MEASURING PUBLIC SATISFACTION FOR GOVERNMENT PROCESS REENGINEERING

Transcription

1 MEASURING PUBLIC SATISFACTION FOR GOVERNMENT PROCESS REENGINEERING Ning Zhang, School of Information, Central University of Finance and Economics, Beijing, P.R.C., Lina Pan, School of Information, Central University of Finance and Economics, Beijing, P.R.C., Abstract Public satisfaction measurement is the best way to evaluate the implementation effects of government process reengineering (GPR). The research on public satisfaction of electronic government is almost adapted from research on customer satisfaction, and is mainly about e-gov websites or various types of public services, while rare attention has been paid to the implementation results of GPR. This paper presented a public satisfaction model for GPR based on the definition of public satisfaction for GPR. An empirical study is conducted through statistics process reengineering in City Municipal Bureau of Statistics. Structural equation model and partial least squares method are used to conduct data analysis. The results show that statistics process reengineering in City Municipal Bureau of Statistics has high degree of public satisfaction. Public satisfaction for GPR is positively and strongly affected by perceived process quality, while perceived process quality is positively and strongly affected by process maturity. Public satisfaction in turn positively and strongly affects public trust. Keywords: government process reengineering, public satisfaction, structural equation model, partial least squares.

2 1 INTRODUCTION Electronic government (e-gov) is the use of information technology (especially Internet) to support government operations, engage citizens, and provide government services (Chandler & Emanuels 2002). Government is the largest information holder and information technology user in the society. The essential of electronic government is a deep change of government management model. Traditional government processes, which lead to low efficiency and coordination difficulty, cannot adapt to electronic government environment. Government process reengineering (GPR), the thought of which comes from business process reengineering (BPR), is the kernel of electronic government (Ye 2007). Public satisfaction measurement is the best way to evaluate the implementation effects of GPR because the final purpose of e-gov is to satisfy the public. The public is the major targets which the government provides products or services for. The public satisfaction which also originates from the business term, customer satisfaction, is a feeling of pleasure or disappointment resulting from comparing a products/services perceived performance or outcome in relation to his or her expectations (Kotler & Keller 2006). The current research work on satisfaction is mainly about customer satisfaction. The research on public satisfaction is almost adapted from research on customer satisfaction, and is mainly about e-gov websites (Liu et al. 2010; Li & Song 2012) or various types of public services (Zou & Ma 2009; Chen & Cao 2013), while rare attention has been paid to the implementation results of GPR. Some research has created performance evaluation index system for GPR (Gu 2008), but not from the view of public satisfaction. 2 THEORETICAL MODEL 2.1 ACSI Model in Public Sectors Customer satisfaction index (CSI) model is widely applied to satisfaction measurement in many countries, and is gradually applied to public sectors. American customer satisfaction index (ACSI) model is the best applied one. ACSI has measured satisfaction with government since the project s inception in 1994 (Morgeson 2013). In 1999, ACSI was chosen as the gold standard measure of citizen satisfaction by the Federal government. The central objective of the model is to explain what influences customer satisfaction (ACSI), and in turn what is influenced by ACSI. In the model, ACSI is embedded in a system of cause-and-effect or structural relationships. All of the variables in the model are measured using multiple indicators (survey questions), increasing their precision and reliability. The ACSI model in public sectors is shown in figure 1. Customer service Customer expectations Customer complaints Process Customer satisfaction Information Figure 1. Perceived quality ACSI model in public sectors Agency trust 2.2 Public Satisfaction Model for GPR Based on the analysis of satisfaction and process reengineering, here we define public satisfaction for GPR as a feeling of pleasure or disappointment resulting from comparing perceived process performance after reengineering in relation to process performance before reengineering (different from comparison between perceived performance and customer expectations in the definition of

