Editor-in-Chief Dr. V. S. More
Ex-Dean Dept. of Commerce, University of Pune, Pune
Director, Institute of Management & Research, Nasik (India)
Associate Editors Dr. Saroj Dash Dr. Surendra Sisodia Mr. Abdul Rahman
Assistant Editors
Ms. Swati Chauhan Ms. Ashu Bhojwani
Managing Editor
Dr. Arif Anjum(India)
Frequency :
Quarterly-Four Issues Per Year
Correspondence Address:
Indian Journal of Management Science, S.N.21, P.N.24, Mirza Ghalib Road, Malegaon Nasik, Maharashtra – 423203 (India)
Contact: 0919764558895 Email: infoijms@gmail.com Website: www.scholarshub.net
Impact Factor: The Global Impact Factor (GIF) provides quantitative and qualitative tool for ranking, evaluating and categorizing the journals for academic evaluation and excellence. This factor is used for evaluating the prestige of journals. The evaluation is carried out by Global Impact Factor, Australia.
Disclaimer: The views expressed in the journal are those of author(s) and not the publisher or the Editorial Board. The readers are informed, editors or the publisher do not owe any responsibility for any damage or loss to any person for the result of any action taken on the basis of the work. © The articles/papers published in the journal are subject to copyright of the publisher. No
INDIAN JOURNAL
OF
MANAGEMENT SCIENCE
Volume – III
Issue – 3
July 2013
EDITORIAL BOARD
Rohaizat bin Baharun Department of Management Faculty of Management Universiti Teknologi Malaysia
Yasser Mahfooz, PhD Department of Marketing,
College of Business Administration, King Saud University, Riyadh, Saudi Arabia
Edhi Juwono
Perbanas Economics School for Management Information Systems,
Indonesia
Dr.Mu.Subrahmanian Professor & Head,
Department of Management Studies, Naya Engineering College, Chennai
Prof. D. P. Singh
Delhi college of engineering, Delhi
Dr. Nafis Alam, School of Business, University of Nottingham, Malaysia
Michael Sunday Agba,
Department of Public Administration, Federal Polytechnic,
INDEX
SN
TITLE
PAGE
NO.
1.
The Application of Effective Coaching Techniques in Designing a Coaching Plan for
Performance Improvement in Graduate Assistants
Tracie V. Cooper & Donovan A. McFarlane (UUSSA)A
01-07
2.
A Hybrid Data Mining Approach to Construct the Target Customers Choice
Reference Model
Shih-Chih Chen & Ruei-Jr Tzeng (TTaaiiwwaan)n
08-15
3.
The Used of it Balanced Scorecard to Build the Performance Measurement Model of
Academic Information Systems (Case Study Academic Information System of Satya
Wacana)
Paskah Ika Nugroho, Prihanto Ngesti Basuki & Evi Maria (IInnddoonneessiia)a
16-22
4.
Increasing the Accountability of the Institution through the Whistle Blowing System
Jony Oktavian Haryanto, Yefta Andi Kus Nugroho, Rizal Edy Halim & Rizal Edwin Manansang (IInnddoonneessiia)a23-33
5.
Agricultural TFP and R&D Spending in Iran
Solmaz Shamsadini, Saeed Yazdani & Reza Moghaddasi (IIrraan) n
34-41
6.
Ranking Indian Domestic Banks with Interval Data
–
The Dea Application
Dr. T. Subramanyam & Dr. R.V.Vardhan (IInnddiia)a
42-47
7.
The Effects of Financial Reporting Quality on Stock Price Delay & Future Stock
Return
Azam Pouryousof, Hilda Shamsadini & Mina Abousaiedi (IIrraan)n
48-52
8.
Gold Price Movements in India and Global Market
Shaik Saleem, Dr. M. Srinivasa Reddy & Shaik Karim (IInnddiia)a
53-60
9.
The Kerala Building and other Construction Workers Welfare Fund Board
–
Social
Impact on Members
Dr. Abdul Nasar VP & Dr. Muhammed Basheer Ummathur (IInnddiia)a
61-70
10.
A Study of Socio Economic Condition of Child Labour Engaged in Rag-Picking at
Silchar
Shima Das, Dr. Amit Kumar Singh & Bidhu Kanti Das (IInnddiia)a
71-78
11.
Stock Market Anomalies: Empirical Evidence from Weekend Effect on Sectoral
Indices of Indian Stock Market
Potharla Srikanth & P. Srilatha (IInnddiia)a
79-85
12.
Internet Banking: Does it Really Impacts B
ank’s Operating Performance
THE APPLICATION OF EFFECTIVE COACHING TECHNIQUES
IN DESIGNING A COACHING PLAN FOR PERFORMANCE
IMPROVEMENT IN GRADUATE ASSISTANTS
Tracie V. Cooper,
Faculty Support Coordinator
H. Wayne Huizenga School of Business
and Entrepreneurship
Nova Southeastern University,
Fort Lauderdale, Florida, USA
Donovan A. McFarlane,
Adjunct Professor of Marketing,
Nova Southeastern University
Adjunct Professor of Leadership Studies,
Bethune-Cookman University
Adjunct Professor of Business Administration,
Broward College
Visiting Professor of Management,
Keller Graduate School
–
DeVry University
Professor of Business Administration & Business
Research, Fredrick Taylor University
Faculty Blog Manager, Huizenga School of Business
Director, The Donovan Society, LLC, USA.
ABSTRACT
This paper examines effective coaching techniques that could potentially be incorporated into a coaching plan to improve the performance of new-start graduate research assistants in an academic school and department at a university. From the perspective of a supervisory or managerial capacity, the authors play the role of the prospective “Coach” responsible for faculty support, and therefore attempt to meet the requirements of this office by working collaboratively through and with hired work-study graduate students who serve as graduate research assistants in an academic department and school at a university. The opportunity for coaching unfolds in the scenario where four new start graduate students from the schools of business and computer sciences are hired as research assistants in an academic department and must effectively meet the needs of the faculty in being able to competently perform several tasks related to research. Most of the tasks are already within the ability-scope of these graduate students. However, blending into their roles as newly hired employees and research assistants to the faculty support coordinator and professors in this department and school requires developing familiarization with organizational culture, process protocol, work study portfolio organization and competence in their new roles. This presents an opportunity for coaching using several techniques to address familiarization, competence, and motivational and work-process issues. Thus, examining the literature on effective coaching and coaching techniques, the authors in a coaching capacity will develop, design, and implement a Coaching Plan or program to address these competencies and work-needs-skills in this situation based on practical guidelines or recommendations of previous research. This paper describes this opportunity for effective coaching and presents relevant literature on coaching techniques and effectiveness, recommends a viable coaching plan and resolution to identify issues, and draws conclusion based on what constitutes success or effectiveness in real-life situations. Additionally, broader implications for coaching strategies and techniques applied to real problems, opportunities, or issues in organizational contexts and examined.
