RESEARCH. Human Capital Benchmarking in Further Education Preliminary Findings. Laurie Bassi and Daniel McMurrer McBassi & Company

Transcription

1 RESEARCH Human Capital Benchmarking in Further Education Preliminary Findings Laurie Bassi and Daniel McMurrer McBassi & Company Research Report RW88

2 Research Report RW88 Human Capital Benchmarking in Further Education Preliminary Findings Laurie Bassi and Daniel McMurrer McBassi & Company The views expressed in this report are the authors and do not necessarily reflect those of the Department for Education and Skills. Mcbassi and company 2006

3 Contents 1. Executive Summary 1 2. Measuring Human Capital Management 3 Measurement Framework 3 The Consortium 4 3. Overall Benchmarking Findings 6 4.The Relationship Among HCM, Employee Commitment and Learner Outcomes 12 5.Recommendations, Lessons Learned, and Suggestions for Improvement 14 Recommendations 14 Lessons Learned from Pilot Phase of Project 15 Suggestions for Improvement 15 Appendix A. Index and Factor Definitions 17 Indices 18 Factors 19 Appendix B. Index and Factor Scores 21 Appendix C. List of Tables and Figures 30

4 1. Executive Summary Over the course of the past five years, DfES has implemented a variety of policies to improve success rates within further education (FE) colleges. Considerable progress has been made, and targets for improvement have been achieved ahead of schedule. DfES, however, lacks a clear understanding of the key drivers behind the improvements that have occurred. With that in mind, DfES launched a pilot project, the Human Capital Benchmarking Consortium, in January 2006, as a preliminary step toward identifying the key people-related (or human capital ) drivers behind learner success rates. This report summarizes the findings that have emerged to date from the Consortium. Five FE colleges Bury, Chesterfield, East Berkshire, Park Lane, and Southwark participated in this pilot project by deploying McBassi & Company s Human Capital Capability Scorecard (HCCS), a rigorous, research-based benchmarking measurement methodology. This methodology has the following capabilities: Efficiently quantifies variations in the maturity of departments and functions within and across FE colleges on key measures of the development and management of employees (i.e., human capital management) Benchmarks these measures against best practice Statistically links these measures to a variety of measures of learner success rates and employee commitment, thereby identifying the specific aspects of human capital management (HCM) that are the most important drivers of these outcomes Provides a foundation for making data-driven decisions for improving learner success through more effective HCM On a 5-point scale, the overall HCM maturity score for the organisations participating in the Benchmarking Consortium ranged from a 2 to a 3 revealing relatively modest variation in the average maturity levels across the colleges. There is, however, a much wider variation across departments and functions with the overall HCM scores for these units ranging from 1 to 5 within a single, participating FE college. Importantly, the HCM maturity scores for teaching staff were consistently lower (an overall average of 2) than the HCM scores for non-teaching staff (an overall average score of 3). And the overall level of employee commitment/engagement, particularly among the teaching staff, is quite low relative to levels observed in other organisations that have deployed the HCCS framework (including other educational institutions). Statistical analysis was undertaken to identify the specific items that determine employee commitment/engagement within the FE colleges. This analysis, in combination with information on the strengths and weaknesses of each of the participating FE colleges on these factors (i.e., their HCM scores), was then used to develop fact-based, prioritized recommendations for improving learner outcomes by taking actions to improve the level of commitment among FE college staff (and the teaching staff, in particular). A summary of these recommendations can be found at the end of this report. 1

