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Leveraging People Analytics for an Adaptive Complex Talent Management System

DOI:10.1016/j.procs.2020.02.269
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Kristin Saling at United States Army
Kristin Saling
Michael D. Do
Michael D. Do
Michael D. Do
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Abstract and Figures

Data analytics inform many facets of our everyday life, from Netflix recommendations to the ads that pop up on our social media feeds. This same technology can make an enormous difference in human resource and talent management enabling individuals to market their skillsets and organizations to describe their job requirements down to a granular level of detail in the hopes that searches, optimization algorithms, and simple recommendation engines can guide them towards an optimal decision for talent management – the right person in the right job at the right time. While these analytic tools are important to optimizing decisions, it is not always evident where to apply them for the best possible effect. The Army advanced analytics in a way that allows them to forecast their ability to fill critical job requirements over time by forecasting new acquisitions, promotions, and losses at the aggregate level. However, that system falls far short of being able to match people to positions in an optimal manner and results in long lag times when it comes to meeting emerging requirements. A new data collection system identifying both unit-required and individual-possessed knowledge, skills, and behaviors (KSBs) will enable the Army to make forecasts and fill positions much more rapidly (along with assigning the best person to the position) provided the data is available to decision makers at the right time to best support talent management decisions. This paper outlines the new structure of this complex human resource (HR) system from data collection to analytic tools along with showing how modeling this system illustrates an adaptive complex system based on data engineering.
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Procedia Computer Science 168 (2020) 105–111
1877-0509 © 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems Conference with Theme: Leveraging AI
and Machine Learning for Societal Challenges
10.1016/j.procs.2020.02.269
10.1016/j.procs.2020.02.269 1877-0509
© 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientic committee of the Complex Adaptive Systems Conference with Theme: Leveraging AI and Machine
Learning for Societal Challenges
Available online at www.sciencedirect.com
ScienceDirect
Procedia Computer Science 00 (2019) 000–000
www.elsevier.com/locate/procedia
1877-0509 © 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems Conference with Theme: Leveraging AI and Machine Learning for
Societal Challenges
Complex Adaptive Systems Conference with Theme:
Leveraging AI and Machine Learning for Societal Challenges, CAS 2019
Leveraging People Analytics for an Adaptive Complex Talent Management
System
Kristin C. Saling
a
*, Michael D. Do
a
a
Army Talent Management Task Force, 300 Army Pentagon, Washington, DC 20310
Abstract
Data analytics inform many facets of our everyday life, from Netflix recommendations to the ads that pop up on our social media feeds. This same
technology can make an enormous difference in human resource and talent management enabling individuals to market their skillsets and
organizations to describe their job requirements down to a granular level of detail in the hopes that searches, optimization algorithms, and simple
recommendation engines can guide them towards an optimal decision for talent management – the right person in the right job at the right time.
While these analytic tools are important to optimizing decisions, it is not always evident where to apply them for the best possible effect.
The Army advanced analytics in a way that allows them to forecast their ability to fill critical job requirements over time by forecasting new
acquisitions, promotions, and losses at the aggregate level. However, that system falls far short of being able to match people to positions in an
optimal manner and results in long lag times when it comes to meeting emerging requirements. A new data collection system identifying both unit-
required and individual-possessed knowledge, skills, and behaviors (KSBs) will enable the Army to make forecasts and fill positions much more
rapidly (along with assigning the best person to the position) provided the data is available to decision makers at the right time to best support talent
management decisions. This paper outlines the new structure of this complex human resource (HR) system from data collection to analytic tools
along with showing how modeling this system illustrates an adaptive complex system based on data engineering.
