Aim/Purpose: The aim of this paper is to propose an ensemble learners based classification model for classification clickbaits from genuine article headlines. Background: Clickbaits are online articles with deliberately designed misleading titles for luring more and more readers to open the intended web page. Clickbaits are used to tempted visitors to click on a particular link either to monetize the landing page or to spread the false news for sensationalization. The presence of clickbaits on any news aggregator portal may lead to an unpleasant experience for readers. Therefore, it is essential to distinguish clickbaits from authentic headlines to mitigate their impact on readers’ perception. Methodology: A total of one hundred thousand article headlines are collected from news aggregator sites consists of clickbaits and authentic news headlines. The collected data samples are divided into five training sets of balanced and unbalanced data. The natural language processing techniques are used to extract 19 manually selected features from article headlines. Contribution: Three ensemble learning techniques including bagging, boosting, and random forests are used to design a classifier model for classifying a given headline into the clickbait or non-clickbait. The performances of learners are evaluated using accuracy, precision, recall, and F-measures. Findings: It is observed that the random forest classifier detects clickbaits better than the other classifiers with an accuracy of 91.16 %, a total precision, recall, and f-measure of 91 %.

Aim/Purpose: The growing complexity of the business environment and business processes as well as the Big Data phenomenon has an impact on every area of human activity nowadays. This new reality challenges the effectiveness of traditional narrowly oriented professional education. New areas of competences emerged as a synergy of multiple knowledge areas – transdisciplines. Informing Science and Data Science are just the first two such new areas we may identify as transdisciplines. Universities are facing the challenge to educate students for those new realities. Background: The purpose of the paper is to share the authors’ experience in designing curriculum for training bachelor students in Informing Science as a concentration within an Information Brokerage major, and a master program on Data Science. Methodology: Designing curriculum for transdisciplines requires diverse expertise obtained by both academia and industries and passed through several stages - identifying objectives, conceptualizing curriculum models, identifying content, and development pedagogical priorities. Contribution: Sharing our experience acquired in designing transdiscipline programs will contribute to a transition from a narrow professional education towards addressing 21st-century challenges. Findings: Analytical skills, combined with training in all categories of so-called “soft skills”, are essential in preparing students for a successful career in a transdiciplinary area of activities. Recommendations for Practitioners: Establishing a working environment encouraging not only sharing but close cooperation is essential nowadays. Recommendations for Researchers: There are two aspects of training professionals capable of succeeding in a transdisciplinary environment: encouraging mutual respect and developing out-of-box thinking. Impact on Society: The transition of higher education in a way to meet current challenges. Future Research The next steps in this research are to collect feedback regarding the professional careers of students graduating in these two programs and to adjust the curriculum accordingly.

Aim/Purpose: There is ample evidence that team processes matter more than the characteristics of individual team members; unfortunately, very few empirical studies have examined communication process variables closely or tied them to team outcomes. Background: The University of Miami Laboratory for Integrated Knowledge (U-LINK) is a pilot funding mechanism that was developed and implemented based on empirically-established best practices established in the literature on the Science of Team Science (SciTS). In addition to addressing grand societal challenges, teams engaged in processes designed to enhance the process of “teaming”. This study uses the Inputs-Mediator-Outputs-Inputs (IMOI) model as a blueprint for an investigation into how team communication processes (shared communication, shared leadership, formal meetings, informal meetings) influence intermediary team processes (goal clarity, role ambiguity, process clarity, trust) and team outcomes (team satisfaction, team productivity). Methodology: Monte Carlo methodologies were used to explore both longitudinal self-report (survey of communication and team outcome variables) data and objective data on scholarly productivity, collected from seventy-eight members of eleven real-world intact interdisciplinary teams to explore how team communication processes affect team outcomes. Contribution: This study is among the few that centers communication practice and processes in the operationalization and measurement of its constructs and which provides a test of hypotheses centered on key questions identified in the literature. Findings: Communication practices are important to team processes and outcomes. Shared communication and informal meetings were associated with increased team satisfaction and increased research productivity. Shared leadership was associated with increased research productivity, as well as improved process and goal clarity. Formal meetings were associated with increased goal clarity and decreased role ambiguity. Recommendation for Researchers: Studying intact interdisciplinary research teams requires innovative methods and clear specification of variables. Challenges associated with access to limited numbers of teams should not preclude engaging in research as each study contributes to our larger body of knowledge of the factors that influence the success of interdisciplinary research teams. Future Research: Future research should examine different team formation and funding mechanisms and extend observation and data collection for longer periods of time.