5 Although the relationship between HCM scores and available learning outcomes data (from the 2003/04 academic year) was also analysed, no significant relationships or actionable results were found in that version of the analysis. We would recommend that the statistical analysis be conducted again, using more recent learning outcomes data when they become available. [Because the only full set of available clean learner outcomes data were from two years earlier, the analysis was backward looking, as it examined the relationship between current human capital factors and learner results from two years earlier. It was thus not surprising that no significant statistical relationships exist between the older outcomes data and the current HCM data, which are designed to be forward-looking.] The remainder of this report provides detailed scores and more discussion of the points described above. Chapter 2 outlines the methodology that was used for collecting data for the DfES Human Capital Benchmarking Consortium. Chapter 3 provides an overview of the findings that emerged from the benchmarking analysis of all participating colleges. 1 Chapter 4 outlines the findings that emerged from the statistical analysis of the relationship between HCM scores, employee commitment, and learner outcomes. Chapter 5 provides recommendations, summarises the lessons learned during this pilot project, and makes suggestions for improvements that might be made if DfES chooses to proceed with the Benchmarking Consortium on a larger scale. Appendices provide definitions of the foundational factors that served as the basis for data collection and benchmarking, as well as detailed tables of index and factor scores. In order to protect the privacy of colleges scores, the five participating colleges were randomly assigned the designations Colleges A, B, C, D, and E, which have been applied throughout the data tables and discussion that follow. 1 Please note that additional detail on each FE college is included in the customised reports provided to the individual colleges; these reports are also compiled in an accompanying supplemental report provided to DfES directly. 2

6 2. Measuring Human Capital Management The Human Capital Capability Scorecard is an intensively researched measurement system that is designed to: Efficiently quantify key measures of the development and management of employees Benchmark these measures against best practice Statistically links these measures to learner outcomes and employee commitment, thereby identifying those management and development practices and processes that are the most important drivers of these outcomes Provide a foundation for making data-driven decisions for improving results through more effective management and development of employees Measurement Framework The conceptual framework that serves as the basis for all data collection is noted below in Figure 2-1. The framework consists of 23 foundational factors (such as accountability and training). 1 These 23 foundational factors are then aggregated into 5 human capital indices (ranging from leadership practices to learning capacity), which are then linked to a measures of employee commitment and learner outcomes. Measures of these 23 foundational factors and 5 human capital indices were created for each of the FE colleges participating in the Benchmarking Consortium by deploying easy-touse employee questionnaires that solicit information as follows: All selected employees were invited to respond to an online questionnaire that gathered information on approximately 70 different HCM items, which were then aggregated to create 18 foundational factor scores and 5 index scores A single employee from the HR (or related) function who is knowledgeable about the college s overall systems and processes for various people-related functions was asked to respond to a short questionnaire regarding the use of systems and processes within the college as a whole (these responses were used to create 5 different systems/process scores for each college) Figure 2-1. Human Capital Factors and Corresponding Indices 1 Definitions of the 5 HCM indices and the 23 foundation factors can be found in Appendix A. 3

7 The Consortium Five FE colleges participated in the Benchmarking Consortium: Bury College Chesterfield College East Berkshire College Park Lane College Southwark College For purposes of the benchmarking scores and analysis throughout this report, one college (College C) was excluded due to its low number of responses, which raised major questions about how meaningful its data could be. Employees from a variety of departments and functions within each college were invited to respond to the survey. Across the four participating colleges with sufficiently high numbers of respondents to be included in the benchmarking analysis, data were collected from employees in dozens of different departments and functions. Each participating college used the same data collection instruments, as described above (with the exception of minor wording modifications that were designed not to change the meaning of any of the questions). Separate score breakdowns were calculated for all departments and functions that met at least the minimum response threshold. 1 1 The minimum response threshold for a department or other unit to be included in the tables and analysis was that at least 5 employees from that unit completed the questionnaire. As noted below, caution should be exercised in interpreting the results for units with particularly low response levels or rates. 4