© 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems Conference with Theme: Leveraging AI and
Machine Learning for Societal Challenges
Keywords: People analytics; data analytics; predictive analytics; AI; marketplace; talent management
*Tel.: 808-783-3279
E-mail address: Kristin.c.saling.mil@mail.mil
Tel.: 520-820-8002
E-mail address: michael.d.do..mil@mail.mil
Available online at www.sciencedirect.com
ScienceDirect
Procedia Computer Science 00 (2019) 000–000
www.elsevier.com/locate/procedia
1877-0509 © 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems Conference with Theme: Leveraging AI and Machine Learning for
Societal Challenges
Complex Adaptive Systems Conference with Theme:
Leveraging AI and Machine Learning for Societal Challenges, CAS 2019
Leveraging People Analytics for an Adaptive Complex Talent Management
System
Kristin C. Saling
a
*, Michael D. Do
a
a
Army Talent Management Task Force, 300 Army Pentagon, Washington, DC 20310
Abstract
Data analytics inform many facets of our everyday life, from Netflix recommendations to the ads that pop up on our social media feeds. This same
technology can make an enormous difference in human resource and talent management enabling individuals to market their skillsets and
organizations to describe their job requirements down to a granular level of detail in the hopes that searches, optimization algorithms, and simple
recommendation engines can guide them towards an optimal decision for talent management – the right person in the right job at the right time.
While these analytic tools are important to optimizing decisions, it is not always evident where to apply them for the best possible effect.
The Army advanced analytics in a way that allows them to forecast their ability to fill critical job requirements over time by forecasting new
acquisitions, promotions, and losses at the aggregate level. However, that system falls far short of being able to match people to positions in an
optimal manner and results in long lag times when it comes to meeting emerging requirements. A new data collection system identifying both unit-
required and individual-possessed knowledge, skills, and behaviors (KSBs) will enable the Army to make forecasts and fill positions much more
rapidly (along with assigning the best person to the position) provided the data is available to decision makers at the right time to best support talent
management decisions. This paper outlines the new structure of this complex human resource (HR) system from data collection to analytic tools
along with showing how modeling this system illustrates an adaptive complex system based on data engineering.
© 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems Conference with Theme: Leveraging AI and
Machine Learning for Societal Challenges
Keywords: People analytics; data analytics; predictive analytics; AI; marketplace; talent management
*Tel.: 808-783-3279
E-mail address: Kristin.c.saling.mil@mail.mil
Tel.: 520-820-8002
E-mail address: michael.d.do..mil@mail.mil
106 Kristin C. Saling et al. / Procedia Computer Science 168 (2020) 105–111
Saling, Do / Procedia Computer Science 00 (2019) 000–000
1. Introduction – From Industrial to Data Driven
The U.S. Army’s senior leaders challenged the Army Talent Management Task Force (ATMTF) to examine data and analytical
tools that enable a better understanding of the individuals’ knowledge, skills, behaviors, and assignment preferences (KSB-Ps) that
will optimally employ them across the force and increase readiness. This concept requires the Army to leverage existing data
collection and analytical efforts as well as integrating them with new data collection and analytical programs that provide a more
holistic view of an individual’s KSB-Ps relative to their potential for employment, development, recruitment, and retention. In order
to optimize officer KSB-Ps, the ATMTF must identify any gaps in data collection and analytic capability as well as providing
recommendations that advances the state of human capital analytics.
Organizations throughout the world have leveraged advanced data analytics to solve complex problems, which provided new
opportunities for organizational success. Common data analytic applications include fraud detection, optimizing projected return on
investment, improving the efficiency of operations to include predictive maintenance and depot level forecasting, and reducing risks.
Multiple Army partners in industry recommend programs and applications to execute data analytics, but the Army already executes
advanced analytics in multiple areas.
Advanced analytical efforts in the Army are already being leveraged to optimize retention, detect insider threat, improve the
processing of security clearances, reduce the amount of time required for medical evaluation and out-processing, and implementing
predictive maintenance programs in vehicle fleets. In the area specific to personnel and human capital, the Army uses advanced
analytics to assess how Soldiers react to crisis situations such as in virtual reality training environments, improve medical treatments,
and analyzing significant amounts of data to determine potential indicators for alcohol and drug abuse, sexual assault, post-traumatic
stress disorder, suicide, and a number of other potentially destructive behaviors. However, none of these analytics are being used to
impact personnel and talent management decisions.
The Army applied advanced analytics in the personnel domain at a slower rate than in other areas. The current industrial age
Army personnel system functions in a way that modernization efforts does not lend itself to change, leading to an “if it isn’t broken,
don’t fix it” mentality. In addition, human behavior is largely complex and the amount of data required to develop algorithms
capable of actually predicting behavior is extensive and complex.