Aim/Purpose: The article is designed to contradict the existing opinion that “transdisciplinarity is a marginal direction of contemporary science.” Background: The difficulties of implementing transdisciplinarity into science and education are connected with the fact that its generally accepted definition, identification characteristics, and methodological features are still missing. In order to eliminate these disadvantages of transdisciplinarity, its prime cause and initial idea had to be detected. Then an attempt was made to analyze correspondence of the existing opinions about transdisciplinarity with the content of its prime cause and initial ideas. Methodology: The bibliometric content analysis of the literature reviews on the subject of transdisciplinary was used in order to determine correspondence of the opinions about transdisciplinarity with the meaning of its prime cause and initial ideas, as well as to generalize these opinions. This method allowed detecting and classifying opinions into 11 groups including 39 stereotypes of transdisciplinarity. For substantiation of transdisciplinary approaches consistency with the approaches of the contemporary science C.F. Gauss random variables normal distribution was used. The “Gauss curve” helped to show the place of transdisciplinary and systems transdisciplinary approaches in the structure of academic and systems approaches. The “Gauss curve” demonstrated the step-by-step broadening of the scientific worldview horizon due to sequential intensification of synthesis, integration, unification, and generalization of the disciplinary knowledge. Contribution: Based on rethinking the results from bibliometric content analysis of the literature reviews, the generalized definition of transdisciplinarity could be formulated, as well as the definition for the transdisciplinary and systems transdisciplinary approaches could be given. It was shown that transdisciplinarity is a natural stage for development of contemporary science and education, and the transdisciplinary approaches were capable to suggest the methods and tools to solve the complex and poorly structured problems of science and society. Findings: Many existing stereotypes of transdisciplinarity do not meet its prime cause and initial ideas. Such stereotypes do not have deep philosophic and theoretical substantiation, as well as not suggesting the transdisciplinary methods and tools. Thus, the authors of such stereotypes often claim them to be transdisciplinary or suggest perceiving them as transdisciplinarity. This circumstance contributed to the fact that many disciplinary scientists, practitioners, and initiators of higher education view transdisciplinarity as a marginal direction of contemporary science. Based on the generalized definition of transdisciplinarity, as well as its prime cause and initial ideas, we managed to show that transdisciplinarity is presented in contemporary science in the form of two different approaches: the transdisciplinary approach and the systems transdisciplinary approaches. The objective of the transdisciplinary approach is ensuring science development at the stage of synthesis and integration of disciplinary knowledge. The objective of the systems transdisciplinary approach is ensuring solving of modern society problems using unification and generalization of disciplinary knowledge. Recommendation for Researchers: The researchers should consider that within the limits of the transdisciplinary approach the disciplinary specialists are managed. Within the limits of the systems transdisciplinary approach the disciplinary knowledge is managed. Thus, the transdisciplinary approach is efficient for organization and research with participation of the scientists of complementary disciplines. An example for such research can be a team of researchers of medical disciplines and complimentary disciplines from chemistry, physics, and engineering. The systems transdisciplinary approach is efficient for organization and performance of research with participation of scientists of non-complementary disciplines, for example, economics, physics, meteorology, chemistry, ecology, geology, and sociology. Future Research: In terms of the main initial idea, transdisciplinarity is formed as a global approach. The global approach should have a traditional institutional form: it should be a science discipline (meta-discipline) and have carriers with the transdisciplinary worldview. Training for such carriers can be organized by the universities within the limits of the systems transdisciplinarity departments and Centers of Systems Transdisciplinary Retraining for Disciplinary Specialists. Thus, it is reasonable to initiate discussion for the idea to reform the disciplinary structure of the universities considering creation of such departments and centers.

This article is part of the 2024 BCLT-BTLJ-CMTL Symposium.  Angela R. Riley When my tribe, the Citizen Potawatomi Nation of Oklahoma (CPN), established an Eagle Aviary to protect and care for injured eagles that could no longer survive in the wild, it did so with a few goals in mind. ...

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This article is part of the 2024 BCLT-BTLJ-CMTL Symposium.  Leo Yu The national security concerns surrounding TikTok appear straightforward: it is China. To many policymakers and scholars, the mere connection to China warrants severe measures, including either divestment to an American firm or a complete shutdown. What renders China’s involvement ...