8 Data Scoring and Analysis All data gathered from employees are analyzed within a maturity/capability scoring algorithm. The resultant maturity scores for the 5 Human Capital Indices, the 23 detailed factors range from 1 to 5. A score of 1 (which is colour coded red) indicates a low level of capability, while a 5 (which is colour coded blue) indicates world-class capability, as indicated in Figure 2-2. These scores are then statistically linked both to employee commitment and to learner outcomes (both within and across the participating colleges), thereby creating an empirical mechanism for linking variations in HCM to variations in these outcomes. This linkage then provides a basis for assessing which factors and indices are the most critical drivers of those results. Figure 2-2. The Maturity Scale Scale Level 1 Label Not Addressed Description Little or no attempt to address the stated area or factor 2 Initiating 3 Maturing 4 Extending 5 Optimizing Early, non-systematic attempts to address at least some component of the stated area or factor are demonstrated Average, or baseline, capability demonstrated, representing a good foundation for improvement in the stated area or factor Systematically demonstrated that organisation is beginning to extend the capability of its human capital as it relates to the stated area or factor Consistently demonstrated superior capability to optimise organisation s human capital in the stated area or factor 5

9 3. Overall Benchmarking Findings The overall HCM maturity score for the colleges participating in the Benchmarking Consortium is a 3, indicating an average level of maturity for the colleges as a group. 1 The HCM maturity scores for teaching staff, however, were consistently lower (an overall average of 2) than the HCM scores for non-teaching staff (an overall average score of 3). The average index scores for all respondents in the five colleges are shown in Table Table 3-1a. Index Scores: All Respondents NOTE: For colleges with response rates of less than 40 percent, caution should be exercised in interpreting all scores (here and in following tables), as the responses may not be representative of the college (or group) as a whole. 50%* * To improve comparability with other colleges, College A response rate excludes sessional staff (rate would be 36% with sessional staff included). Table 3-1b. Factor Scores: All Respondents 50%* * To improve comparability with other colleges, College A response rate excludes sessional staff (rate would be 36% with sessional staff included). 1 Note that this average excludes College C because the number of responses at that college was too low to generate meaningful data. 2 For the purposes of allowing colleges to compare their data to a benchmark of all participating institutions, these data on the overall set of all respondents in each college should not be used. This is because different colleges included somewhat different mixes of teaching and non-teaching staff in the survey group, which could affect overall scores. Thus, official comparison benchmarks were not created for the overall group. For most accurate comparisons, cross-college comparison and benchmarking should only be done separately for teaching staff and non-teaching staff. 6

10 Table 3-1b. Factor Scores: All Respondents, cont'd There is wide variation in HCM maturity across departments and functions within colleges, as well as across standard definitions of areas of learning across all of the colleges. The overall HCM scores for these various groups range from 1 to 5, sometimes even within a single FE college. The index score findings indicate that, as a group, these colleges have the following attributes: Significant differences in HCM maturity as reported by teaching versus non-teaching staff (with teaching staff consistently reporting lower levels of maturity) Notably low levels of maturity with regard to issues associated with Employee Engagement among teaching staff The factor tables indicate that the participating colleges generally have the following attributes: Very high levels of maturity with regard to Conditions Relatively high levels of maturity with regard to Job Design, Knowledge Availability, and Training Lower levels of maturity with regard to Executives Communication, Executives Inclusiveness, and Commitment to Employees Tables 3-2 and 3-3 on the following pages report the HCM indices and foundation factors that emerge from the employee data collection instrument for teaching and non-teaching staff, respectively. 1 1 To improve comparability with other colleges in these tables, sessional staff from College A are excluded from both Teaching and Non-Teaching Staff groups. 7

11 Table 3-2a. Index Scores: Teaching Staff Table 3-2b. Factor Scores: Teaching Staff 8

12 Table 3-3a. Index Scores: Non-Teaching Staff Table 3-3b. Factor Scores: Non-Teaching Staff 9

13 Few consistent differences in the HCM scores were observed when the data were broken down by a variety of attributes of respondents, including the average age of the students taught, full-time vs. part-time, job tenure, and total years in the industry. Table 3-4 reports the overall average score from all participating colleges on the 5 HCM Systems/Process factors (information on systems maturity was gathered from one representative of each college). 1 These tables indicate that, as a group, these colleges report the following: Relatively high maturity on the systems that support Learning Capacity and Workforce Optimization Relatively low maturity on the systems that support Leadership Practices Table 3-4. Maturity Scores for 5 Systems Factors: Overall, by College 1 Only one respondent per college was asked to provide Systems/Process maturity information because, in most organisations, most employees are not fully informed of the operating details of these organisation-wide systems (for definitions, see the Systems components of Figures A-2 to A-6). These data may thus be less reliable than the questionnaire-based data elsewhere in this report (and this may also account for some of the apparent difference between the maturity of human capital systems and the maturity in other human capital areas in the participating colleges). 10