General James McConville, the 40
th
Chief of Staff of the Army, stated in a response to the Senate Armed Services Committee that
he considers personnel to be one of the six critical domains that the Army must modernize. “The Army will
accomplish…modernization through its greatest asset and most important weapons system – people”
1
. With his support, the Army
has the opportunity to transform its personnel system from an industrial age legacy to an information age cutting edge talent
management system.
In order to make recommendations on where best to implement changes to the existing system and modernize it for future efforts,
the authors have taken a “complex adaptive systems” approach to visualizing the individual life cycle of an Army employee. T his
allows the task force to visualize influence points, decision points, and other critical points in the employee life cycle, which should
be informed by data and advanced analytics.
1.1. Moving to an Information Age Personnel System
The Army possesses a massive amount of data on its most valuable asset – its people, but the compartmentalization of data does
not allow for effective execution of synchronized advanced analytics necessary to run an effective talent management program.
Compared to industry partners, the Army has a much wider authority for collecting information and conducting research. However,
the data is contained in discrete databases belonging to separate organizations that often do not share or are unaware of the others’
existence. This results in a data poor environment to operate and it’s as if the Army did not collect the data in the first place.
The G-1’s Technology and Business Architecture Integration (TBAI) office estimates that there are over 180 data systems housing
personnel data registered to the Army. The Human Capital Big Data (HCBD) initiative and G-1 Omnibus Agreement enacted in
2016 was intended to overcome this problem. The development of the Integrated Pay and Personnel System – Army (IPPS-A) will
incorporate and replace 57 of these personnel data systems scheduled for released for Active Component use (Release 3, June 2021)
in the near future has already become the de facto authoritative data source for much of the National Guard and Army Reserve
components’ data. HCBD and the Research Facilitation Lab (RFL) have been working with the IPPS-A team to ensure timely and
complete data sharing to enable analysis.
The Army’s HCBD enclave in the RFL’s Person-event Data Environment (PDE) stores much of this data with additional legal,
medical, and educational databases layered within the PDE. RFL and HCBD leadership have designed an interface and a governance
system to enable secure access to this data. However, the data quality is insufficient for analysis. Data cleaning and synchronizing
efforts are underway to make this data useful.
The biggest obstacles to advancing predictive analytics in the Army are data use permissions and data quality. While the Army
possesses large amounts of data, that data is often stored in separate databases with incompatible formats that need to be updated in
Kristin C. Saling et al. / Procedia Computer Science 168 (2020) 105–111 107
Saling, Do / Procedia Computer Science 00 (2019) 000–000
order to be useful. Many organizations view their data as proprietary and while the Army must balance data sharing with effectively
safeguarding data, the owning agencies must provide a source for the data to ensure its proper analysis. However, many are overly
restrictive of their data and thus, reluctant to share source data.
The Army could also benefit from the use of technological solutions to many of these problems. While the Army possesses
analysts capable of building these technological solutions, there is typically insufficient time to conduct the research necessary to
develop technological solutions. Many of the existing solutions have been created as an “ad hoc” science project due to other
primary duties, which leave little time for these projects. The Army must prioritize advanced analytics and develop a unified effort
toward developing these technical solutions in order to achieve success.
1.2. Modeling the System
The systems engineering (SE) discipline has great utility in many different academic domains. For example, application of
systems thinking approach and its modeling techniques link extremely well to existing processes to provide a holistic understanding
of complex topics. In the human resources domain where system processes and data appear complexed, SE provides a method for
breaking down these complex systems into sub-systems where individual functions and their interdependencies can be analyzed
without losing any critical linkages. These sub-systems and their linkages can be traced back to the overall system.
The authors studied different system modeling techniques to identify what tools might be best to decompose the system for
analysis. They elected to functionally decompose the Army’s personnel system through a technique called systemigrams. This
process allows for conceptualization of the Army’s personnel system to identify where data is currently being used to drive systems
that could be improved through the use of people analytics and identify where decisions currently being made that could be improved
through artificial intelligence.
2. Methodology
2.1. Characteristics of a Complex Adaptive System
Complex systems, quite simply, consist of large numbers of interacting elements or sub-systems. When the number of interacting
systems becomes significantly large, typically applied mathematical tools lose their ability to model or predict the behavior of these
systems. Adaptive complex systems are characterized by complex behaviors growing from the numerous and often non-linear
interactions of components known as emergent behaviors, which are difficult to mathematically explain and impossible to predict.