Introduction:
Coaching is becoming more and more important as a process and performance improvement method and approach in organizations across all fields. Coaching can be defined as “a process that enables learning and development to occur and thus
performance to improve” (Parsloe, 1999, p.8). Coaching effectiveness is what is important in today‟s organizations as coaching becomes both a corrective process and action to address performance, behavioral, and other issues across organizational
boundaries, and more and more managers attune to the coaching process and its application. According to Parsloe (1999), “To
be a successful coach requires a knowledge and understanding of process as well as the variety of styles, skills and techniques
that are appropriate to the context in which the coaching takes place” (p. 8). Managers or supervisors must use effective
coaching techniques that cater to individual and group, as well as organizations needs.
The International Coach Federation [ICF] (2011) defines coaching as “partnering with clients in a thought-provoking and
creative process that inspires them to maximize their personal and professional potential” (p. 1). This definition takes a service-provision or orientation to coaching, and coaching is in fact based on service-philosophy to individuals and organizations with the end result being to improve performance and productivity. Coaching is indeed a creative process and it is the responsibility of the coach to ensure that creative techniques or methods are used to address different coachee needs. Coaching is especially important in helping new hires or new organizational members to improve their present skills levels as they are coached by experienced organizational members and managers to perform important tasks effectively and efficiently to meet organizational goals. While this is the case, most application of coaching seems to be in contexts involving organizational members or employees with significant time onboard, but lingering problems that affect attitude and work morale; hence, performance.
Literature Review:
The performance benefits of coaching are becoming more widely known and accepted and “coaching is [now] seen as having clear and unique advantages, and is establishing itself alongside related activities, such as mentoring and counselling, as a key
development technique” (Phillips, 1996, p. 29). Coaching in organizational contexts fills several roles and confers several
benefits. According to the International Coach Federation [ICF] (2011) “Individuals who engage in a coaching partnership can
expect to experience fresh perspectives on personal challenges and opportunities, enhanced thinking and decision making skills,
enhanced interpersonal effectiveness, and increased confidence in carrying out their chosen work and life roles” (p. 1). The
benefits gained from coaching depend on how well the coach uses effective techniques that cater to individual skills development or developing top talent that will serve the organization (Hunt & Weintraub, 2011). The coaching interaction is an important factor in considering coaching techniques as managers need to recognize that employees have a need to express themselves as they influence organizational policy and decisions without authority.
According to Cohen and Bradford (2005) influence is important in human social interaction, and the coaching process involves two-way influence, a process where the coach is influencing the coachee to make some form of change, progress, or improvement; and a process where the coachee without vested managerial authority influences the views, decisions, and strategies of the coach. Leadership coaching in organizations requires influence, and Wakefield (2006) argues that “Leadership coaching is a vital tool for developing talent in organizations. Hunt and Weintraub (2011) certainly concur with this view. Managers and supervisors who facilitate coaching must also recognize that both tasks and relationship are important in coaching (Hunt & Weintraub, 2005). Thus, important concepts such as trust which functions to achieve influence and cooperation should be integrated into the approach to coaching, especially where employees or coachees depend on their manager or coach to hone their
skills to maximize their performance and job security. According to Hunt and Weintraub (2005), “good relationships make it easier to gain cooperation, it pays to be generous and engage in win-win exchanges” (p. 23). Managers and leaders who engage the coaching process to address performance-related individual and organizational opportunities and challenges must build effective relationships with their employees in order to facilitate progress and get results.
Wakefield (2006) suggests engaging the four P‟s that will help employees become more innovative problem solvers during the coaching process. These four P‟s are: (i) partnering for technological collaboration; (ii) possibilities for turning necessity into opportunity; (iii) perspective by providing opportunity for individuals to broaden their problem-solving skills and experiences; and (iv) practicing innovation throughout the coaching process and the organization using total quality management (TQM). Coaching is a social process and the coach must bear in mind that people are the most important of organizational assets. According to Case and Kleiner (1993), this fact must be recognized before managers can begin coaching their employees effectively. Case and Kleiner (1993) assert that there are many methods or techniques to facilitate coaching. With this understanding, they argue that coaching is not a method, but a combination of methods or practices applying different tactics and strategies that are used to guide employees towards maximizing their potential in organizational work settings. Case and Kleiner (1993) list several techniques that they argue are coaching techniques: rewards, compensation, training, employee development programs, goal setting, discipline, employee participation, and group participation problem solving.
Clutterbuck (2005) believe that effective coaching involves the ability to influence employees who are able to identify
individuals who have been “helpful” in their career and have influenced them in ways which contribute to success or
performing successfully in their organizational roles. The process of visioning as a technique in coaching can be used in many situations, and is especially powerful in goal-setting. According to Megginson and Clutterbuck (2005), the core of effective
visioning is engaging all the learner‟s senses and inner emotion. This inner emotion affects the individual coachee‟s perceptions
and attitudes toward the coaching process. Visioning involves a process of visualization that asks questions such as: (a) where do you want to be? (b) what do you see around you in terms of the environment and people? (c) how do you appear? (d) what are you doing and why? and (e) how do you feel and why do you feel this way? Among other questions that attune the coachee to the present situation, the need for change, and the goal or vision of what he or she wants to accomplish from the coaching relationship or training are important.
Megginson and Clutterbuck (2005) believe that “Visioning is best used when the learner is relatively relaxed” (p. 12), and that the technique requires the coach to engage the coachee to focus his or her whole consciousness into placing the self in a possible future. This stands to reason, as coaching for performance improvement involves developing talent in the organization to a certain optimum or to meet certain standards. Individuals and groups must be able to display certain levels of performance, attitudes, work morale and skills to effectively increase productivity and organizational competitiveness. Therefore, the coach must use this technique to foster a sense of potential and demonstrate to the coachee the ability to develop and apply the skills to reach that potential in a reasonable time frame. Organizational rewards and compensation can be used as techniques that supplement this process, and Case and Kleiner (1993) argue that these not only serve in the roles of feedback, but as motivators
since “everyone in an organization gives of his or her abilities and efforts in exchange for rewards given by the organization”
(p. 8). Thus, rewarding and compensating; the manner in which these are done as performance-based indices, can significantly contribute to overall coaching effectiveness and success.