14 Table 3-5 summarizes the data on the two specific questions 1 that were included in the employee questionnaire as measures of individual employee commitment, and also compares those scores to an external benchmark. The comparison indicates that employees responses to these two questions are relatively low, particularly among teaching staff. 2 Questions of this nature typically generate quite high responses. Thus, while the DfES colleges averages indicate that a majority of respondents agree or strongly agree with the two statements, what is notable is that a larger-than-average percentage of respondents disagree or strongly disagree with the statements, resulting in average scores that are lower than the external benchmark. Table 3-5. Average Responses on Employee Commitment Questions Commitment Question DfES: Teaching Staff DfES: Non- Teaching Staff External Benchmark Overall, I am satisfied with my job This is an organisation to which I am willing to contribute effort over and beyond that which is required Finally, Table 3-6 summarizes the scores of the two customized questions on employee qualifications that were added to the survey. 3 Responses to the first question indicate a generally moderate level of confidence in the qualifications of the respondents colleagues. 4 (On the second question, employees rate their own skill qualifications much more highly than those of their colleagues, suggesting that the self-rating question is not reliable.) Table 3-6. Average Responses on Employee Qualification Questions Qualification Question DfES: Teaching Staff DfES: Non- Teaching Staff My colleagues hold the full qualifications to carry out their jobs with complete effectiveness. I hold the full qualifications to carry out my job with complete effectiveness This table uses a 1-to-100 scale, where a neutral average response is a 50, a maximum ( strongly agree ) response is a 100, and a minimum ( strongly disagree ) response is a 0. 2 The external benchmark includes all organisations (inside and outside the United Kingdom) that have used the McBassi questionnaire since these questions were added. Based on the small number of organisations from the UK that are included in this group, scores from UK respondents tend to be slightly lower than those from respondents in other countries. It should be noted that the overall DfES averages are lower than this UK average as well, especially for the effort over and beyond question. 3 As in the previous table, this table uses a 1-to-100 scale, where a neutral average response is a 50, a maximum ( strongly agree ) response is a 100, and a minimum ( strongly disagree ) response is a 0. 4 In the section that follows, the specific questions that are the top predictors of employee commitment are discussed. Neither of the two qualification questions emerged as a top predictor overall, nor in specific colleges. 11

15 4. The Relationship Among HCM, Employee Commitment and Learner Outcomes The relationships between the colleges HCM scores and two categories of outcomes data were analyzed: 1. Learning outcomes a. Learners completion rate (percentage of course starters who complete a course) b. Learners achievement rate (percentage of those completing the course who achieve the necessary level of qualification) c. Learners success rate (percentage of course starters who achieve the necessary level of qualification) 2. Measures of individual employee commitment, as reported by questionnaire respondents A variety of statistical techniques (comparison of means, correlation analysis, multivariate regression analysis) were used to examine the relationship between HCM scores (at the index, factor, and individual item level) and each of the three learning outcomes listed above. Each area of learning at each college represented a separate observation for analysis purposes. Overall, there were no consistent, statistically significant relationships that emerged between the HCM variables and any of the three separate measures of learner outcomes. While the absence of statistically significant results is disappointing, it is not surprising, given that the analysis was backward-looking, with the only available full set of clean learner outcome data two years old (academic year 2003/04). Consequently, final analysis of the determinants of learner outcomes must await the availability of 2005/06 learner data. It was still possible, however, to analyze the determinants of employee commitment, since both HCM variables and employee commitment measures were captured from questionnaire respondents at the same time. For the purpose of this analysis, each individual questionnaire respondent represented a separate observation. Employee commitment in this analysis is defined as an index measure of the following two items, as self-reported by each employee who completed the questionnaire: The employee s willingness to contribute discretionary effort to the college The employee s overall job satisfaction level Figure 4-1 shows the magnitude of the relationship between individuals overall HCM score (broken into deciles) and the average level of commitment reported for each decile. It demonstrates that individuals who reported higher levels of HCM maturity in their department and college also have higher levels of employee commitment. 12