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[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 ...

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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.

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

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.

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.

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

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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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 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 [...]

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, [...]

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.

Prior research on mergers and acquisitions (M&As) has substantially advanced our understanding of how isolated acquirer- and deal-specific factors affect abnormal returns. However, investors are likely to perceive and evaluate M&As holistically—that is, as complex configurations (i.e., Gestalts) of characteristics, rather than as a list of independent factors. Yet, extant M&A literature has not addressed why and how configurations of factors elicit positive or negative reactions. In other words, overlooking the interdependent nature of factors known to influence acquisition success has limited our understanding of both M&As and investor judgment. Taking an inductive approach to addressing this important issue, this study relies on fuzzy set methodology. Our results provide compelling evidence that investor perceptions of M&A announcements are not only configurational in nature but also characterized by equifinality - or the presence of multiple paths to success - and asymmetric causality - that is, configurations that represent bad deals are not simply a mirror image of good deals, but differ fundamentally. By constructing a typology of "good" and "bad" deals as perceived by market participants, we develop a mid-range theory of M&A stock market performance. As such, this study offers novel theoretical and empirical insights to scholars, and implications for practitioners.

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

Using data from 607 subjects organized in 161 teams (84 laboratory teams and 77 organizational field teams), we examined how leader humility influences team interaction patterns, emergent states, and team performance. We developed and tested a theoretical model arguing that when leaders behave humbly, followers emulate their humble behaviors, creating a shared interpersonal team process (collective humility). This collective humility in turn creates a team emergent state focused on progressively striving toward achieving the team's highest potential (collective promotion focus), which ultimately enhances team performance. We tested our model across three studies wherein we manipulated leader humility to test the social contagion hypothesis (Study 1), examined the impact of humility on team processes and performance in a longitudinal team simulation (Study 2), and tested the full model in a multistage field study in a health services context (Study 3). The findings from these lab and field studies collectively supported our theoretical model, demonstrating that leader behavior can spread via social contagion to followers, producing an emergent state that ultimately affects team performance. Our findings contribute to the leadership literature by suggesting the need for leaders to lead by example, and showing precisely how a specific set of leader behaviors influence team performance, which may provide a useful template for future leadership research on a wide variety of leader behaviors.

Drawing on theories of behavioral decision making and situational strength, we developed and tested a multilevel model that explains how the performance outcomes of personal initiative tendency depend on the extent of alignment between organizational control mechanisms and proactive individuals' risk propensities. Results from a sample of 383 middle managers operating in 34 business units of a large multinational corporation indicated that risk propensity weakens the positive relationship between personal initiative tendency and job performance. This negative moderating effect was further amplified when middle managers receive high job autonomy but was attenuated in business units with a strong performance management context. We discuss the implications of these findings for research on proactivity, risk taking, and organizational control.

The law of requisite variety is widely employed in management theorizing, and is linked with core strategy themes such as contingency and fit. We reflect upon requisite variety as an archetypal borrowed concept. We contrast its premises with insights from institutional and commitment literatures, draw propositions that set boundaries to its applicability, and review the ramifications of what we term "complexity misalignment." In this way, we contradict foundational assumptions of the law, problematize adaptation- and survival-centric views of strategizing, and theorize the role of human agency in variously complex regimes.

This article examines the conditions under which organizations publicly respond to unfavorable consumer evaluations that challenge their market identity. Because organizations' market identities are certified by expert evaluations, consumers' devaluations that challenge these expert evaluations represent an identity threat. However, organizations do not always react to consumers' devaluations because of the risks associated to public responses. Hence, we first predict that organizations are more likely to respond to severe devaluations than to weaker ones; second, we propose that organizations, when faced with severe devaluations, are more likely to craft responses that justify their actions and behaviors. We further contend that, for any market identity under consideration, an organization's reputation amplifies these relationships. Analyses of a dataset of London hoteliers' responses to online reviews posted on TripAdvisor during the period 2002-2012 lend substantial support to our hypotheses.

