Shared service implementation in multidivisional organizations: A meta-synthesis study

Richter, Philipp Clemens; Brühl, Rolf

doi:10.1177/0306307020914139

Cited by 10 publications

(12 citation statements)

References 88 publications

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“…Turning to the impact of organizational size, in our study, there was no substantial impact of organizational size on management tool usage. This might be a bit surprising, as organizational size is often considered in studies of single management tools [33,35,81]. For instance, [73] examined how the business orientation of manufacturing enterprises determines the utilization of lean production.…”

Section: Discussionmentioning

confidence: 99%

“…There are no studies that delineate the impact of typical organizational drivers on management tool usage. In terms of organizational drivers that may influence the utilization of management tools, the selection is narrower compared to personal drivers, but it is clear that the studies that emphasize management tools [1,27,33,35,39,76,81] (see also Table 1) frequently consider organizational size as important in their surveys. This is a clear indication that organizational size is considered to have an impact on the studied management tools.…”

Section: Organizational Driversmentioning

confidence: 99%

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What Drives the Usage of Management Tools Supporting Industry 4.0 in Organizations?

Nedelko

2021

Sensors

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The main purpose of this study was to examine how personal and organizational drivers influence the utilization of management tools aimed at supporting organizational working in Industry 4.0 settings. We built our research upon the recognized importance of management tools for organizational working under Industry 4.0 settings and explored the key personal and organizational drivers of management tool usage. Calculations were performed based on the responses of 222 employees working in organizations across Europe. The results revealed that, among personal drivers, a higher level of education leads to significantly higher usage of six sigma, rapid prototyping, outsourcing, customer relationship management, knowledge management, core competencies, and strategic planning. More experienced employees use significantly more six sigma, total quality management, supply chain management, knowledge management, and core competences than their less experienced peers. The impact of organizational drivers is substantially weaker, where only industry shows significant influence, indicating that lean production, six sigma, and supply chain management are used more in manufacturing than in service organizations. Gender, one’s position in the organization, and the organization size do not play a substantial role in management tool usage. Managers should recognize the role of personal and organizational drivers of management tool usage in order to more quickly implement Industry 4.0 principles in organizations.

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Section: Discussionmentioning

confidence: 99%

Section: Organizational Driversmentioning

confidence: 99%

What Drives the Usage of Management Tools Supporting Industry 4.0 in Organizations?

Nedelko

2021

Sensors

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“…Following the capability-based view (CBV; e.g. Helfat et al , 2007), scholars explain that SSCs represent firms' operational capabilities for employing and leveraging corporate activities (Richter and Brühl, 2021). Operational capabilities are those capacities that “perform an activity on an on-going basis using more or less the same techniques on the same scale to support existing products and services for the same customer population” (Helfat and Winter, 2011 p. 1244).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, scholars assert that SSCs can increase companies' financial performance (e.g. Aksin and Masini, 2008), particularly in contrast to other sorts of operational capabilities such as multibusiness forms (Richter and Brühl, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Second, studies have consistently found that SSC characteristics change during the implementation journey. For instance, Richter and Brühl (2021) provide a meta-synthesis model that shows iterative development of SSC characteristics throughout the implementation. Although research has recognized those dynamics, the development of SSC configurations and their reasons are scarcely investigated and, hence, are theoretically little understood.…”

Section: Introductionmentioning

confidence: 99%

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Shared services: configurations, dynamics and performance

Richter

2021

BJM

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PurposeThe purpose of this paper is to explore the configurations of shared service center (SSC) characteristics, their performance implications and the dynamics of SSC configurations during their implementation.Design/methodology/approachThis study uses the capability-based view and configurational approach to suggest a model that explains performance outcomes of shared service configurations. Survey data are analyzed with a cluster analysis to examine shared service configurations in distinct stages of implementation. Moreover, a lifecycle framework of shared service configurations is conceptualized.FindingsThis study considers shared service configurations as operational capabilities to run corporate support activities. The purpose is to examine the configurations of those capabilities, their performance implications and their dynamics during the shared service implementation.Practical implicationsThe findings help senior executives to effectively implement and transform shared services when deciding to renew corporates' support activities.Originality/valueThis study is one of the first that conceptually and empirically explores shared service configurations, performance and configurational dynamics.

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Machine learning in smart production logistics: a review of technological capabilities

Flores-García,

Hoon Kwak,

Jeong

et al. 2024

International Journal of Production Research

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Recent publications underscore the critical implications of adapting to dynamic environments for enhancing the performance of material and information flows. This study presents a systematic review of literature that explores the technological capabilities of smart production logistics (SPL) when applying machine learning (ML) to enhance logistics capabilities in dynamic environments. This study applies inductive theory building and extends existing knowledge about SPL in three ways. First, it describes the role of ML in advancing the logistics capabilities of SPL across various dimensions, such as time, quality, sustainability, and cost. Second, this study demonstrates the application of the component technologies of ML (i.e. scanning, storing, interpreting, executing, and learning) to attain superior performance in SPL. Third, it outlines how manufacturing companies can cultivate the technological capabilities of SPL to effectively apply ML. In particular, the study introduces a comprehensive framework that establishes the technological foundations of SPL, thus facilitating the successful integration of ML, and the improvement of logistics capabilities. Finally, the study outlines practical implications for managers and staff responsible for the planning and execution of tasks, including the movement of materials and information in factories.

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Shared service implementation in multidivisional organizations: A meta-synthesis study

Cited by 10 publications

References 88 publications

What Drives the Usage of Management Tools Supporting Industry 4.0 in Organizations?

What Drives the Usage of Management Tools Supporting Industry 4.0 in Organizations?

Shared services: configurations, dynamics and performance

Machine learning in smart production logistics: a review of technological capabilities

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