In coaching individuals to improve their performance in the work setting, coaches must focus on building those defined set of business or work-related skills that will affect individuals‟ abilities to work independently, as well as part of teams and groups (Butler, Forbes, & Johnson, 2008). As Case and Kleiner (1993) note, there are many methods or techniques of effective coaching available to managers, but managers must be able to choose the best methods or techniques suited for particular employees or subordinates. This requires remembering that people are individuals. Case and Kleiner (1993) argue that coaching methods or techniques used must be refined or should be “changed in the event of continued poor morale and
performance to ensure that resources are not merely being wasted” (p. 10).
Contemporary techniques in coaching are being developed across various organizations by managers and leaders to address individual and organization specific performance and challenges. This includes the increasing use of the telephone to facilitate coaching. According to Gaskell (2006) and Sparrow (2006), as confidence and expertise grow in coaching as a development intervention, the telephone option is being increasingly used as a viable alternative to face-to-face meeting for coaching. Gaskell (2006) argues that telephone coaching is catching on because it is convenient and less expensive. Managers are increasingly conducting one-to-one coaching over the telephone and are getting significant results. This means that telephone coaching is becoming more and more popular, and there are different companies and individuals using this technique. Sparrow (2006) shows how telephone coaching forms the basis of account manager development programs at Elizabeth Arden, cosmetic
giant company. According to Sparrow (2006), telephone coaching has been successfully used by this company‟s managers to
deal with professional and personal tensions in an effective manner.
Telephone coaching holds good promise as a technique because of its cost-saving advantage, flexibility and convenience as managers can be in different locations while providing instructions to employees as to performance on various issues.
According to Gaskell (2006) “Telephone coaching can work because there is something powerful about the voice entering the mind of the coachee more directly” (p. 24). The coach on the other side of the line must however be a very good communicator since the absence of face-to-face interaction sometimes creates communication problems in similar scenarios. The use of telephone coaching also gives consideration to other coaching techniques making use of different technologies including the computer, videos, and other forms of applied communication techniques.
Appendix 1: Steps in Effective Coaching Plan). Methodology:
This article examines effective coaching techniques that could potentially be incorporated into a coaching plan to improve the performance of new-start graduate research assistants in an academic school and department at a university. Four new-start graduate students from the schools of business and computer sciences were hired as research assistants in an academic department and school of a university to effectively meet the needs of the faculty in being able to competently perform several tasks related to research. From the perspective of a supervisory or managerial capacity, the authors play the role of the
prospective “Coach” responsible for faculty support, and therefore attempt to meet the requirements of this office by working collaboratively through and with hired work-study graduate students who serve as graduate research assistants in an academic department and school at a university.
The opportunity for coaching unfolds in the scenario where four new start graduate students from the schools of business and computer sciences are hired as research assistants in an academic department and must effectively meet the needs of the faculty in being able to competently perform several tasks related to research. Most of the tasks are already within the ability-scope of these graduate students. However, blending into their roles as newly hired employees and research assistants to the faculty support coordinator and professors in this department and school requires developing familiarization with organizational culture, process protocol, work study portfolio organization and competence in their new roles. This presents an opportunity for coaching using several techniques to address familiarization, competence, and motivational and work-process issues. Thus, examining the literature on effective coaching and coaching techniques, the authors in a coaching capacity will develop, design, and implement a Coaching Plan or program to address these competencies and work-needs-skills in this situation based on practical guidelines or recommendations of previous research. This article describes this opportunity for effective coaching and presents relevant literature on coaching techniques and effectiveness, recommends a viable coaching plan and resolution to identify issues, and draws conclusion based on what constitutes success or effectiveness in real-life situations. Additionally, broader implications for coaching strategies and techniques applied to real problems, opportunities, or issues in organizational contexts and examined.
The Coaching Opportunity:
Four new-start graduate students from the schools of business and computer sciences were hired as research assistants in an academic department and school of a university to effectively meet the needs of the faculty in being able to competently perform several tasks related to research. Most of the tasks are already within the ability-scope of these graduate students. However, blending into their roles as newly hired employees and research assistants to the faculty support coordinator and professors in this department and school requires developing familiarization with organizational culture, process protocol, work study portfolio organization and competence in their new roles. The job performance of graduate assistants requires them to be competent in performing the basic functions in described Table 1 below.
Table 1: Graduate Assistant General Job Description
Source: CareerPlanner.com, (2011).
Different skills set and competence levels will require assistance in meeting some of the assigned tasks given to graduate assistants by professors and faculty support coordinator. Generally, faculty support coordinators are responsible for training or coaching graduate assistants in meeting their job roles and in becoming familiar with different aspects of their jobs related to organizational culture and the tools and equipment they will use to meet their job roles. The need for proficiency in these areas (Table 1) and becoming part of the organizational culture provide opportunities for coaching and the development of coaching relationships. The coaching opportunities from graduate assistant jobs allow coaches not only to develop their own coaching skills, but to coach these graduate assistants who may become future faculty support coordinators or faculty support trainers and managers. Thus, the benefits can be seen immediately in performance as well as, as an investment in organizational future. This coaching opportunity with graduate assistants provides for application of coaching skills and techniques on several levels.
The Coaching Plan:
The proposed Coaching Plan to address the opportunity of training these four graduate assistants to function at their maximum
and in an effective capacity will be based on “Solution-Focused Coaching”, which involves using a variety of techniques Assists department chairperson, faculty members or other professional staff members in college or
described above to facilitate their skills and abilities in effectively performing their job functions and assigned tasks. The dominant techniques that that will be used include telephone coaching, rewarding and compensation, and what could be called instructional-face-to-face coaching. A combination of techniques will be used according to the specific needs of these individuals and their levels of skills. It is reasonable to assume that some of these graduate assistants will have differing skills in terms of job-specific required competences and that their learning levels and communication skills might require unique consideration in the application of coaching techniques. However, based on experience and the nature of their job functions, instructional face-to-face coaching and telephone coaching are the core coaching techniques that will serve best to meet coaching goals in both physical and virtual environments.
Instructional face-to-face coaching will probably be the most dominant techniques since the graduate assistants will mainly need hands-on or technical skills to function in their current roles. For example, these graduate assistants must know how to construct PowerPoint presentations, photocopy papers, scan and attached papers in emails, fax papers, use the Scantron, access electronic databases for research and retrieval of articles and data, format papers for professional presentation and publication,
compile materials and folders for specific courses according to professors‟ request, and perform other related academic
functions which may require the use of programs not limited to Excel, Access, and other functions in Microsoft Office, and even use statistical software such as SPSS and PSPP.
