Aim/Purpose: The amount of information published has increased dramatically due to the information explosion. The issue of managing information as it expands at this rate lies in the development of information extraction technology that can turn unstructured data into organized data that is understandable and controllable by computers Background: The primary goal of named entity recognition (NER) is to extract named entities from amorphous materials and place them in pre-defined semantic classes. Methodology: In our work, we analyze various machine learning algorithms and implement K-NN which has been widely used in machine learning and remains one of the most popular methods to classify data. Contribution: To the researchers’ best knowledge, no published study has presented Named entity recognition for the Kikuyu language using a machine learning algorithm. This research will fill this gap by recognizing entities in the Kikuyu language. Findings: An evaluation was done by testing precision, recall, and F-measure. The experiment results demonstrate that using K-NN is effective in classification performance. Recommendation for Researchers: With enough training data, researchers could perform an experiment and check the learning curve with accuracy that compares to state of art NER. Future Research: Future studies may be done using unsupervised and semi-supervised learning algorithms for other resource-scarce languages.

Aim/Purpose: To provide a systematic approach to defining task complexity using a three worlds model previously introduced in informing science research. Background: The task complexity construct presents researchers with a quandary. While it appears useful on the surface, repeated attempts to define it rigorously have failed to gain traction in the broader research community. The level of inconsistency between definitions is shown to have changed little in the past 20 years. Methodology: Using a common framework that treats task complexity as a latent construct residing between sources and outcomes, moderated by both task familiarity and task discretion, separate models for each of the three worlds are developed. Contribution: Our paper proposes a potential path forward by showing how many issues in past task complexity research can be reconciled by framing the construct according to the three worlds model: the world we experience, the world of human artifacts, and the “real world.” Findings: The framework defines experienced complexity as occurring in the mind of the task performer while performing a single task instance, intrinsic complexity as a function of the internal characteristics of the problem space used to perform a bounded set of task instances, and extrinsic complexity as the ruggedness of the fitness landscape in which the task is performed. Recommendation for Researchers: It offers a path to convergence for definitions of task complexity. Future Research: The three worlds of task complexity can potentially be applied to many practical problems.

Aim/Purpose: This review paper aims to unveil some underlying machine-learning classification algorithms used for political election predictions and how stack ensembles have been explored. Additionally, it examines the types of datasets available to researchers and presents the results they have achieved. Background: Predicting the outcomes of presidential elections has always been a significant aspect of political systems in numerous countries. Analysts and researchers examining political elections rely on existing datasets from various sources, including tweets, Facebook posts, and so forth to forecast future elections. However, these data sources often struggle to establish a direct correlation between voters and their voting patterns, primarily due to the manual nature of the voting process. Numerous factors influence election outcomes, including ethnicity, voter incentives, and campaign messages. The voting patterns of successors in regions of countries remain uncertain, and the reasons behind such patterns remain ambiguous. Methodology: The study examined a collection of articles obtained from Google Scholar, through search, focusing on the use of ensemble classifiers and machine learning classifiers and their application in predicting political elections through machine learning algorithms. Some specific keywords for the search include “ensemble classifier,” “political election prediction,” and “machine learning”, “stack ensemble”. Contribution: The study provides a broad and deep review of political election predictions through the use of machine learning algorithms and summarizes the major source of the dataset in the said analysis. Findings: Single classifiers have featured greatly in political election predictions, though ensemble classifiers have been used and have proven potent use in the said field is rather low. Recommendation for Researchers: The efficacy of stack classification algorithms can play a significant role in machine learning classification when modelled tactfully and is efficient in handling labelled datasets. however, runtime becomes a hindrance when the dataset grows larger with the increased number of base classifiers forming the stack. Future Research: There is the need to ensure a more comprehensive analysis, alternative data sources rather than depending largely on tweets, and explore ensemble machine learning classifiers in predicting political elections. Also, ensemble classification algorithms have indeed demonstrated superior performance when carefully chosen and combined.