2
Researchers have investigated into the idea of an organization as a complex adaptive system. Schnieder and Somers published
literatures on the implication of this work in leadership,
3
Dooley wrote about its application in understanding and potentially
influencing organizational change,
4
and Begun, Zimmerman, and Dooley looked at it specifically in the realm of health care
organizations.
5
Work in personnel systems is scarce although researchers working in Strategic Human Resource Management (SHRM) have
examined similar analytical techniques for breaking down the complex decision strata of HR. This research started with a modified
version of Tsui and Gomez-Mejia’s model of human resource effectiveness
7
, which tied the effectiveness of decisions and activities
of HR managers to the effectiveness of the HR function and the effectiveness of the organization [see Figure 1]. A further
decomposition of these functions led the authors to the selected model (systemigram) used to develop their understanding of the
Army’s system.
108 Kristin C. Saling et al. / Procedia Computer Science 168 (2020) 105–111
Saling, Do / Procedia Computer Science 00 (2019) 000–000
While this model is rudimentary, it allowed the authors to start developing a model to analyze decisions and activities of human
resource managers under the current system. The authors then began documenting impacts to those decisions with external
contextual factors such as the economy, perceived and actual availability of critical skills, changing decisions on the skills required
by units made by other commanders, and other activities.
2.2 Modeling Talent as a Complex Adaptive System
As mentioned earlier, the authors combined the organizational effectiveness model’s perspective with the life cycle of an Army
employee – officer, enlisted Soldier, or civilian hire. The ATMTF has done a majority of its work to date on examining the life cycle
of Army officers, so the authors began modeling that life cycle using a systemigram technique.
The authors based their initial model upon interviews done with human resources professionals throughout the Army Deputy
Chief of Staff for Personnel (G-1), but found patterns emerging consistent with the human capital life cycle focused on talent initially
proposed by Wardynski, Lyle, and Colarusso: professional teams first acquire their talent, seek to employ that talent effectively,
develop that talent in order to maintain a leading edge, and seek to retain the best talent
7
. They found that they could effectively
overlay these domains over the functions relayed by human resource professionals and better determine 1) where critical decision
spaces lay, and 2) where interfering factors proposed by Wardynski, Lyle, and Colarusso were most likely to influence.
The initial draft of the talent management systemigram contained activities with linkages overlaid by the talent focused human
resources functions (see Figure 2).
Fig. 1. Organizational Effectiveness Model
Kristin C. Saling et al. / Procedia Computer Science 168 (2020) 105–111 109
Saling, Do / Procedia Computer Science 00 (2019) 000–000
Fig. 2. Talent Management Systemigram (Acquire and Develop) v.1.
From this version, the authors worked to look at where other factors, such as a strong economy with multiple other job options, might
play a role in impacting decisions about who to acquire, how to develop them, how to best employ them, and how to retain them (see
Figure 3).
Fig. 3. Talent Management Systemigram (Acquire and Develop) v.2.
110 Kristin C. Saling et al. / Procedia Computer Science 168 (2020) 105–111
Saling, Do / Procedia Computer Science 00 (2019) 000–000
3. Understanding the Talent Management Ecosystem and a New System
The authors continue to develop and refine the Talent Management System by applying systemigram modeling technique in an
effort to capture as much as possible the human capital ecosystem with the objective of mapping out decision points that can be
improved by data science and artificial intelligence augmentation. So far, the authors have come up with a number of possible use
cases for big data and artificial intelligence (AI) in the Army human capital ecosystem.
3.1. Leveraging Big Data and AI in Recruiting
Many organizations have begun leveraging AI to attract and screen the best possible employees for a given position. Big data has
given the Army a much better understanding of the requirements necessary for a candidate to succeed through different screening
events, e.g. initial training, physical fitness, cognitive aptitude, and first term performance. AI can help screen initial candidates for
each branch against detailed requirements to provide a targeted list for screening, recruiting, and assigning.