Instructional face-to-face coaching will be a daily on-the-job process where the faculty support coordinator or other qualified and immediate supervisors in the department, including professors can coach graduate research assistants to improve their current skills set and competences to meet their job requirements. This also provides opportunity to build lasting influence relationships as these graduate assistants go on to further their education and even become faculty or future administrators. Telephone coaching where the faculty support coordinator can provide instructions to graduate assistants in performing certain job functions is an effective technique where face-to-face consultation is not an option. For example, at any specific time where a faculty support manager or coordinator or supervisor over the graduate assistant is not present in the immediate office and a graduate assistant needs direction in performing a task, for example scanning a document to email, a simple phone instructional session could facilitate this. This also applies to more complex tasks which the graduate assistant might not be familiar with. With experience and knowledge about all the required tasks and functions a graduate assistant may be asked to perform, an experienced and knowledgeable faculty support manager or coordinator or graduate assistant supervisor can provide effective telephone coaching that improves graduate assistants‟ skills and performance almost immediately or over a very short period of time. Thus, as Sparrow (2006) demonstrates, telephone coaching is extremely useful in the coaching process.
Coaching Plan Resolution:
The above coaching techniques described in the literature review are designed to provide quick solutions with immediate results, and in such an organizational setting and work situation, coaching is an applied-results oriented process where the coachee immediately puts into practices those skills communicated or demonstrated by the coach, and this, mainly through an instructional coaching approach. The overall coaching plan for responding to the coaching opportunity in this paper could be
described as a “Solution-Focused Coaching” because of the need for practical and applied performance skills by the coachees to perform their jobs functions as graduate assistants.
Facilitating performance development and training through coaching requires understanding impacting variables of time, responsibility, performance requirements on the part of coach and coachees, the level of skills training and assistance required, and the available and appropriate coaching techniques that will produce the best results from both human relations and scientific viewpoints. Using the coaching plan described above, the coach should consider keeping the coaching brief and solution-based (Wakefield, 2006). This does not only save organizational time as a valuable resource, but also will ensure that both coach and coachee stay motivated and have a realistic time frame in which to bring the performance coaching session to its close.
Effective and brief solution-focused coaching helps people to tap into their own resources to deal effectively with challenges by making positive changes that can lead to success both personally and for their organizations (Wakefield, 2006). Furthermore, it is based on finding solutions and this alone allows for the coach to focus specifically on resolving or addressing specific
problems and challenges rather than engaging in “umbrella coaching”. The aim of coaching in the case opportunity presented
in this paper requires applying specific techniques that address specific problem-solving issues and task necessitation. For example, graduate assistants must conduct research and know how to identify and retrieve academic peer review articles from electronic databases. While most students at the graduate level would have some knowledge of this, fostering maximum skills development in this task requires the coach to teach by demonstration; that is, showing and doing the required task as an example. This will also provide opportunity for fostering further required competences such as compiling bibliographic lists through the citation function, using exporting and importing functions, and other functions in electronic database for search and retrieval during assigned research.
instructional face-to-face coaching and occasional telephone coaching are the best and most applicable techniques. Additionally, graduate assistants tend to develop many research and technical skills on their own through troubles-shooting and applying problem solving techniques from their programs of study. Furthermore, through observation and mentorship, they will grow into their roles naturally. Using telephone and instructional face-to-face coaching provides for communication and interaction and the appropriate levels of relationship that will foster the development of self and performance improvement. Telephone coaching will also provide for a significant degree of independence, which is a major competence that faculty and administrators in colleges and universities search for in students as potential graduate assistants.
Summary & Conclusion:
Coaching can represent a great opportunity for facilitating and fostering change through communication and interpersonal interaction. Coaching as an effective work-motivation and performance enhancing process has been increasingly applied to various organizations at different levels and in all kinds of industries. The benefits of coaching can be tremendous in terms of its ability to boost worker morale, motivation, increase job performance and skills levels, and reduce employee turnover. When coaching is effectively applied to address deficiencies in an organizational setting it not only serves as a diagnostic, curative, and preventative approach to workplace problems and their consequences, but also adds value to human and capital resources. Coaching graduate assistants certainly requires having a good knowledge and understanding of the coaching process and various techniques because of their levels of education, the special nature of their job requirements and responsibilities, and the fact that they are working in academic environments where they are perhaps very familiar with coaching and already have trainable skills sets required for their job roles. The different coaching techniques presented in this paper can be used at different points to address specific coaching situations and individual needs. However, telephone coaching and face-to-face instructional coaching techniques are ideal in meeting the coaching needs of graduate assistants and can facilitate the building of relationships and performance improvement with convenience and effectiveness. The coach must remember that these individuals have varying skills and needs and must develop a coaching plan with clear goals, objectives, and a reasonable time-frame in which coachees acquire skills. Most importantly, they must provide clear directions and reinforcement and delegate power to graduate assistants to foster independent problem solving and decision making skills.
Recommendations:
Before developing a coaching plan to address what is perceived to be performance related problems, the prospective coach must first engage in several activities. These activities will serve both as diagnostic and assessment indicators that allow the coach to gauge the levels of communication, interaction, develop appropriate coaching plan, and apply the most effective techniques for success from an understanding of coachee needs, standards, and organizational goals. Thus, it is recommended that the prospective coach develop an agenda which has the following components and plan of action:
1. An assessment of present skills sets and needs of the prospective coachee. 2. Clear understanding of what is important in a coaching relationship.
3. Develop trust that will build the relationship required for successful coaching.
4. Identify the coachee‟s weaknesses and strengths, as well as the critical skills set needed to address existing performance gap. 5. Establish a clear and controlled objective for coaching and the coaching process.
6. Apply those techniques with the highest potential for instilling desired change and improvement.
7. Develop an effective plan for coaching that has assessment standards and procedures, as well as a clear time frame. 8. Make feedback and communication continuous; and most importantly,
9. Foster independence throughout the coaching process since the aim is to equip the individual for autonomous self-growth. Coaching is an effective tool for performance improvement and the techniques available are diverse, and their successful application will depend on the scenario, coachee readiness, the skills of the coach and a variety of internal and external individual and organizational factors.
References:
[1] Butler, D., Forbes, B., & Johnson, L. (2008). An examination of a skills-based leadership coaching course in an MBA program. Journal of Education for Business, Marc/April 2008, pp. 227-232; Taylor & Francis Inc. Retrieved from http://search.proquest.com/docview/202821891?accountid=14129
[2] CareerPlanner.com. (2011). Graduate Assistant: Job Description and Jobs. Retrieved from http://www.careerplanner.com/DOT-Job-Descriptions/GRADUATE-ASSISTANT.cfm
[3] Case, T., & Kleiner, B.H. (1993). Effective coaching of organizational employees. International Journal of Productivity and Performance Management, May/Jun 1993; 42, 3, pp. 7-10. Emerald Group Publishing, Limited. Retrieved from http://search.proquest.com/docview/218430873?accountid=14129
Business Information UK. Retrieved from http://search.proquest.com/docview/231093282?accountid=14129
[6] Hunt, J.M., & Weintraub, J.R. (2011). The coaching manager: Developing top talent business, 2nd edition. Thousand Oaks, CA: SAGES Publications, Inc.