Labor pain and its relieving methods is one of the anxieties of mothers having a great impact on the quality of care during delivery as well as the patients' satisfaction. The propensity of using non-medicinal pain relief methods is increasing. The present study aimed to compare the effect of Acupressure at two GB-21 and SP06 points on the severity of labor pain. In this quasi-experimental single blind study started on December 2010 and ended on June 2011 in which 150 primiparous women were divided into three groups of Acupressure at GB-21 point, Acupressure at SP-6 point and control group. The intervention was carried out for 20 min at 3-4 and 20 min at 7-8 cm dilatation of Cervix. The pain severity was measured by Visual Analog Scale before and immediately, 30 and 60 min after the intervention. Then, the data were statistically analyzed. No significant difference was found among the 3 groups regarding the pain severity before the intervention. However, the pain severity it was reduced at 3-4 and 7-8 cm dilatation immediately, 30 and 60 min after the intervention in the two intervention groups compared to the control group (p<0.001). Nonetheless, no statistically significant difference was observed between the two intervention groups (p = 0.93). The results of the study showed that application of Acupressure at two GB-21 and SP-6 points was effective in the reduction of the severity of labor pain. Therefore, further studies are recommended to be performed on the application of Acupressure together with non-medicinal methods.

[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.

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%.

This paper presents a novel deep learning technique called quantum dilated convolutional neural network-DenseNet (Q-DenseNet) for prediction of heart disease in spark framework. At first, the input data taken from the database is allowed for data partitioning using fast fuzzy C-means clustering (FFCM). The partitioned data is fed into spark framework, where pre-processed by missing data imputation and quantile normalisation. The pre-processed data is further allowed for selection of suitable features. Then, the selected features from the slave nodes are merged and fed into master node. The Q-DenseNet is used in master node for the prediction of heart disease. The performance improvement of the designed Q-DenseNet model is validated by comparing with traditional prediction models. Here, the Q-DenseNet method achieved superior performance with maximum of 92.65% specificity, 91.74% sensitivity, and 90.15% accuracy.

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

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

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

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.

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 […]

The post Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach 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 [...]

VAST vs. VPAID Wondering whether to use a Video Player Ad Interface Definition (VPAID) or Video Ad Serving Template (VAST) for your next video ad campaign? The answer lies in their distinct features: VPAID offers interactive ad experiences and granular insights into user data, whereas VAST is all about compatibility and efficient ad [...]

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 [...]

'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.

Beside making organizations look like their peers through the adoption of similar attributes (which we call alignment), this paper highlights the fact that conformity also enables organizations to stand out by exhibiting highly salient attributes key to their field or industry (which we call conventionality). Building on the conformity and status literatures, and using the case of major U.S. symphony orchestras and the changes in their concert programing between 1879 and 1969, we hypothesize and find that middle-status organizations are more aligned, and middle-status individual leaders make more conventional choices than their low- and high-status peers. In addition, the extent to which middle-status leaders adopt conventional programming is moderated by the status of the organization and by its level of alignment. This paper offers a novel theory and operationalization of organizational conformity, and contributes to the literature on status effects, and more broadly to the understanding of the key issues of distinctiveness and conformity.

Hierarchy is a reality of group life, for humans as well as for most other group-living species. And yet, there remains considerable debate about whether and when hierarchy can promote group performance and member satisfaction. We suggest that progress in this debate has been hampered by a lack of clarity about hierarchy and how to conceptualize it. Whereas prevailing conceptualizations of hierarchy in the group and organization literature focus on inequality in member power or status (i.e., centralization or steepness), we build on the ethological and social network traditions to advance a view of hierarchy as cascading relations of dyadic influence (i.e., acyclicity). We further suggest that hierarchy thus conceptualized is more likely to capture the functional benefits of hierarchy whereas hierarchy as inequality is more likely to be dysfunctional. In a study of 75 teams drawn from a wide range of industries, we show that whereas acyclicity in influence relations reduces conflict and thereby enhances both group performance and member satisfaction, centralization and steepness have negative effects on conflict, performance, and satisfaction, particularly in groups that perform complex tasks. The theory and results of this study can help to clarify and advance research on the functions and dysfunctions of hierarchy in task groups.