Despite the proliferation of strategy process and practice research, we lack understanding of the historical embeddedness of strategic processes and practices. In this paper, we present three historical approaches with the potential to remedy this deficiency. First, realist history can contribute to a better understanding of the historical embeddedness of strategic processes; in particular, comparative historical analysis can explicate the historical conditions, mechanisms, and causality in strategic processes. Second, interpretative history can add to our knowledge of the historical embeddedness of strategic practices, and microhistory can specifically help to understand the construction and enactment of these practices in historical contexts. Third, poststructuralist history can elucidate the historical embeddedness of strategic discourses, and genealogy can in particular increase our understanding of the evolution and transformation of strategic discourses and their power effects. Thus, this paper demonstrates how in their specific ways historical approaches and methods can add to our understanding of different forms and variations of strategic processes and practices, the historical construction of organizational strategies, and historically constituted strategic agency.

This paper examines when firms pursue structural realignment through the recombination of business units. Our results refine and extend contingency theory and studies of organization design by drawing on theories of decision avoidance and delay to describe conditions when firms pursue or postpone structural realignment. Our empirical analysis of 46 firms from 1978 to 1997 operating within the U.S. medical device and pharmaceutical sectors demonstrates that while decision makers initiate structural recombination during periods of industry growth (i.e., munificence), they reduce their recombination efforts during periods of industry turbulence (i.e., dynamism) and managerial turbulence (i.e., growth in top management team size). We also find evidence that firms delay realignment and bide their time for better environmental conditions of declining turbulence and industry growth before pursuing more structural realignment. Together, these findings suggest that decision makers often delay initiating structural recombination until they can effectively process information and assess how structural changes will help them realign the organization to the environment.

The work life of misfits - employees whose important values are incongruent with the values of their organization - represents an under-researched area of the person-environment fit literature. The unfortunate reality is that these individuals are likely to be disengaged and unproductive at work. In this manuscript, we entertain the possibility that employees can protect themselves from this situation if they engage in alternative actions that supplement the fundamental needs that go unmet from value incongruence. We integrate theorizing about the motivational role of need fulfillment and work/non-work behaviors in order to examine whether two actions in particular - job crafting and leisure activity - can potentially mitigate the negative effects of value incongruence on employee performance. In a field study of employees from diverse organizations and industries, the results suggest that both job crafting and leisure activity indeed act as a buffer, mitigating the otherwise negative effects of value incongruence on employee engagement and job performance (both task performance and citizenship behavior).

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.

The number of constructs developed to assess workplace aggression has flourished in recent years, leading to confusion over what meaningful differences exist (if any) between the constructs. We argue that one way to frame the field of workplace aggression is via approach/avoidance principles, with various workplace aggression constructs (e.g., abusive supervision, supervisor undermining, and workplace ostracism) differentially predicting specific approach or avoidance emotions and behaviors. Using two multi-wave field sample of employees, we demonstrate the utility of approach/avoidance principles in conceptualizing workplace aggression constructs, as well as the processes and boundary conditions through which they uniquely influence outcomes. Implications for the workplace aggression literature are discussed.

Resource dependence theorists argue that boards of directors with political capital can benefit focal firms by reducing uncertainty and providing preferential resources. Here, we develop theory regarding the downside of board political capital. As the principal-principal agency problem characterizes many parts of the world, we argue that board political capital can exacerbate this problem by enabling large blockholders to undertake more appropriation of firm wealth. Further, we explore how this enabling effect is moderated by ownership-, industry-, and environment-level contingencies. We find empirical support for our arguments using 32,174 directors in 1,046 Chinese listed firms over the period 2008 - 2011. Our study sheds light on new ways in which resource dependence and agency theories can be integrated to advance the extant research on board governance and corporate political strategy.

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

This book serves as a comprehensive guide for individuals interested in mobile robotics, including: (i) novices interested in programming simple simulated robots; (ii) individuals with basic knowledge of robotics, that is, intermediate learners, who seek to know

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

I had one big question after taking on this review: How relevant is this book with the advent of large language models (LLMs)? In the past two years, the launches of OpenAI’s GPT and Google’s Gemma, amongst others, have severely disrupted the study of natural language

Welsh’s article explores how artificial intelligence (AI) developments may redefine the landscape of the field of software development and make traditional coding methodologies obsolete. Readers should find it interesting, as it forecasts the potential impact

The NEXTORCH PIONEER PROFESSIONAL MULTI TOOL is a stout multi-tool that can hold up to tough tasks and offers a few features others don't.

President-Elect Donald Trump reaffirmed his commitment to protecting the Second Amendment by announcing his push for national concealed carry reciprocity.