[7] International Coach Federation [ICF]. (2011). About Coaching. Lexington, KY: International Coach Federation. Retrieved from http://www.coachfederation.org/intcoachingweek/about-coaching/
[8] Parsloe, E. (1999). The Manager as Coach and Mentor. London, England: Chartered Institute of Personnel & Development.
[9] Phillips, R. (1996). Coaching for higher performance. Journal of Workplace Learning, Vol. 8 Iss: 4, pp.29 – 32.
[10] Megginson, D., & Clutterbuck, D. (2005). Goal-seekers. Training & Coaching Today, September 2005, p. 12. Reed Business Information UK. Retrieved from http://search.proquest.com/docview/231098307?accountid=14129
[11] Sparrow, S. (2006). Case Study. Training & Coaching Today, April 2006, p. 24. Reed Business Information UK. Retrieved from http://search.proquest.com/docview/231093282?accountid=14129
[12] Wakefield, M. (2006). New views on leadership coaching. The Journal for Quality and Participation, Summer 2006, 29, 2 pp. 9-12. Association for Quality and Participation. Retrieved from http://search.proquest.com/docview/219091474?accountid=14129
Appendix 1: Steps in Coaching Plan
*Set clear goals. It is essential that every employee knows what the project goal is. A good job cannot be done if the goal is not clear. This requires good communication between the manager and his subordinates. The goal must be very specific and to do this it must be measurable.
* Have objectives. Objectives must be created for every employee or group involved in a project. This breaks down the goals into precise duties for each group or individual employee. Employees are more able to recognize their contributions towards the goal when objectives are set. Objectives also serve as daily reminders of what is to be accomplished
* Develop an action plan. Action plans detail what is to be done and also monitor progress towards project completion. An action plan should consist of checkpoints, activities, relationships and time. Checkpoints monitor progress towards completion. Short-term checkpoints establish frequent feedback methods. More importantly, checkpoints help employees to monitor their own progress. Activities are the methods used from one checkpoint to the next. Highly detailed activities will save time in the long run. Relationships imply the sequence of activities. Some activities may be done simultaneously. Sequencing requires careful consideration. Finally, the time of project from start to finish must be estimated. This requires accurate estimates of activity time.
* Draft a project schedule. The two most common methods of scheduling used are the Gantt Chart and the PERT Chart. Both
are disciplines of management science.
* Give employees direction. Managers cannot do large projects by themselves. Therefore they require a team of supporters and collaborators. Developing a support group takes skill and an understanding of the perspective of others. Managers must be open-minded and need to realize that people are alike and all have like needs. Employees must be treated as individuals in order to be motivated.
* Give reinforcement. Allow people to volunteer for work. People who sign up do not need to be coerced to work. Give people opportunities to develop goals and objectives. This will build commitment to their work. Give encouragement to employees.
People like to be noticed and appreciated. so managers should not hesitate to give an “attaboy”.
* Keep them informed. Effective communication is required to keep employees informed. Some organizational structures can
be a barrier to good communication. This can create ambiguity, which will result in faulty information dispersal. People should be regularly informed and this requires monitoring and feedback. Managers must also learn to be better listeners. Keeping employees informed of progress will reduce anxiety and increase performance.
* Resolve conflicts. Disagreements between groups or individuals are unavoidable, since projects require the integration of work from many people. Conflict is actually desirable, when it is used as a way of unleashing creativity and imagination. Reasoning and logic must be used to resolve conflicts. Managers must gain acceptance by providing sound rationale for their positions.
* Delegate power. Giving employees power encourages them to put in their best effort, ability and initiative. When managers share power, people at all levels feel that they contribute greatly towards reaching the previously set goals and objectives. Managers must also be honest and competent as well as give direction and inspiration.
* Promote risk taking. Organizations should stress the rewards of success rather than the consequences of failure. Time should be allowed for experimentation and creativity. Innovation requires support and should be enhanced by communication and open exchange of ideas. Source: Thomas J. Case & Brian H. Kleiner. (1993). “Effective coaching of organizational employees” in International Journal of Productivity and Performance Management, pp. 7-8.
A HYBRID DATA MINING APPROACH TO CONSTRUCT THE
TARGET CUSTOMERS CHOICE REFERENCE MODEL
Shih-Chih Chen,
Assistant Professor
Department of Accounting Information
Southern Taiwan University of Science and
Technology, Taiwan
Ruei-Jr Tzeng,
Department of Information Management
Tatung University, Taiwan
ABSTRACT
Marketing, the prevailing commercial activity of enterprises, is an important strategy to increase customer loyalty and potential customer for more profit. To maximize profit with limited resources, it would be more profitable for enterprises to choose the right target customers. Therefore, it is necessary to build up an efficient, objective and accurate target customer choice model. Using data mining techniques to find the target customers is a traditional way. However, most studies in the past mainly focused on finding the high accuracy classifier, but different classifiers perform differently in varied situations. So this study is to propose a target customer choice model by integrating support vector machine, neural network and K-Means algorithm into a two-phase analysis methodology. The research results indicate that the integrated methodology is effective in simultaneously enhancing classification accuracy and reducing Type I and Type II errors.
Introduction:
With the business environmental change and increasingly fierce competition, the enterprise must face how to improve the interests of business and make enterprise more competitive. The previous mass marketing is already out of date, now enterprise must to search niche market and create the merchandise that fit it. Peppers (1999) mentioned that one-to-one economic system will become mainstream in the future, this economic model emphasize the customized production and one-to-one marketing. Therefore, for the future changes, quickly and accurately to find the target customers, maximize the interests of marketing with limited resources is important.
In the past, find target customer always using the different classifiers to improve classification accuracy, but don’t consider the classification error. For instance, when a customer wanted to buy products, but the classifier misjudgment him, this produces Type I error. When a customer didn’t want to buy products, but the classifier misjudgment him, this generates Type II error. This study proposes a two-stage target customers choice model to upgrade classification accuracy and reducing statistical Type I and Type II error. So this study proposed a two-stage data mining methodology. First, we separately compute the accuracy with support vector machine and neural network. Second, by using K-Means algorithm to re-classification target customers, we can upgrade the classification accuracy and reduce Type I and Type II error results.
Literature Review: Data Mining:
The principle of data mining is to find useful information or knowledge from the data, it’s also known as data archeology, data model analysis. Technology Review (2001) awarded data mining is one of the ten emerging technologies that affect human life in the 21st century, this shows the importance of data mining. Fayyad et al. (1996) defined data mining is a process that using automatic or semi-automatic methods to analyze large amounts of data. The research (Scott, 2006) that should take advantage of information technology systems, make all users can depend on their needs to find really useful information rather than search for useless message.