The interview questions deal with the means by which Estonia and other republics of the former Soviet Union managed to transform their educational systems and the impact of the Soviet heritage on this transformation. An interview was conducted with Professor Olav Aarna. In 1991 Professor Olav Aarna became the rector of TUT. From 2000 to 2003 he held the position of rector of the first private university in Estonia - Estonian Business School (EBS). From 2003 to 2007 Olav Aarna was member of the Estonian Parliament, serving also as Chairman of the Committee for Cultural Affairs responsible for education, research, culture and sports affairs. From 1998-2000 he was Vice Chairman of Estonian National Council for Research and Development. His experience in the field of educational legislation stems from his advisory position to the Minister of Education of Estonia from 1990 to1992. His competence in the field of the Bologna process results from the development of higher education legislation in Estonia (2002-...) and the development of a higher education quality assurance system for Estonia (2008-...). Olav Aarna has consulted third countries in the national qualifications framework (NQF) development as a European Training Foundation (ETF) expert.

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.

We argue that employees who perceive that they are provided with a safe climate at work within which to voice their concerns and suggestions about work-related issues or problems will not only be more engaged employees but will also be likely to be more engaged and involved members of their communities. By focusing on the importance of employee voice opportunities, in work organizations, we seek to build our understanding of how to create "positive" organizations that contribute to the building of human potential, both inside the organizational setting and outside in our communities and societies. We also consider how employee voice opportunities in for-profit organizations may be influenced by the law and prevailing attitudes about corporate governance.

Identification is integral to mentoring relationships, yet we know relatively little about the process through which mentors and protégés identify with each other, how this mutual identification shifts through the phases of the mentoring relationship, and how identification impacts the quality of the relationship over time. In this paper, we integrate theories of the self, relationships, and relational mentoring to consider the role of identification in informal mentoring. Specifically, we theorize how the process of personal identification occurs in mentoring from the perspective of both the mentor and protégé and offer a model that demonstrates how shifts in identification relate to the quality of the relationship that develops over time. We conclude with a discussion of implications for research and theory in mentoring.

Research about the effects of an organization's general reputation following a negative event remains equivocal: Some studies have found that a high reputation is a benefit because of the stock of social capital and goodwill it generates; others have found it to be a burden because of the greater stakeholder attention and violation of expectations associated with a negative event. We theorize that stakeholders' level of organizational identification helps explain which mechanisms are more dominant. We test our hypotheses on a sample of legislative references associated with NCAA major infractions from 1999-2009. Our results indicate that a high reputation is a burden for an organization when considering low-identification stakeholder support: As the number of legislative references increases, a high-reputation university will receive fewer donations from non-alumni donors than universities without this asset. In contrast, a high reputation is a benefit when considering high-identification stakeholder support: As the number of legislative references increases, a high-reputation university will receive more donations from alumni donors than universities without this asset. However, an exploratory investigation reveals that alumni donations to high-reputation universities decline as the number of legislative references increases, suggesting that the benefit of a high reputation has a limit.

This essay presents a comprehensive briefing on the past and present of a business educational culture that is significantly different in ethos and structure to the widely known systems in the US and UK. That is the history and culture of the French Grandes Ecoles de Commerce. A brief reminder of extant literature on the utility of business education and its seeming misalignment with the competencies and skills as specified by practitioners is then given. Key pressures and trends on and within this system - such as internationalisation, accreditation and a greater emphasis on publications are identified and discussed. These threads are then combined in a partial replication of the work of Dierdorff and Rubin (2006; 2009). Specifically, information on 1582 classes from 542 programmes at the top Grandes Ecoles de Commerce is presented alongside further secondary data and then analysed in respect of alignment with Rubin and Dierdorff's identified behavioural competencies. We argue that whilst well intentioned, the outcome of these pressures may well be that inherent and historical strengths of great value are being discarded, and that the degree of irrelevance and misalignment between educational provision and required managerial competence will stay the same or even get worse.

