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This study investigates the validity of Ferreira et al.’s (2020) Organizational Bullshit Perception Scale by examining its distinctiveness from similar constructs (perceptions of organizational politics, organizational cynicism, procedural justice) and its predictive validity through its relations with important organizational attitudes (organizational identification) and behaviors (counterproductive work behavior and organizational citizenship behavior). This study also examines the moderating effects of honesty–humility on the relations between organizational bullshit perception and the outcomes of counterproductive work behavior, organizational citizenship behavior and organizational identification. Finally, this study examines the incremental validity of organizational bullshit perception in predicting counterproductive work behavior, organizational citizenship behavior and organizational identification above and beyond similar constructs in an exploratory fashion.

Survey data were collected from a sample of working adults online via Amazon’s Mechanical Turk platform across two waves (final N = 323 for wave 1 and 174 for wave 2), one month apart.

The results indicate that organizational bullshit perception, as measured by Ferreira et al.’s (2020) scale, represents a distinct construct that has statistically significant relations with counterproductive work behavior, organizational citizenship behavior and organizational identification, even after controlling for procedural justice, organizational cynicism and perceptions of organizational politics. The results, however, showed no support for honesty–humility as a moderator.

These findings suggest that organizations can benefit from assessing and working to alleviate their employees’ perceptions of organizational bullshit. This construct predicts behaviors and attitudes important for organizational functioning.

This study adds to Ferreira et al.’s (2020) original work by demonstrating organizational bullshit perception’s distinctiveness from existing constructs in the literature and its implications for organizations and their employees.

This study aims to identify and deconstruct bullshit in banks' corporate social responsibility (CSR) communication to advance the management rhetoric research space, which has been characterised by an indifference to truth and meaning.

We provide a typology of bullshit phenomena overview in the banking sector and follow the McCarthy et al.'s (2020) C.R.A.P. framework from to showcase how bullshit can be comprehended, recognised, acted against and prevented.

This paper puts a spotlight on written and spoken language to detect bullshit in banks' CSR statements. It provides actionable insights into how stakeholders can act against and prevent bullshit statements from occurring in the future.

Future research is warranted to assess the use of still imagery, events and video materials in corporate communications and non-financial reporting. Further rigorous assessment of actual CSR initiatives must be undertaken to assess claimed contributions.

Monitoring mechanisms and independent assurance statements prepared by authorised third parties may strengthen the motivation and ethicality of CSR activities.

This viewpoint is the first to follow the C.R.A.P framework and critically assess indifferences towards truth in banks' CSR communications.

The purpose of this paper is to critically evaluate the state of the art of applications of organisational systematics and manufacturing cladistics in terms of strengths and weaknesses and introduce new generic cladistic and hierarchical classifications of discrete manufacturing systems. These classifications are the basis for a practical web-based expert system and diagnostic benchmarking tool.

There were two stages for the research methods, with eight re-iterative steps: one for theory building, using secondary and observational data, producing conceptual classifications; the second stage for theory testing and theory development, using quantitative data from 153 companies and 510 manufacturing systems, producing the final factual cladogram. Evolutionary relationships between 53 candidate manufacturing systems, using 13 characters with 84 states, are hypothesised and presented diagrammatically. The manufacturing systems are also organised in a hierarchical classification with 13 genera, 6 families and 3 orders under one class of discrete manufacturing.

This work addressed several weaknesses of current manufacturing cladistic classifications which include the lack of an explicit out-group comparison, limited conceptual cladogram development, limited use of characters and that previous classifications are specific to sectors. In order to correct these limitations, the paper first expands on previous work by producing a more generic manufacturing system classification. Second, it describes a novel web-based expert system for the practical application of the discrete manufacturing system.

The classifications form the basis for a practical web-based expert system and diagnostic benchmarking tool, but also have a novel use in an educational context as it simplifies and relationally organises extant manufacturing system knowledge.

The research employed a novel re-iterative methodology for both theory building, using observational data, producing the conceptual classification, and through theory testing developing the final factual cladogram that forms the basis for the practical web-based expert system and diagnostic tool.

Exploration and exploitation comprise one of the most well-known constructs in management and organization studies. However, there are three gaps in the extant literature on this topic. First, these studies focus mainly on large organizations and neglect small and medium-sized enterprises (SMEs) and new ventures. Second, when adopting a longitudinal perspective, the research typically consists of cross-sectional studies that fail to capture evolution. Third, the research focuses more on the role of antecedents and mediators of strategies that pursue exploration and exploitation than on the practices that embody such goals. In this chapter, we address these three gaps and complement the previous literature with a study of the growth of an SME from start-up to sale over a 19-year period (1993–2011). We depict the evolution of exploration and exploitation over time through an analysis of management system practices that employs a longitudinal perspective. We analyze the different roles that management systems have played in various stages of the growth paths of the organization. We show that the role of management systems in shaping exploration and exploitation only loosely depends on the design of these systems. The same management systems can fulfill an explorative function in one stage and an exploitative function in another, depending on how such systems are used. Conversely, across stages, the role of management systems typically changes from exploration to exploitation.

The purpose of this paper is to introduce, apply and compare how artificial intelligence (AI), and specifically the IBM Watson system, can be used for content analysis in marketing research relative to manual and computer-aided (non-AI) approaches to content analysis.

