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Although previous research has investigated how performance feedback may affect firms’ strategic actions, their findings has been inconsistent. The relationship between performance feedback and firms’ strategic activities thus appears complex. Moreover, the authors contend that it may vary with the measurement strategies employed (i.e. social or historical feedback, operationalizations of strategic actions or accounting- and market-based performance indicators) and the national contexts.

Therefore, the current article presents a comprehensive meta-analysis of prior research, including 1,637,817 sample observations from 101 studies that span more than 18 countries.

The results indicate that (1) performance that are below or above aspirational levels generally has a positive relationship with firms’ strategic actions; (2) these relationships are contingent on the implementation forms taken by the key variables, such as performance feedback, strategic actions and performance indicators; and (3) the relationships are much stronger in countries where managerial discretion is greater.

The findings contribute to the clarification of long-standing theoretical and empirical debates regarding the relationship between performance feedback and strategic actions, as well as some pertinent directions for future research.

Social factors can shape economic decisions. Corporate governance (CG) studies and guidelines usually neglect that the chief executive officer (CEO) and board members may be socially tied. This study investigates the effects of social ties between the CEO and board members on earnings management (EM).

The authors run a series of regressions using a sample of Brazilian companies listed on the Brazilian Stock Exchange [B]³ between 2011 and 2017 to assess the effect of the social ties between the CEO and board members on EM using a social ties index. The authors also employ five robustness tests to verify the consistency of results, including alternative proxies of EM and social ties and an estimation using fixed effects.

After developing and computing a social ties index between the CEOs and members of the board of directors (BD) and the fiscal council (FC), the study’s findings indicate that a significant level of social ties between the CEO and BD has a negative impact on EM. However, for FC members, the authors found non-significant results.

Unlike previous studies, the authors built a social tie index (STI) from five elements of social ties assessed in an environment with a two-tier board system. Results show that elements of social interactions and personal relationships can benefit the company, as a CEO's level of social ties with the BD reduces EM practices.

This study evaluates the effect of the media image content of business to business (B2B) organizations to accelerate social media engagement. It highlights the importance of strategically designing image content for business marketing strategies.

This study designed a computation extensive research model based upon the stimulus-organism-response (SOR) theory using 39,139 Facebook posts of 125 organizations selected from Fortune 500 firms. Attributes from images and text were estimated using deep learning models. Subsequently, inferential analysis was established with ordinary least squares regression. Further machine learning algorithms, like support vector regression, k-nearest neighbour, decision tree and random forest, are used to analyze the significance and robustness of the proposed model for predicting engagement metrics.

The results indicate that the social media (SM) image content of B2B firms significantly impacts their social media engagement. The visual and linguistic attributes are extracted from the image using deep learning. The distinctive effect of each feature on social media engagement (SME) is empirically verified in this study.

This research presents practical insights formulated by embedding marketing, advertising, image processing and statistical knowledge of SM analytics. The findings of this study provide evidence for the stimulating effect of image content concerning SME. Based on the theoretical implications of this study, marketing and media content practitioners can enhance the efficacy of SM posts in engaging users.

In this age of extremely well-developed social media, it is necessary to detect any change in the corporate image of an enterprise immediately so as to take quick action to avoid the wide spread of a negative image. However, existing survey-based corporate image evaluation methods are costly, slow and static, and the results may quickly become outdated. User comments, news reports and we-media articles on the internet offer varied channels for enterprises to obtain public evaluations and feedback. The purpose of this study is to effectively use online information to timely and accurately measure enterprises’ corporate images.

A new corporate image evaluation method was built by first using a literature review to establish a corporate image evaluation index system. Next, an automatic text analysis of online public information was performed through a topic classification and sentiment analysis algorithm based on the dictionary. The accuracy of the topic classification and sentiment analysis algorithm is then calculated. Finally, three internet enterprises were chosen as cases, and their corporate image was evaluated.

The results show that the author’s corporate image evaluation method is effective.

First, in this study, a new corporate image evaluation index system is constructed. Second, a new corporate image evaluation method based on text mining is proposed that can support data-driven decision-making for managers with real-time corporate image evaluation results. Finally, this study improves the understanding of corporate image by generating business intelligence through online information. The findings provide researchers with specific and detailed suggestions that focus on the corporate image management of emerging internet enterprises.

Even with the recognized impact organizational leaders have on the outcome of digital transformation (DT), a comprehensive scholarly understanding of the competencies that leaders must possess to lead a DT to success is lacking.

To derive and list the competencies considered by experts as necessary for managing DT, the authors recruited 18 international senior managers with relevant experience and applied the Delphi method to survey the managers. Upon the completion of three survey rounds and the authors modifying the response list until consensus was reached, 39 items were shortlisted as constituting key competencies for managing DT. Furthermore, the authors engaged in inductive theorizing to derive propositional statements using these findings.

The practitioners agreed on visionary thinking, agility, understanding the value of data, data-driven decision-making, knowledge of strategy and accepting change as the most important requirements for managing DT. Through inductive theorizing, the authors further derived that the seven discovered clusters fell into two broader competencies – behavioral and strategic – and that each behavioral competency would have varying importance depending on the country and industry that the organization operates in.

As is typical for Delphi studies that involve multiple survey rounds, the study participant response rate was moderate. The implications of this study, in finding that a variety of leadership competencies are needed to ensure successful DT, validate prior research that people, not technology, drive DT.