In the analysis of data mining functions, Berry & Linoff (1997) proposed six analysis functions, this is a brief description of the various analysis functions:
(1) Classification: Without first giving the characteristics of each category and clearly defined, and then through the prepared training data to build a model, Let yet classified data to be classified in each category.
(2) Algorithm: Let the high homogeneous data be clustered in the same group, the principle is that the same group has high homogeneity and between the different group has highly heterogeneous.
(3) Prediction: Speculate value may be incurred in the future or the future trend.
(4) Estimation: To deal with the continuity value, according the existing continuity value to estimate the unknown continuity data. (5) Affinity Grouping: To explore an event or data will appear in a same time, this is used to generate association rules. (6) Description and Visualization: At different angles or different levels to describe complex data, help to make decisions.
Support Vector Machine:
Support vector machine is a machine learning technique that based on statistical learning theory and follow the structural risk minimization principle, now widely used in classification problems. Vapnik (1995) proposed SVM, this is the principle of support vector machine, letting the independent variables and the dependent variable from the original nonlinear corresponding relationship elevated to the high dimensional vector space, and looking for a hyperplane to separate the data into two class in this vector space, making distance between the two class farthest in feature space to achieve the best classification results.
Since support vector machine has performed very well in classification problems, it is widely used in document classification (Joachims, 1998), image recognition (Pontil & Verri, 1998) and biological technology (Yu et al., 2003). The advantages of support vector machine is good summarized ability and training speed, and the SVM's architecture is based on solving a binary programming problem, it can make up for local extreme problem in neural network, therefore, the study will use support vector machine with neural network to analyze.
Neural Network:
nervous system to build a simplified neural system mode, using parallel computing that similar to human brain and self-learning ability, and making system can be accumulated experience through repeated training to achieve the learning effect. Until today, neural network still has new architecture and theories been proposed, because operational speed of computer is more quickly, making neural network more powerful and more widely used.
Research on neural network developed rapidly in recent years, application fields include industrial management, biology, medicine, business and credit Scoring (Stern, 1996; Vellido & Vaughan, 1999; Zhang & Hu, 1998), neural network is very suitable for classify and predict because it can self-organizing, self-learning and generalization.
K-Means Algorithm:
K-Means algorithm was first proposed by James MacQueen in 1967. The k-means approach to algorithm performs an iterative alternating fitting process to from the number of specified clusters. It is one of the simplest unsupervised learning algorithms. With the advantage of good efficiency and simple concept, K-Means algorithm is widely used in various types of data mining and statistical analysis software.
K-Means algorithm is often applied in a variety of researches such as document algorithm (He et al., 2003), data watermarking (Zhang et al., 2001), and graphic retrieval (Kanungo et al., 2000). In multivariate perspective, if the attribute of the real world be abstracted into a vector, it will be able to be calculated by K-Means algorithm. A variety of studies use K-Means algorithm as the analytical tool because of its abstract application.
Research Methodology:
The purpose of this research is to enhance the accuracy when choosing target customers, and meanwhile reduce misspecification rate (including type I and type II errors) when classifying. To achieve the goal, a two-phase target customer choice model is proposed. First of all, we classify the customer data as control group and tested group, and then step into the first phase. Input the data of control group into neural network and support vector machines class models. Run the models and calculate the class accuracy. Compare the results of neural network and support vector machines, if the results are identical, it will be the finale result whether consists with the original data or not, else we will step into second phase to analyze data by using K-Means algorithm.
The second phase purposes to cluster the unclassified data by using K-Means algorithm. We divide the customer data into two clusters, including good customer cluster and bad customer cluster. Then calculate the distance between the unclassified data and the cluster centers of two clusters by using K-Means algorithm. In this research, we define the distance between the unclassified data and the cluster center of the good customer cluster as VG (value of distance from cluster (good)’s center to data), and the distance between the unclassified data and the cluster center of the bad customer cluster as VB (value of distance from cluster (bad)’s center to data). When VG<VB, the data has higher similarity with the good customer cluster, and be clustered to the target customer cluster; otherwise be clustered to un-target customer cluster. Finally, we pour the consistent result from the first phase output and the re-judgment result form the second phase back to the customer data. We calculate the accuracy by support vector machine and neural network again, and observe the effectiveness. The process architecture shows in Figure 3.1.
Fig. 3.1: Target customer choice model
IBM in 2009. Today, we call the new version modeler as IBM SPSS Modeler in which was renamed by IBM in 2010. We choose SPSS Modeler as the data mining tool, because it products directly help improve business processes in many real-life cases. For example, Cablecom GmbH, is the largest cable network operator in Switzerland. By using SPSS Predictive Analytics, Cablecom has continuously seen customer churn rates decrease from 19 percent to 2 percent. In another case, through the use of SPSS Modeler, Dutch insurance firm FBTO Verzekeringen, has also increased conversion rates by 40 percent and decreased its direct mailing costs by 35 percent. Base on the effect of the real-life cases, in this study, we attempted to use SPSS Modeler as a data analyzing and model building platform.
The first phase analysis: Support Vector Machine:
The support vector machine operation process is divided into two parts, operates as following:
Construct the classification system:
The data from this study is nonlinear partitioned dataset, can’t find a hyperplane in the original space, required through kernel function to covert the data from the original space to the high dimensional feature space, and classifying it in this space. We can simplify the complex computational problem become through kernel function. There are four commonly used kernel functions:
Linear:
( ,
)
,
T
i j i j
K x x
x x
Polynomial:
( ,
) (
)
T d
i j i j
K x x
x x
r
,
>0Radial Basis Function: ( , ) exp( ) d
i j i j
K x x
x x,
>0Sigmoid:
( ,
)
tanh(
)
T
i j i j
K x x
x x
r
Kernel function is the key to construct a good performance support vector machine, but the different problems need different kernel function. In this research, we adopt polynomial kernel to construct the classification system because it is good to obtain higher benefit in nonlinear and high dimensional data, and the parameter that we adjust only C value and Gamma value, it's not easy to have too much deviation. (Hsu et al., 2003)
Using different C value and Gamma value will generate different accuracy rate, we through SPSS Modeler to find the best parameter, then we can get better classification performance.
Calculate the correct rate:
Using the support vector machine with set parameter to classify data and calculate the correct rate.
Neural Network:
The neural network has different modes. e.g., back propagation network, Hopfield network and radial basis function network, and back propagation network is the method that is the most commonly used in commercial research (Vellido et al., 1999). Therefore, we using the multilayer perception in back propagation network to analyze data.