16 Figure 4-1. Relationship Between HCM Score and Employee Commitment Level The ten specific items (from the employee questionnaire) that emerged as the most important determinants of the employee commitment index (as measured by correlation coefficients) are as follows: 1 Table 4-1. HCM Factors Most Closely Associated With Employee Commitment Item Correlation Coefficient My job is interesting and meaningful to me I have confidence that my college's managers can guide our college to success I have confidence that our senior management team can guide our college to success Work in our college is well organised so I can do a quality job Employees and our senior management team work in partnership to achieve the college's goals. Employees are treated with respect by our senior management team My job makes good use of my skills and talents Employees and managers work in partnership to achieve my college's goals My college values and supports learning and development My college has well-defined procedures to get work done A comparable analysis was done separately for each of the participating FE colleges, and the results of that analysis were provided to each college in their customized report (a copy of which is available in the supplement to this report). 13

17 5. Recommendations, Lessons Learned, and Suggestions for Improvement By combining two pieces of information on each of the individual HCM items (its correlation with the employee commitment measure and the overall score received by that item from all respondents), it is thus possible to develop a set of fact-based, prioritized recommendations for taking corrective action that target those specific areas that have two qualities: (1) important in determining employee commitment (i.e., a high correlation with the employee commitment index), and (2) are areas of general weakness (i.e., a low average score from all respondents). Given that employee commitment is known to be an important determinant of organisational results, and that the overall level of commitment is low among the participating FE colleges (especially among teaching staff), these recommendations are worthy of attention. In combination with additional recommendations that will emerge from future analysis of the direct determinants of learner outcomes (which, as noted above, must await the availability of up-todate outcomes data), they should provide DfES with concrete guidance on those areas of priority and focus that are particularly promising for improving student achievement, based on summary data from all colleges. In the section that follows, these recommendations, generated by analysis of each individual item, are then clustered into logically cohesive groupings. What is striking about these groupings is that, based on previous analyses of similar data in other organisations, they are quite similar to what we would have expected had they been based on a statistical analysis of learner outcome data. Hence, we feel confident in recommending that these are issues of significance for DfES consideration (even prior to the availability of current learner outcome data). Ultimately, these recommendations all have to do with the capabilities of the managers and leaders within the FE colleges to manage and develop their workforce. Recommendations Leadership development and succession planning The development of collaborative and inclusive leaders is a high priority. Focus needs to be on the following: Developing the capacity of leaders to work in partnership with teaching staff The ability to communicate openly, honestly and respectfully, to inspire and motivate staff, and to set and communicate clear expectations for staff Developing succession systems for identifying and grooming next-generation leaders Managerial skills The critical managerial skills that require strengthening include the ability to achieve the following: Define and improve work processes that enable staff to accomplish their tasks efficiently Work collaboratively and identify and eliminate unnecessary barriers Create environments that foster knowledge sharing, continuous improvement, and innovation 14