3.2. Retention Management
The Army already predicts the aggregate how many officers, Soldiers, and civilians it will have over time, but increasing data
collection and AI tools broaden that capability to retention prediction at the individual level. In a talent-based human capital system,
the Army needs the capability to predict which employees possessing particular talents are most likely to stay and those that are
likely to leave in order to improve job satisfaction and offer appropriate incentives.
3.3. Automated Career Planning Assistants
As the Army implements open hiring markets in place of its antiquated personnel assignment process, officers, Soldiers, and
civilians must sort through hundreds of available jobs in order to contact organizations to select the jobs most suited for their
individual talents and development along their chosen career path. The Army is reviewing options for implementing a career
coaching program, but automated assistants programmed with knowledge of job prerequisites, talent requirements, manner of
performance, and other critical information could provide considerable help in mining through the data.
3.4. Command Climate Monitoring
The Army collects survey information on command climate in order to identify a variety of problems impacting employee
satisfaction and morale. Advanced sentiment monitoring capability might improve this by letting leaders know when a group’s
behavior or satisfaction has changed abruptly, or when certain groups are expressing lower than normal morale levels. Sentiment
analysis is difficult to implement in the current environment, so there are significant obstacles to this effort. However, with proper
permissions, it could be a powerful tool for employee motivation.
4. Future Work
In this paper, the authors have presented a methodology for viewing the career ecosystem as a complex adaptive system with
recommended influence points where data scientists and policy makers can best apply augmentation tools to improve human
resources decisions. They have introduced a means for viewing this system in an interdisciplinary manner and provided a number of
potential solutions that furthers the research and modeling efforts in order to continue to improve the system.
The authors will continue to improve the model in order to get a deeper understanding of the number of factors that contribute to
acquisition, retention, development, and employment decisions along the individual career decisions and how these factors can best
be influenced through analytics-augmented decisions in order to help individuals better manage their careers and allow the Army to
field the best possible force. Additionally, the authors recommend a pilot study be undertaken to validate this future adaptive talent
management system. The pilot study will look how data analytics and HCBD have been used to improve the Army’s Talent
Management system to acquire, employ, develop, and retain personnel with the requisite skills required to improve readiness across
the entire Army.
Kristin C. Saling et al. / Procedia Computer Science 168 (2020) 105–111 111
Saling, Do / Procedia Computer Science 00 (2019) 000–000
Acknowledgements
The authors express their thanks and gratitude to the multiple members of the U.S. Army Deputy Chief of Staff G-1 (Personnel)
and other staff elements of the Army staff that agreed to be interviewed for the purposes of this project. The views presented within
this project are those of the authors and do not necessarily represent the views of the United States Army, the Department of Defense,
or its components.
References
[1] Senate Armed Services Committee Advance Policy Questions for General James McConville, USA, Chief of Staff of the Army. https://www.armed-
services.senate.gov/download/mcconville_apqs_04-02-19 Accessed 28 Jun 2019.
[2] Cilliers, Paul. Complexity and Post-Modernism: Understanding Complex Systems. Taylor & Francis Group. Accessed from the web at
http://www.academia.edu/download/28128125/Complexity_and_Postmodernism.pdf. Accessed 28 Jun 2019.
[3] Schneider, Marguerite, and Mark John Somers. “Organizations as complex adaptive systems: Implications of Complexity Theory for Leadership Research.” The
Leadership Quarterly 17 (4): 351-365.
[4] Dooley, Kevin J. “A complex adaptive systems model of organizational change.” Nonlinear Dynamics, Psychology, and Life Sciences 1 (1): 69-97.
[5] Begun, James W., Brenda Zimmerman, and Kevin J. Dooley (2003) “Health care organizations as complex adaptive systems,” in S.S. Mick and M.E. Wyttenbach
(eds) Advances in Health Care Organization Theory, San Francisco, Jossey-Bass.
[6] Tsui, Anne S. and Luis R. Gomez-Mejia (1988). “Evaluating Human Resource Effectiveness” from Lee Dyer (ed) Human Resource Management Evolving Roles
and Responsibilities, Washington, DC, Bureau of National Affairs, Inc.
[7] Wardynski, Casey, David S. Lyle, Michael J. Colarusso (2009) Towards a U.S. Army Officer Corps Strategy for Success: A Proposed Human Capital Model
Focused on Talent. Carlisle, PA: Strategic Studies Institute.