Evidence-based management has been widely advocated in management studies. It has great ambition: all manner of organizational problems are held to be amenable to an evidence-based approach. With such ambition, however, has come a certain narrowness which risks restricting our ability to understand the diversity of problems in management studies. Indeed, in the longer term, such narrowness may limit our capacity to engage with many real-life issues in organizations. Having repeatedly heard the case for evidence-based management, we invite readers to weigh up the case against. We also set out an alternative direction - one that promotes intellectual pluralism and flexibility, the value of multiple perspectives, openness, dialogue, and the questioning of basic assumptions. These considerations are the antithesis of an evidence-based approach, but central to a fully rounded management education.

Growing workforce diversity increases the likelihood that supervisors and subordinates will differ along demographic lines, a situation that has important implications for their relationship quality and individual outcomes. In a sample of 1,253 employees from 54 work-units, we investigate the effects of differences in disability status between supervisors and subordinates on leader-member-exchange (LMX) quality and subsequent performance ratings, and find that incongruence in general is related to lower LMX quality and lower performance. In addition, we propose and find an asymmetrical effect of disability incongruence, such that LMX quality is worse in dyads in which the supervisor has a disability than in dyads in which the subordinate has a disability. Furthermore, we investigate the moderating role of unit-level climate for inclusion on this relationship and find support for a buffering effect of inclusive climates on the negative incongruence-LMX relationship for scenarios in which the supervisor, but not the subordinate, has a disability. We build relevant theory for the relational demography, disability, LMX, and organizational climate literatures by predicting these effects on the basis of status mechanisms. These findings have important practical implications, as they provide companies with a feasible way to manage their diverse workforce.

Over the past decade, the terms "crisis" and "crisis management" have become increasingly popular topics of interest for business professionals and management academics alike. According to the Institute for Crisis Management (2013), "Newsworthy business crises have been on a steady upward trend since 2009.

We examine when and why creative role identity causes entitlement and unethical behaviors and how this relationship can be reduced. We found that the relationships among the creative identity, entitlement, and dishonesty are contingent on the perception of creativity being rare. Four experiments showed that individuals with a creative identity reported higher psychological entitlement and engaged in more unethical behaviors. Additionally, when participants believed that their creativity was rare compared to common, they were more likely to lie for money. Moreover, manipulation of rarity of creative identity, but not practical identity, increased psychological entitlement and unethical acts. We tested for the mediating effect of psychological entitlement on dishonesty using both measurement of mediation and experimental causal chain approaches. We further provide evidence from organizations. Responses from a sample of supervisor-subordinate dyads demonstrated that employees reporting strong creative identities who perceived creativity as rare in their work-group rather than common were rated as engaging in more unethical behaviors by their supervisors. This paper extends prior theory on negative moral consequences of creativity by shedding new light on assumption regarding the prevalence of creativity and the role psychological entitlement plays.

Social enterprise has gained widespread acclaim as a tool for addressing social and environmental problems. Yet, because these organizations integrate the social welfare and commercial logics, they face the challenge of pursuing goals that frequently conflict with each other. Studies have begun to address how established social enterprises can manage these tensions, but we know little about how, why, and with what consequences social entrepreneurs mix competing logics as they create new organizations. To address this gap, we develop a theoretical model based in identity theory that helps to explain: (1) how the commercial and social welfare logics become relevant to entrepreneurship, (2) how different types of entrepreneurs perceive the tension between these logics, and (3) the implications this has for how entrepreneurs go about recognizing and developing social enterprise opportunities. Our approach responds to calls from organizational and entrepreneurship scholars to extend existing frameworks of opportunity recognition and development to better account for social enterprise creation.