Back propagation network is a multilayer feedforward network and it has input layer, hidden layer and output layer. Input layer neurons major role in transmission, and hidden layer and output layer are neurons that really work. Input layer neurons expressed as the number of input variables, in this study, the number of input layer neurons represent variables of customer data, the output layer represent determine customer that is target customer or not,
when the output shows “yes”, represents this data attributable to target customer, if the output shows “no”, represents this data can’t attributable to target customer, and hidden layer represents the interaction between processing unit in input layer.
The second phase analysis:
predictions from two classifiers are not the same. In the second phase, we devoted to cluster the customer data by using K-Means algorithm. We compare the unclassified data with target customer cluster and non-target customer cluster. The unclassified data will be clustered into the cluster according to their similarity. To begin with, we define the cluster centers of each cluster by using K-means algorithm and vector the unclassified data. Then compare the distance between the unclassified data and the cluster centers of each cluster by using a mathematical calculation known as the Euclidean distance (Buttrey & Karo, 2002; Davidson, 2002).
After the VG and VB of the unclassified data are calculated by K-means algorithm, the unclassified data is able to be clustered. When VG<VB, the data has higher similarity with the good customer cluster, and therefore be clustered to the target customer cluster; otherwise be clustered to un-target customer cluster.
This study using K-means algorithm in the second phase because of it won’t be affected by the quality of training data. K-means algorithm clusters data not based on the pre-defined categories but based on the similarity of the data, and the methods in the first phase may produce errors due to the quality of training set. Therefore, in the second phase, we analyze the inconsistent data from the first phase by using K-means algorithm to get the better results.
The Analysis of Case:
This study uses the data of a Portuguese banking institution that from the UCI machine learning database. The bank marketing data set contains 4521 instances and 17 attributes. There use 16 attributes to describe the customer data and the condition of the bank marketing (phone cells), including 7 numeric attributes, 6 categorical attributes and 3 binary attributes. The target attribute represents whether the customers subscribe the long-term bank deposits or
not, including 521 “yes” and 4000 “no”. We define the customer in which has subscribed as the target customer,
and process analysis.
To begin with, we divide the data set into training set and test set. The result of proportion show about 80% and
20% for training set and test set. Training set contains 3604 samples, including 418 “yes” and 3186 “no”. Test set
contains 917 samples, including 103 “yes” and 814 “no”.
The first phase analysis: Support Vector Machine:
In this study, we use the SVM modules of SPSS Modeler to classify, and select polynomial kernel to construct the classification system. After repeated tests and cross-validation, we find that when the value C=2 and Gamma=0.3 will achieve the best classification results. Using support vector machine with set parameter to classify the test set. Fig. 4.1 shows, the average accuracy of test set is 85.5%, the classification accuracy of 817 samples “no” is 91.2%,
the classification accuracy of 103 samples “yes” is 40.8%%, Type I error is 59.2% and Type II error is 8.8%.
Table 4.1: Support vector machine classification result
Original class Classified class
NO YES
NO 742(91.2%) 72(8.8%)
YES 61(59.2%) 42(40.8%)
Neural Network:
In this research, we use the multilayer perception of SPSS Modeler to analyze. In this case, the number of input layer neurons expressed as 16 attributes of customer data, and the number of output layer neurons expressed as target attribute. Setting the hidden layer of multilayer perception to two levels, after repeated tests and cross-validation, we setting the first level of hidden layer to 3, and setting the second level of hidden layer to 4, using the neural network with set parameter to classify test set and calculate the accuracy of classification. Fig. 4.2 shows, the
average accuracy of test set is 90.4%%, the classification accuracy of 817 samples “no” is 96.6%%, the
Table 4.2: Neural network classification result
Original class Classified class
NO YES
NO 786(96.6%) 28(3.4%)
YES 60(58.3%) 43(41.7%)
The second phase analysis:
In this phase, we compare the classification results from support vector machine and neural network. If the two classifications are consistent, the classification will be the finale result whether it consists with the original data or not. Otherwise, the procedure will step into second phase, to analyze data by using K-Means algorithm. First, to divide the customer data into good customer cluster and bad customer cluster. We calculate the VG (value of distance from cluster (good)’s center to data) and VB (value of distance from cluster (bad)’s center to data) of the unclassified data by using K-means algorithm. When VG<VB, the data has higher similarity with the good customer cluster, and will be clustered to the target customer cluster; otherwise will be clustered to un-target customer cluster.
NO.98 customer in Figure 4.1, for example, is the data that classed by support vector machine and neural network. We compared the classification results produced from two classifiers, and found that they are not the same. Next, we calculate the data by using K-means algorithm of SPSS Modeler, and figure out VG=1.852, VB=2.042. If VG<VB, the no.98 customer is similar to the target customer cluster. Therefore, it comes to a conclusion that no.98 customer is clustered to the target customer cluster.
Figure 4.1: K-Means algorithm flowchart
Through support vector machine and neural network classification, there are 814 samples that judgment is same, output them for result. And 103 samples that judgment is not same, the original data as "yes" are 35 samples, as "no" are 68 samples. Importing this data to second phase analysis, through K-Means algorithm, there are 53 samples be clustered to non-target customer cluster, 50 samples be clustered to target customer cluster.
For example, in Table 4.3, the five data are not clustered to the target customer cluster in original. After analyze data by using K-means algorithm, we get the four data that can be clustered to the target customer cluster because of their VG<VB. The rest of the unclassified data may be deduced by analogy.
Table 4.3: Examples of reassigned results
Finally, to verify the effect, we analyze customer data of the two-phase model output by using support vector machine and neural network. As shown in Table 4.4 and Table 4.5, after analysis, we get the classification accuracies as 98.91% and 94.55%. Both of them are higher than the initial classification accuracies from support vector machine and neural network. Also, Type I error and Type II error are reduced. Consequently, the simulations show that two-phase target customer choice model in this study not only increasing the accuracy of classification but also reducing the Type I and Type II error.
Table 4.4: Using SVM to verify the result of two-phase model
Original class Classified class
NO YES
NO 786(99.6%) 3(0.4%)
YES 7(5.5%) 121(94.5%)
Table 4.5: Using NN to verify the result of two-phase model
Original class Classified class
NO YES
NO 772(97.9%) 17(2.2%)
YES 30(23.4%) 98(76.6%)
Conclusion:
Increasing global competition is changing the environment facing most enterprises today. For any enterprise, it is an important issue that how to reduce costs, promote the interests of marketing, or find out the potential customers. In recent years, various data mining methods have been widely used in marketing and customer relationship management fields. If a enterprise is able to collect a lot of customer data and analysis useful information, it will become a leader of the field.
