback loop that we term the Complexity Cycle. As examples, we analyze ChatGPT, blockchain and quantum computing, through the lens of the complexity cycle, speculating how experienced complexity can lead to greater intrinsic complexity in task performance through the incorporation of IT which, in turn, increases the extrinsic complexity of the economic/technological environment. Recommendations for Practitioners: Consider treating increasing task complexity as an externality that should be considered as new systems are developed and deployed. Recommendation for Researchers: Provides opportunities for empirical investigation of the proposed model. Impact on Society: Systemic risks of complexity are proposed along with some proposals regarding how they might be addressed. Future Research: Empirical investigation of the proposed model and the degree to which cognitive changes created by the proposed complexity cycle are necessarily problematic.

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.

COVID-19 created a surge in e-commerce usage, leading to fierce channel competition between the manufacturer's online sales and the offline retailer. Hence, the imperative need for effective and innovative optimisation strategies to mitigate channel competition. Manufacturer-coupons are widely practiced in market, yet research on the importance they play in coordinating channel competition to achieve optimisation in channel distributions is scarce. This research addresses this gap by examining the effectiveness of manufacturer-coupons on the coordination of the manufacturer's online sales and offline retailer's sales. The findings indicate that issuing a manufacturer-coupon to the customers who buy from the offline retailer reduces the competition in the different channel distributions, but cost sharing of the retailer coupon is a better strategy. We thus examine if profit sharing is an effective strategy to facilitate the use of manufacturer-coupon in the market. After comparing different scenarios, we conclude that advanced profit-sharing can be effective in making manufacturer-coupon prevalent in the market and thus alleviate channel competition effectively.

Data envelopment analysis (DEA) is widely used in various fields and for various models. Inverted data envelopment analysis (inverted DEA) is an extended model of DEA. Regression analysis (RA) is a statistical process for estimating the relationships among variables based on the model of averaged image. There are no essential relations among DEA and RA and inverted DEA. We creatively combine DEA, RA and inverted DEA to propose a new model: DEA-RA-Inverted DEA model. The model realises the efficiency estimation and evaluation through a discussion of the residual variables and the residual ratio coefficients. In addition, we will demonstrate the effectiveness of the model by applying it to efficiency estimation and evaluation of 16 Chinese logistics enterprises.

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

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

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

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

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.

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.

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.

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.

Automated Data Augmentation via Wikidata Relationships Oyesh Singh, UMBC10:30-11:30 Monday, 21 October 2019, ITE 346 With the increase in complexity of machine learning models, there is more need for data than ever. In order to fill this gap of annotated data-scarce situation, we look towards the ocean of free data present in Wikipedia and other […]

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

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The PALS system captures physical context from sensed data, reasons about the context and associated context-driven policies to make access-control decisions and detect intrusions into smart home systems based on both network and behavioral data

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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 [...]

Amazon Fire TV Commercials Guide Understanding Amazon Fire TV advertisements is essential for maximizing their marketing potential. This guide provides a comprehensive overview of the various ad options on Amazon Fire TV, including inline ads, feature rotators, sponsored screensavers, and sponsored tiles. It also explores targeting and personalization features to tailor advertisements to [...]

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 Direct? In this article, we will delve into Programmatic Direct, a technique by which advertisers utilize automated technology to buy digital advertising space directly from publishers. By doing so, the middlemen are eliminated, resulting in more focused and effective ad placements. Programmatic Direct simplifies sales processes, making it easier for [...]

What is Programmatic OOH? Programmatic Out-of-Home (OOH) refers to the automated buying and selling of Digital Out-of-Home (DOOH) advertising spaces using data-driven technology. Unlike traditional OOH, which requires manual negotiations, programmatic OOH utilizes software to optimize ad placements efficiently and target specific audiences based on data. This article explores the benefits, workings, and [...]

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 [...]

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 [...]

Programmatic Guaranteed vs. PMP Deciding between Programmatic Guaranteed and PMP (Private Marketplace) deals? Programmatic advertising has revolutionized digital advertising by using advanced technology and data to streamline the buying and selling of digital ad space. Unlike traditional methods, programmatic buying enables advertisers to target audiences more effectively and distribute ads on a large [...]

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 [...]

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 [...]

Programmatic Ad Mediation Explained Programmatic ad mediation allows publishers to manage multiple ad networks from a single platform, maximizing revenue and efficiency. This article explores how it works, its benefits, and tips for selecting the right platform. Key Takeaways Programmatic ad mediation streamlines the management of multiple ad networks through a unified platform, [...]

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.

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.

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.

Management educators often assume that research-based arguments ought to be convincing to students. However, college students do not always accept even well-documented research findings. Among the reasons this might happen, we focus on the potential role of psychological mechanisms triggered by scholarly arguments that affect students' self-concepts, leading them to engage in self-enhancing or self-protective responses. We investigated such processes by examining students' reactions to a research argument emphasizing the importance of intelligence to job performance, in comparison to their reactions to research arguments emphasizing the importance of emotional intelligence and/or fit. Consistent with our predictions, students were less likely to accept the argument for the importance of intelligence compared to the alternative, less threatening, arguments (i.e., the importance of emotional intelligence or fit). Further, acceptance of the argument about the importance of intelligence was affected by students' grade point average (GPA) and moderated by their emotional stability. Specifically, consistent with self-enhancement theory, students with lower GPAs were more likely to reject the argument for intelligence and give self-protective reasons for their responses, whereas students with higher GPAs were more likely to accept the argument and give self-enhancing reasons. Implications for future research and for management teaching are discussed.

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.

Psychological empowerment has been studied extensively over the past few decades in a variety of contexts and appears to be especially salient within dynamic and complex environments such as healthcare. However, a recent meta-analysis found that psychological empowerment relationships vary significantly across studies, and there is still a rather limited understanding of how empowerment operates across levels. Accordingly, we advance and test a multi-level model of empowerment which seeks to better understand the unique and synergistic effects between unit and individual empowerment in hospital units. Analysis of data involving 544 individuals in 78 units, collected from multiple sources over three different time periods, revealed that unit empowerment evidenced a synergistic interaction with individual-level psychological empowerment as related to individuals' job performance, as well as an indirect effect on performance via individual empowerment, while controlling for previous performance levels. Notably, these effects were significant at relatively high, but not at relatively low levels of unit empowerment. Furthermore, we found that unit voice climate increased unit empowerment and thereby enhanced individual psychological empowerment. These findings suggest that, in complex and dynamic environments, empowering work units is an important means by which leaders can enhance individuals' performance.

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