To illustrate the use of AI-enabled content analysis, this paper examines the text of leadership speeches, content related to organizational brand. The process and results of using AI are compared to manual and computer-aided approaches by using three performance factors for content analysis: reliability, validity and efficiency.

Relative to manual and computer-aided approaches, AI-enabled content analysis provides clear advantages with high reliability, high validity and moderate efficiency.

This paper offers three contributions. First, it highlights the continued importance of the content analysis research method, particularly with the explosive growth of natural language-based user-generated content. Second, it provides a road map of how to use AI-enabled content analysis. Third, it applies and compares AI-enabled content analysis to manual and computer-aided, using leadership speeches.

For each of the three approaches, nine steps are outlined and described to allow for replicability of this study. The advantages and disadvantages of using AI for content analysis are discussed. Together these are intended to motivate and guide researchers to apply and develop AI-enabled content analysis for research in marketing and other disciplines.

To the best of the authors’ knowledge, this paper is among the first to introduce, apply and compare how AI can be used for content analysis.

Digitally oriented firms are faced with new opportunities and risks in today’s ever-changing world. Drawing upon organisational entrainment theory, this study investigates how supply chain (SC) entrainment improves the effects of digital orientation on firm performance through absorbing risks and exploiting opportunities.

Survey data were collected from 307 Chinese manufactures and analysed using structural equation modelling and regression analysis.

The results show digital orientation absorbs risk through evoking three SC entrainment dimensions (i.e. internal entrainment, entrainment with customers and entrainment with suppliers). Entrainment with customers and suppliers mediate the relationship between internal entrainment and firm performance. An opportunity exploitation mechanism is evidenced by the moderating effects of internal and external entrainment on the relationship between digital orientation and firm performance.

The empirical findings provide timely insights for managers to fully harness the benefits of digital orientation by using SC entrainment, i.e. to match the tempo and pace of internal and external cyclical activities to reduce the risks and increase the benefits of adopting advanced digital technologies. The authors show managers how to adjust their organization’s actions to keep tempo and synchronous phase with their SC partners.

The study introduces and conceptualizes a construct (i.e. SC entrainment) to understand how risks and opportunities arising from digital transformation can be addressed to maximize the value of advanced digital technologies.

This paper proposes optimisation models to evaluate and examine the selling of extended warranty policies in terms of improved profits in producing/marketing remanufactured products. These models are numerically solved using a quadratic programming solution approach and implemented in the decision support system (DSS).

The purpose of this paper is to develop the optimisation models for a DSS and evaluate different warranty policies for buyers.

This study has demonstrated the flexibility and usefulness of a model-driven DSS for the quality and warranty management, which is applied to examine and evaluate different configurations (i.e. component reuse, rebuild and recycle) for remanufactured products and propose the selling of extended warranty policies for buyers.

The developed model-driven DSS can assist manufacturers to select and increase the number of components, e.g. to be reused, rebuilt, and recycled for producing a remanufactured product and propose suitable warranty policies for buyers. However, this study focusses only on the evaluation of warranty policies for specific remanufactured products in a DSS, i.e. types of air compressors for production operations in manufacturing industry.

This study developed optimisation models to be used in a DSS for proposing the selling of extended warranty of a remanufactured product to improve customer satisfaction and maximise the gained profits for manufacturers.

Introduces a framework for benchmarking organisations – “evolutionary benchmarking”. Discusses the concept and operation of the framework, along with its proposed advantages in aiding benchmarking. The benchmarking approach proposed utilises an evolutionary classification method called cladistics. Uses an example classification of automotive assembly plants to show how the proposed framework helps benchmarking in terms of: providing a framework for representing benchmarking information; enhancing the quality and validity of the information according to the classification rules of parsimony, congruence and homology; and providing a comparison that indicates how the practices should be adopted. This framework is considered to be useful to researchers who study benchmarking methodologies and those that categorise the findings of benchmarking studies.

The aim of this chapter is to explore employee behaviors and expectations of the role of social media when searching for jobs, to offer recruiters and companies valuable insights to design and manage appropriate web-based employer branding and recruitment strategies.

The research strategy is based on semi-structured in-depth interviews involving 34 central informants: talented Gen Yers and social media recruitment experts and mangers. The project focuses on the Italian context, an exemplary country with the highest social media penetration rate.

The results demonstrate the “bounded” popularity of social media as a recruitment tool among Gen Yers who implement up to five active and passive behaviors, albeit not all widespread, according to varying patterns and using different social media for different purposes: receiving, seeking, sharing, leading, and experiencing. Gen Yers, with aims that vary in line with various staffing phases, collect and share rumors and voices from both internal and controlled organizational sources but also, and above all, from external and organizational sources that companies do not control directly.

Social media seem to offer appealing and valuable opportunities to attract and engage talented young individuals, sustaining the quality, quantity, and fairness of employment relationships. Conversely, they also involve some organizational risks and costs. The chapter offers some managerial cautions and advocates a radical change in the prevalent HRM mindset for the improved management of transparency that social media solutions entail.

Results contribute in understanding how social media can better sustain employer branding and recruitment activities, especially considering the needs and expectations of talented young employees and professionals in the Italian context. Italy is an emblematic context, where the social media potential appears to be extremely interesting, considering its high rate of social media penetration.