18 HR Policies and Practices The human resource functions need to be strengthened to ensure the following: Promotions are based on merit and competence Employees are recognized for their accomplishments Employees have a development plan that helps them achieve their career objectives Lessons Learned from Pilot Phase of Project The methodology used in the DfES Human Capital Benchmarking Consortium appears to be well-suited for application in the FE college system. [The ultimate test of the methodology, however, must await the availability of clean, up-to-date learner outcome data.] Few modifications were required of the data collection instruments, and the standard implementation processes worked well. Nonetheless, there were three lessons learned during the pilot phase of the project that would help improve the project if it is implemented on a larger scale: 1. While FE colleges appear to be interested in the methodology and approach, many of them are undertaking other surveys of their own and/or had scheduling conflicts (e.g., vacation schedules) that made it difficult or impossible for them to participate in the DfES Benchmarking Consortium. 2. Employee morale and commitment is quite low (at least within the sample of FE colleges included in this pilot project), which makes employees less likely to take the time to respond to an employer-supported questionnaire. In addition, compared with other organisations with which we have worked, FE college employees seemed unusually suspicious about the potential that their responses will not remain anonymous. Hence, getting employees to respond to the questionnaire requires more than the usual amount of time and effort. We found that response rates did increase sometimes significantly after the questionnaire deadline was extended by some of those colleges that started the questionnaire process earlier and therefore had significant additional time available for questionnaire responses. 3. Clean learner outcome data are only available to DfES with a considerable time lag. Moreover, there were some differences between the colleges audited data and the data provided from DfES s central database. Suggestions for Improvement Should DfES decide to proceed with the Human Capital Benchmarking Consortium on a larger scale, we would suggest that the following changes be made moving forward: 1. DfES should announce its intent to undertake a Benchmarking Consortium to the FE colleges well in advance of launching the project. That way those FE colleges that are interested in participating will have the opportunity to substitute the DfES survey for one that they would otherwise undertake and/or schedule the DfES survey so that it does not conflict with other surveys or inconvenient times (e.g., holidays, inspections). 2. More time is needed for the data collection phase of the project. This will be particularly true if the number of colleges is increased significantly, since it will be necessary to have sufficient flexibility to accommodate the potential schedule conflicts of a large number of colleges. 15

19 3. Refinements need to be made to the messages that are sent to employees, requesting them to respond to the survey. We would suggest the following: a. The principal of each FE college should be involved from the beginning in making the request that employees respond to the survey b. Each college should invest energy to enlist the support of department managers by educating them of the benefits of encouraging their employees to participate (e.g., specific data on HCM factors in their individual department if there are sufficient responses) c. Special attention should be given to ensure employees that their responses will be kept anonymous. 4. Alternatives for acquiring clean learner outcome data in a timely manner need to be considered. One option might be to gather the data directly in a consistent manner from the colleges themselves. 16

20 Appendix A. Index and Factor Definitions 17

21 Indices Figure A-1. Definitions and Interpretation of HC Indices Index Definition Strategic importance of monitoring Leadership Practices Employee Engagement Knowledge Accessibility Workforce Optimization Managers and leaders communication, performance feedback, supervisory skills, demonstration of key organisational values, efforts and ability to instil confidence. The organisation s capacity to engage, retain, and optimise the value of its employees hinges on how well jobs are designed, how employees time is used, and the commitment that is shown to employees. The extent of the organisation s collaborativeness and its capacity for making knowledge and ideas widely available to employees. The organisation s success in optimizing the performance of its workforce by establishing essential processes for getting work done, providing good working conditions, establishing accountability, and making good hiring choices. Leadership and managerial practices are the foundation for ensuring that human capital is developed, sustained, and deployed successfully. These practices thus lay the foundation for the achievement of all organisational goals. Many researchers have concluded that this factor is the most important driver of an organisation s ability to retain its top performing people. The most successful organisations are those that proactively manage talent retention by creating desirable work environments, including designing jobs purposefully, ensuring that employees time is well used, recognizing and valuing employees and their work, and providing opportunities for employee advancement. Organisations that capture, apply, and re-use knowledge and best practices among departments and divisions, and that have successful, collaborative team structures are best able to leverage their knowledge and talent for business results. Since human capital represents a major portion of most organisations total operating cost, the quality of the practices, systems, and processes for ensuring that employees are effective is a foundational determinant of business results. Learning Capacity The organisation s overall ability to learn, innovate, and continually improve. An organisation s ability to respond effectively to constant (and inevitable) changes in its environment hinges on its ability to learn. Hence, training, development and innovation must be valued and supported in order for an organisation to have the capacity to respond to changing conditions and consistently achieve strategic goals. 18