This study helps mitigate assumptions that successful DT processes are only possible by hiring technological experts, as doing so highlights the importance of behavioral leadership competencies.

The study is one of the first to interlink digital leadership with DT by inductively theorizing behavioral and strategic competencies. The authors also establish that contexts are vital in determining which aspects of leadership competencies are deemed most important in driving DT.

The partial least squares structural equation modeling (PLS-SEM) approach for construction management (CM) scholars has become the preferred approach for its capability of assessing the complex relationship and relaxed normality and sample size assumptions. This paper systematically maps the structure of knowledge about PLS-SEM in CM using bibliometric analysis. Also, the study employs meta-analysis to explore how data and model characteristics, model evaluation and advanced modeling techniques have been utilized in the CM domain.

This study integrated two methods: bibliometric analysis on a sample of 211 articles identified using the PRISMA framework and meta-analysis on 163 articles identified based on the availability of full-length articles and relevant information.

The results revealed the leading knowledge formation entities (countries, institutions, authors, sources and documents). Also, the study employs full content analysis to identify six research themes, and meta-analysis is used to explore the use of PLS-SEM based on the following criteria: (1) reasons for using PLS-SEM in CM, (2) data characteristics, (3) model characteristics and evaluation and (4) use of advanced modeling and analysis techniques. Further, the study uses regression analysis and identifies “advanced modeling and analysis techniques” as the critical feature responsible for the publication in a journal with high scientific prestige. Finally, the study presented the comprehensive guidelines to be used by construction management scholars who wish to use PLS-SEM in their research work.

To the author’s knowledge, it is the first study of this kind to use PLS-SEM in CM research. This study provides an extensive analysis of the Scopus database and an in-depth review of the data characteristics, model characteristics and use of advanced modeling techniques in CM research.

Due to the dominant use of the singular view of the self-categorization process in the literature, this article seeks to develop a typology, from a dyadic categorization perspective, that describes different types of prejudice and justice in the organization based on one's self-categorization and others' categorization of one's self.

The authors develop a typology by drawing upon social identity, self-categorization and self-consistency theories.

The authors propose the following findings. First, the more an individual self-categorizes as an in-group member regardless of how others categorize the individual, the more likely the individual experiences a particular form of justice. Second, the more an individual self-categorizes as an out-group member regardless of how others categorize the individual, the more likely the individual experiences a particular form of prejudice. Finally, based on the dyadic categorization approach, the authors propose four distinct types of prejudice and justice: communal prejudice, self-induced prejudice, fantasized justice and actualized justice.

The authors advance the literature by providing a dyadic categorization view that helps describe employees' experience of prejudice or justice in the organization. Additionally, this article offers some managerial recommendations that help managers actualize true justice in the organization.

Medical diagnosis and treatment processes exhibit a high degree of variability, as during the process execution, healthcare professionals can decide on additional steps, change the execution order or skip a task. Process models can help to document and to discuss such processes. However, depicting variability in graphical process models using standardized languages, such as Business Process Model and Notation (BPMN), can lead to large and complicated diagrams that medical staff who do not have formal training in modeling languages have difficulty understanding. This study proposes a pattern-based process visualization that medical doctors can understand without extensive training. The process descriptions using this pattern-based visualization can later be transformed into formal business process models in languages such as BPMN.

The authors derived patterns for expressing variability in healthcare processes from the literature and medical guidelines. Then, the authors evaluated and revised these patterns based on interviews with physicians in a Danish hospital.

A set of business process variability patterns was proposed to express situations with variability in hospital treatment and diagnosis processes. The interviewed medical doctors could translate the patterns into their daily work practice, and the patterns were used to model a hospital process.

When communicating with medical personnel, the patterns can be used as building blocks for documenting and discussing variable processes.

The patterns can reduce complexity in process visualization. This study provides the first validation of these patterns in a hospital.

The purpose of this paper is to inform scholars and practitioners about the current body of knowledge on the role of social capital in scaling social impact since these concepts are still poorly understood and literature is fragmented despite their importance.

A systematic literature review of 27 highly relevant studies in leading journals is conducted, and the results are synthesized into an integrative theoretical framework.

The framework identifies possible dependent, independent, mediating and moderating variables which conceptualize the role of social capital in scaling social impact.

To the best of the authors’ knowledge, this is the first study to systematically map social capital’s role in scaling social impact literature with the help of an integrative theoretical framework. For researchers, this framework would help by providing a shared frame of reference to conceptualize the role of social capital in scaling social impact and identify future research directions. Practitioners can use the findings of this review as a guide while designing and implementing scaling social impact programs.

This paper aims to trace the development of Hong Kong's Happy Valley from a space associated with dangerous miasmas to the site of a racecourse, recreation ground and a series of cemeteries for the colony's foreign communities while examining the relationship between the exclusion of Chinese from Happy Valley and the notion of colonial order.

This paper makes use of empirical evidence from historical documents, such as newspapers and government records, and applies Michel Foucault's notion of the heterotopia as a theoretical model.

This paper provides insights into the relationship between space and power in the colonial setting. It demonstrates that the imposition of colonial order in Happy Valley was a process that involved the exclusion of Chinese and that the various ways in which this order was reinforced, contested and negotiated revealed it to be shallow and incomplete.

This paper sheds light on an underexamined but important colonial space in 19th and early 20th century Hong Kong and complicates the notion of colonial control.