[8] Hughes, Julia C., and Evelina Rog. (2008) “Talent management.” International Journal of Contemporary Hospitality Management 20 (7): 743-757.
[9] Liberatore, Matthew J., and Wenhong Luo. “The analytics movement: implications for operations research.” Interfaces 40 (4): 313-324.
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Analytical Approaches in Human Resources - A Systematic Review
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Full-text available
  • Oct 2022
Surveys related to analytics in the area of human resources (HR) have increased in the last 10 years. They usually suggest frameworks, tools, and concepts. Although there is much useful information, there is still a lack of materials consolidating real case studies or quantitative experiments with HR data. This systematic review analyzes 42 papers with analytical experiments in terms of three different segments of HR: recruitment, talent management, and turnover. The goal is to offer an updated perspective of what is being applied in HR regarding the problems that can be solved with data analysis, the most used techniques, and what could be explored to promote more scientific research on data-oriented projects in HR. Some of the results include talent management as the segment with the most related papers and the use of companies’ internal data as predominant in the studies. Keywords HR analytics; People analytics; Strategic human resources management; Talent management; Turnover
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Presenting a structural model of digitalised talent management in a new age: A case study on the mobile telecommunication industry in Iran
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Full-text available
  • Oct 2022
Orientation: Digital transformation has changed the process of talent management. Traditional models embraced activities and processes that guided the right employee towards the right positions or a broad view of talent management. Research purpose: This study aimed to investigate the mediating impact of digitalised process management on relationship between talent management (TM) and organisational performance. Motivation for the study: Digitalisation in enhanced industries such as mobile telecommunications emphasises agility required to attract talent in a dynamic environment in terms of marketing, competition, et cetera. Research approach, design and method: Research data were collected through a quantitative approach. The statistical population of participants in this research included 298 managers and specialists in the field of the mobile Iranian telecommunications industry. Data were collected using a standard questionnaire, the validity of which was assessed based on the validity of content and structure and its reliability through Cronbach’s alpha. Obtained model was analysed using SPSS version 26 and Smart PLS version 3.3.3 software. Practical or managerial implications: Impact of TM on organisational performance through the mediation of digitalised process management is identified. Results show that investments are required to correlate the digitalised process into TM processes to take organisational performance into the digitalisation era. Contribution or value-add: This study extends the knowledge about future of the TM process, which is enhanced by digitalisation aspects to support company and organisational performance for achieving and adopting in the digital era. The study also extends digitalisation process by using a structural model to investigate the future of process in mobile telecommunication industry in Iran.
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Organizations as complex adaptive systems: Implications of Complexity Theory for leadership research
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This article contrasts the assumptions of General Systems Theory, the framework for much prior leadership research, with those of Complexity Theory, to further develop the latter's implications for the definition of leadership and the leadership process. We propose that leadership in a Complex Adaptive System (CAS) may affect the organization indirectly, through the mediating variables of organizational identity and social movements. A rudimentary model of leadership in a CAS is presented. We then outline two non-linear methodologies, dynamic systems simulation and artificial neural networks, as appropriate to enable development and testing of a model leadership under the assumptions of Complexity Theory.
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The Analytics Movement: Implications for Operations Research
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Full-text available
  • Aug 2010
The movement toward the increased use of analytics in organizations has generated much discussion by aca- demics and professionals about the impacts and opportunities that analytics offers. Although operations research (OR) has been a driving force in applying quantitative and analytical models for organizational decision making, it is less clear how we as OR practitioners can take advantage of the surging interest in analytics to promote the OR profession and expand its reach. In this paper, we discuss the drivers of the analytics movement, an example of an analytics project, and the opportunities and implications for OR, i.e., the problem scope, models and methods, implementation issues, organizational role, professional skills, and education.
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Evaluating Human Resource Effectiveness" from Lee Dyer (ed) Human Resource Management Evolving Roles and Responsibilities
  • Jan 1988
  • Anne S Tsui
  • R Luis
  • Gomez-Mejia
Tsui, Anne S. and Luis R. Gomez-Mejia (1988). "Evaluating Human Resource Effectiveness" from Lee Dyer (ed) Human Resource Management Evolving Roles and Responsibilities, Washington, DC, Bureau of National Affairs, Inc.