22 Factors Figure A-2. Definitions of HC Factors: Leadership Practices Communication (Managers) Communication (Executives) Definition Managers are open and honest in their communications; have an effective process in place for communicating news, strategies and goals to employees and; do a good job of letting employees know what is expected of them. Executives are open and honest in their communications; have an effective process in place for communicating news, strategies and goals to employees and; do a good job of letting employees know what is expected of them. Inclusiveness (Managers) Managers seek and use employee input, work in partnership with employees, and treat them with respect. Inclusiveness (Executives) Supervisory Skills Executive Leadership Systems Senior executives seek and use employee input, work in partnership with employees, and treat them with respect. Managers demonstrate organisational values, eliminate unnecessary barriers to getting work done, provide constructive feedback, provide employees with performance appraisals, and inspire confidence. Senior executives demonstrate organisational values, eliminate unnecessary barriers to getting work done, provide constructive feedback, and inspire confidence. Systems and processes for identifying and developing the next generation of leaders and ensuring smooth leadership transitions. Figure A-3. Definitions of HC Factors: Employee Engagement Definition Job Design Commitment to Employees Time Systems Work is effectively organised, makes good use of employees talents and skills, and is interesting and meaningful. Employees have appropriate responsibility to determine how best to do their work, and creative job design is used to help makes jobs fit employees needs. Employees jobs are secure, employees are recognised for their accomplishments, and employees are provided with opportunities for advancement. The work load allows employees to do their jobs right, make thoughtful decisions, and achieve an appropriate balance between work and home. Systems and processes that help to retain good performers by continually evaluating trends in employee engagement. The information from these systems is used to determine the key drivers of productivity and customer satisfaction. 19

23 Figure A-4. Definitions of HC Factors: Knowledge Accessibility Definition Availability Collaboration & Teamwork Information Sharing Systems People have the information they need to do their jobs, the necessary manuals and job tools are available, and there are procedures in place that enable employees to access training when they need it. Teamwork is encouraged and enabled, places are provided for people to meet informally, and time is set aside time for people to share with and learn from one another. Best practices and tips are shared and improved, and circulated across departments. Systems and processes are in place that collect and store information and make it available to all employees who might need it. Figure A-5. Definitions of HC Factors: Workforce Optimization Processes Conditions Accountability Definition Well-defined processes exist to get work done, employees are well trained on these processes. And efforts are made to continually improve these processes. Employees have access to the materials and technologies they need to be effective, and working conditions contribute to good performance. Employees are held accountable for producing quality work, promotion is based on competence, poor performance is appropriately handled, and employees trust their coworkers to get the job done. Hiring Decisions Selection is based on skill requirements; new hires receive adequate orientation, induction, and a description of required skills; and employees provide input into hiring decisions. Systems Systems and processes are in place for managing employees performance and talents. This system enables managers and leaders to view the overall proficiency of the workforce, helps employees realise their full performance potential in their current jobs, identifies development opportunities for those experiencing performance difficulties, and prepares motivated employees to progress in their career fields. Figure A-6. Definitions of HC Factors: Learning Capacity Factor Innovation Training Development Value & Support Systems Definition New ideas are welcomed, employees are encouraged to find new and better ways to do work, and employees input is sought in solving problems. Training is practical and supports organisational goals and employees receive training on work-related technologies. Employees have formal development plans in place, and these plans are used to help them achieve their career goals. The behaviour of leaders consistently demonstrates that learning is valued in our organisation, and managers consistently make learning a priority. A learning management system exists that automates the administration of all aspects of training/learning events, provides reports to management, and includes features such as content management and skill or competency management. 20