We study the interplay between regulatory mandates and competition on a focal firm's new resource investments. While prior literature has separately pointed to the influence of competition and regulatory policy on a focal firm's resource decisions, less is known about how the policy effect interacts with the competitive effect. Studying how regulatory mandates moderate the effect of competition on a focal firm's new resource investments, we show that resource redeployment is not simply a function of internal firm decisions but a response to external forces. We find that regulatory mandates dampen the effect of competitors' new resource investments on a focal firm's new resource investments. Distinguishing between different clean technology types, we show that this dampening effect is the stronger, the more distant the new resource is from incumbents' old resource base, and the more established the mandate is. We test our hypotheses in the context of renewable energy investments in waste-to-energy, wind, and solar in the U.S. electricity industry. Our data comprises 1542 utilities and private energy firms and their renewable investments from 1999 to 2010.

In this paper, we use a practice-based wisdom perspective to address the challenges of managing competing logics in social enterprises. From previous work it is clear that a major task for social entrepreneurs is to manage the tension between social welfare and commercial logics. Although the social welfare logic and its related values and practices form the foundations of social enterprises, social entrepreneurs have also to ensure that their businesses are commercially sustainable making it necessary to engage with the commercial logic. To this end, we develop a curriculum matrix based on social practice wisdom to assist students to learn appropriate knowledge and skills, enact social entrepreneurship goals, and integrate competing logics in innovative and sustainable ways.

Many firms seek to manage their legal and regulatory environments by influencing policymakers. Typically, researchers have focused on how firms use nonmarket actions, including lobbying, campaign contributions, and related activities, to gain policy influence. We argue that firms may also seek to change the effects of policies through market actions. Market actions may lead to both formal policy change (i.e., transformations of codified rules) and interpretive policy change (i.e., transformations of the effects of rules without changes in their codified form). We identify two pathways by which firms' market actions may produce interpretive policy change: implementation and innovation. Implementation-driven change occurs when firms' interpretations of incomplete laws alter and clarify the meaning of those laws. Innovation-driven change occurs when firms engage in novel activities that are difficult to interpret within existing regulatory frameworks, and thus alter the effects of those regulations. We then theorize how firms' market actions may complement traditional, nonmarket political mobilization in an analysis of sequences of formal and interpretive policy change.

Avast Secure Browser strives to offer a ‘private, fast and secure’ service for Windows users. Simply put, this product has been built for privacy by security experts. It boasts an array of features to make sure that all cybersecurity bases are more t...

Ashampoo WinOptimizer 17 cleans, accelerates and secures your Windows system. The program takes care of maintenance issues that arise from day-to-day Windows use. Tools such as 1-click optimization declutter hard disks, repair invalid shortcuts and d...

With this book, O’Regan efficiently addresses a wide range of ethical and legal issues in computing. It is well crafted, organized, and reader friendly, featuring many recent, relevant examples like tweets, fake news, disinformation

This rather difficult read introduces the programming language FSM and the programming platform DrRacket. The author asserts that it is a convenient platform to design and prove an automata-based software

Improving equity in data science, edited by Colby Tofel-Grehl and Emmanuel Schanzer, is a thought-provoking exploration of how data science education can be transformed to foster equity, especially within K-16 classrooms. The editors advocate for redefining

As an academic, I have cheered for and welcomed the open access (OA) mandates that, slowly but steadily, have been accepted in one way or another throughout academia. It is now often accepted that public funds means public

Wales scrum-half Tomos Williams is ruled out of the Autumn Nations Series match against Australia on Sunday.

Spain's Civil Protection Agency sent a mass alert to phones warning of an "extreme risk of rainfall".

Hegseth, who is also a former soldier without political experience, will lead the world's most powerful military.

For now, there are several good choices for gun owners that DeWine can make, a few problematic ones, and one that’s completely unacceptable, his previous pick Dolan.

As if gun rights activists have not had enough winning for one week, with the landslide Trump election win.  Now, a major victory for gun rights advocates has unfolded in Illinois...

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