In this research, we presents a two-phase target customer choice model. First, we perform the class predictions by using support vector machine and neural network. Comparing the class predictions, if the judgment is not the same, it will proceed to the next phase. The second phase attempts to analysis the customer data by K-Means algorithm. We cluster the customers by comparing VG and VB. The simulations show that our methods not only increasing the accuracy of classification but also reducing the Type I and Type II error. The proposed approach appears an excellent performance, and shows that this study has contribution on practice and academic value at the same time. To believe firmly, the advantages of our two-phase target customer choice model are helpful to reduce marketing costs, find out the potential customers and increase enterprise profits for the enterprises.
References:
[1] Berry, M.J.A., & Linoff, G. (1996). Mastering Data Mining, the Art and Science of Customer Relationship Management. NY: John Wiley and Sons.
[2] Buttrey, S.E. & Karo, C. (2002). “Using K-nearest- neighbor classification in the leaves of a tree,” Computational Statistics and Data Analysis, 40(1), 27-37.
[3] C. W. Hsu, C. C. Chang and C. J. Lin (2003). “A Practical Guide to Support Vector Classification,” Technical Report, Department of Computer Science and Information Engineering, University of National Taiwan, Taipei, 1-12.
[4] Davidson, I. (2002). “Understanding K-means non-hierarchical clustering,” SUNY Albany Technical Report, 2-25. [5] Fayyad, U. & Piatetsky-Shapiro, G. &Smyth, P. (1996). “From Data Mining to Knowledge Discovery in
Databases,” Advances in Knowledge Discovery and Data Mining, Calif.: AAAI Press, 37–54.
[6] H. Zha, C. Ding, M. Gu, X. He, and H.D. Simon. (2001). “Spectral rlaxation for k-means clustering,” Neural Information Processing Systems vol.14 (NIPS), 1057–1064.
[7] He, J., A. Tan, C. L. Tan, and S. Y. Sung, (2003). “On quantitative evaluation of clustering systems,” Clustering and Information Retrieval Anonymous, 105-134.
[9] J. B. MacQueen (1967). “Some Methods for classification and Analysis of Multivariate Observations,” Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1, 281-297.
[10] Joachims, T. (1998). “Text categorization with support vector machines,” In Proceedings of European conference on machine learning (ECML). Chemintz, DE, 137–142.
[11] Kanungo, T., Mount, D., Netanyahu, N., Piatko, C., Silverman, R., and Wu (2000).“An efficient K-means clustering algorithm: Analysis and implementation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 881–892.
[12] Peppers, D. and Rogers, M. (1999), The One to One Future, Doubleday, N.Y.
[13] Pontil, M. & Verri, A. (1998). “Support vector machines for 3D object recognition.,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(6), 637–646.
[14] S. Moro, R. Laureano and P. Cortez. (2011). “Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology,” In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, 117-121.
[15] Scott, N. (2006). The basis for bibliomining: frameworks for bringing together usage-based data mining and bibliometrics through data warehousing in digital library services. Information Processing and Management, 42, 785-804.
[16] Stern, H. S. (1996). “Neural Networks in Applied Statistics,” Technometrics, 38, 205-216. [17] UC Irvine Machine Learning Repository, http://archive.ics.uci.edu/ml/
[18] Vapnik, V. (1995). The Nature of Statistical Learning Theory, Springer-Verlag, New York.
[19] Vellido, A., Lisboa, P. J. G., & Vaughan, J. (1999). “Neural networks in business: a survey of applications (1992–1998),” Expert Systems with Applications, 17, 51-70.
[20] Yu, G. X., Ostrouchov, G., Geist, A., & Samatova, N.F. (2003). “An SVM-based algorithm for identification of photosynthesis-specific genome features,” In 2nd IEEE computer society bioinformatics conference, CA, USA, 235–243.
[21] Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). “Forecasting with artificial neural networks: the state of the
art,” International Journal of Forecasting, 14, 35-62.
THE USED OF IT BALANCED SCORECARD TO BUILD THE
PERFORMANCE MEASUREMENT MODEL OF ACADEMIC
INFORMATION SYSTEMS (CASE STUDY ACADEMIC
INFORMATION SYSTEM OF SATYA WACANA)
Paskah Ika Nugroho,
Faculty of Economics and Business
Satya Wacana Christian University, Indonesia.
Prihanto Ngesti Basuki,
Faculty of Information Technology
Satya Wacana Christian University
Indonesia.
Evi Maria,
Faculty of Information Technology
Satya Wacana Christian University
Indonesia.
ABSTRACT
The aim of this research is to make a model of performance measurement of academic information system to facilitate the auditors in conducting a periodically performance measurement of Satya Wacana Academic Information System using IT Balanced Scorecard. SI performance measurement model was developed through systematic measures in the form of the action process, reflection, evaluation, and innovation by applying the method of survey research, development, experiments , and evaluation. Performance measurement modeling of Academic Information Systems (SIASAT) in SWCU has been done by making a framework model which was developed by considering the following parameters: (a) the duties and functions of the university, (b) the aspects of university management, (c) the duties and functions of the IT organization in university, (d) the need of information system for academic activities, and (e) the methodology of IT basic framework used, which is the IT Balanced Scorecard (IT-BSC).
Introduction:
The use of Information Technology (IT) in Higher Education institutions especially for the use of information systems and the Internet can not be separated due to the demands of the stakeholders (Indrajit, 2006). IT Management in Higher education institution is a Critical Success Factor (CSF) for leaders and partners of Higher education institutions (Henderi, 2010). However, the complexity of IT implementation makes the leaders of the various levels in the Higher education institutions and stakeholders have difficulty in managing the IT. The complexity of IT implementation in higher education institutions in Indonesia happens because the higher education institution does not have a specific framework model when establishing the information system (Mutyarini and Sembiring, 2006). As a result, the benefits of using IT is not comparable to the investments value which has already been incurred.
Satya Wacana Christian University is one of the universities, which has already used IT as an infrastructure and facility to provide services for students, lecturers and all the staff, and also assists the running of the activities around the work units. In carrying out its main activity, that is to provide educational services, SWCU has supported by IT of Satya Wacana Academic Information Systems (SIASAT). IT management has been applied in SWCU, but it has not been applied using a well-structured method and approach. On the other hand, IT implementation must be controlled because the control provides reasonable assurance to management that the implementation process has been done in accordance with the plans and goals of the organization (Maria, 2011).
Each IT process requires a controlled IT measurement to indicate the performance of IT in achieving the control objectives and facilitate the management to make improvements to the performance of IT. IT performance measurement can be performed by using IT Balanced Scorecard IT where the IT performance is measured from 4 perspectives: corporate contribution, user orientation, operational excellence, and future orientation (Van Grembergen, 2000). IT Balanced Scorecard is an effective method of managing IT organizations as well as evaluating the success and development of the system/application, the development of computer and network investment, quality of products and IT