24 Appendix B. Index and Factor Scores 21

25 Table B-1. Index Scores: Teaching Staff, by DfES Learning Area 22

26 Table B-2. Factor Scores: Teaching Staff, by DfES Learning Area 23

27 Table B-3. Index Scores: Teaching Staff, by Ages Taught Table B-4. Factor Scores: Teaching Staff, by Ages Taught 24

28 NOTE: Full-time/part-time status data were not available from all participating colleges; data below include only those colleges for which that information was available. Table B-5. Index Scores: Teaching Staff, by Full/Part-Time Status Table B-6. Factor Scores: Teaching Staff, by Full/Part-Time Status 25

29 Table B-7. Index Scores: Teaching Staff, by Tenure at College Table B-8. Factor Scores: Teaching Staff, by Tenure at College 26

30 Table B-9. Index Scores: Teaching Staff, by Total Years of FE Experience Table B-10. Factor Scores: Teaching Staff, by Total Years of FE Experience 27

31 Table B-11. Index Scores: Non-Teaching Staff, by Tenure at College Table B-12. Factor Scores: Non-Teaching Staff, by Tenure at College 28

32 Table B-13. Index Scores: Non-Teaching Staff, by Total Years of FE Experience Table B-14. Factor Scores: Non-Teaching Staff, by Total Years of FE Experience 29

33 Appendix C. List of Tables and Figures 30

34 List of Tables and Figures Figure 2-1. Human Capital Factors and Corresponding Indices...3 Figure 2-2. The Maturity Scale...5 Table 3-1a. Index Scores: All Respondents...6 Table 3-1b. Factor Scores: All Respondents...6 Table 3-2a. Index Scores: Teaching Staff...8 Table 3-2b. Factor Scores: Teaching Staff...8 Table 3-3a. Index Scores: Non-Teaching Staff...9 Table 3-3b. Factor Scores: Non-Teaching Staff...9 Table 3-4. Maturity Scores for 5 Systems Factors: Overall, by College...10 Table 3-5. Average Responses on Employee Commitment Questions...11 Table 3-6. Average Responses on Employee Qualification Questions...11 Figure 4-1. Relationship Between HCM Score and Employee Commitment Level...13 Table 4-1. HCM Factors Most Closely Associated With Employee Commitment...13 Figure A-1. Definitions and Interpretation of HC Indices...18 Figure A-2. Definitions of HC Factors: Leadership Practices...19 Figure A-4. Definitions of HC Factors: Knowledge Accessibility...20 Table B-1. Index Scores: Teaching Staff, by DfES Learning Area...22 Table B-2. Factor Scores: Teaching Staff, by DfES Learning Area...23 Table B-3. Index Scores: Teaching Staff, by Ages Taught...24 Table B-4. Factor Scores: Teaching Staff, by Ages Taught...24 Table B-5. Index Scores: Teaching Staff, by Full/Part-Time Status...25 Table B-6. Factor Scores: Teaching Staff, by Full/Part-Time Status...25 Table B-7. Index Scores: Teaching Staff, by Tenure at College...26 Table B-8. Factor Scores: Teaching Staff, by Tenure at College...26 Table B-9. Index Scores: Teaching Staff, by Total Years of FE Experience...27 Table B-10. Factor Scores: Teaching Staff, by Total Years of FE Experience...27 Table B-11. Index Scores: Non-Teaching Staff, by Tenure at College...28 Table B-12. Factor Scores: Non-Teaching Staff, by Tenure at College...28 Table B-13. Index Scores: Non-Teaching Staff, by Total Years of FE Experience...29 Table B-14. Factor Scores: Non-Teaching Staff, by Total Years of FE Experience

35 Copies of this publication can be obtained from: DfES Publications P.O. Box 5050 Sherwood Park Annesley Nottingham NG15 0DJ Tel: Fax: Minicom: Online: Mcbassi and company 2006 Produced by the Department for Education and Skills Ref No: RW88