3 customer satisfaction). According to the definition, the public satisfaction model for GPR is created as shown in figure 2. Public satisfaction for GPR is affected by both perceived process quality (simplicity, efficiency, cost, etc.) after reengineering and also process quality before reengineering. Process quality is affected by process maturity (clarity, rationality, normalization, etc.), and the public trust (the extent of support to GPR, etc.) is affected by public satisfaction. Perceived process quality before reengineering Process maturity Public satisfaction Public trust Figure 2. Public satisfaction model for GPR Thus, the hypotheses are as follows: H1: process maturity positively affects perceived process quality. H2: perceived process quality after reengineering positively affects public satisfaction. H3: perceived process quality before reengineering negatively affects perceived process quality after reengineering. H4: perceived process quality before reengineering negatively affects public satisfaction. H5: public satisfaction positively affects public trust. 2.3 Latent Variables and Indicators Latent variables and their measurable variables (indicators) in the model are shown in table 1. Latent variables Measurable variables Perceived process Overall evaluation of process before reengineering (x 1 ) quality before Simplicity of process before reengineering (x 2 ) reengineering (ξ 1 ) Efficiency of process before reengineering (x 3 ) Cost of process before reengineering (x 4 ) Perceived process Overall evaluation of process after reengineering (y 1 ) quality after Simplicity of process after reengineering (y 2 ) reengineering (η 1 ) Efficiency of process after reengineering (y 3 ) Cost of process after reengineering (y 4) Process maturity (ξ 2 ) Clarity of information after reengineering (x 5 ) Rationality of process design after reengineering (x 6 ) Normalization of process execution after reengineering (x 7 ) Public satisfaction (η 2 ) Public trust (η 3 ) Support to GPR (y 9 ) Table 1. Perceived process quality after reengineering Overall satisfaction of process after compared to before (y 5 ) Satisfaction of process simplicity after compared to before (y 6) Satisfaction of process efficiency after compared to before (y 7) Satisfaction of process cost after compared to before (y 8 ) Latent variables and their measurable variables 2.4 Parameter Estimation Method Parameter estimation methods for satisfaction models include weighted average method, linear structural relation (LISREL), partial least squares (PLS), etc. PLS regression is a recent technique that

4 generalizes and combines features from principal component analysis and multiple regression analysis (Abdi 2007). It has been gaining interest and use among IS researchers in recent years because of its ability to model latent constructs under conditions of nonnormality and small to medium sample sizes. PLS optimally weights the indicators such that a resulting latent variable estimate can be obtained. The weights provide an exact linear combination of the indicators for forming the latent variable score which is not only maximally correlated with its own set of indicators (as in components analysis), but also correlated with other latent variables according to the structural (i.e. theoretical) model. Therefore, this research chooses PLS as the parameter estimation method for the public satisfaction model for GPR and gets the latent variable score including public satisfaction index. 3 PROCESS REENGINEERING IN BUREAU OF STATISTICS In order to provide publics with full benefits from transactions (especially statistics data collection) over the Internet, City Municipal Bureau of Statics has been implementing statistics process reengineering in several recent years. 3.1 Process Analysis and Evaluation City Municipal Bureau of Statics has city-level institutions, subordinate district/county-level institutions and street/town-level institutions. For each level, departments are divided by national economy industry (such as manufacturing industry, construction industry, wholesale and retail industry) or by profession (such as investment, price, labor wage). Each department was relatively independent and accomplished its own tasks of data collection, data organization, data analysis, and data publication. Although each level and each department had their own autonomy in collecting and processing data, this kind of diverse way of primary statistics process had brought following problems: Statistics survey objects had great difficulties in reporting data to different departments or even different institutions of each level since their reporting ways, reporting periods and data formats were usually different. This often increased unnecessary and extra burden to the survey objects. It was hard for different departments to exchange and summarize statistics data since these data had different structures and format standards. Although statistics institutions had large amount of data resources, these resources could not be utilized fully and shared widely. Diverse way often led to management confusion. It was nearly impossible to unify behaviors of workers from different departments and levels. The phenomena of data missing and data inconsistency occurred frequently, which reduced data quality greatly. 3.2 Process Reengineering In order to solve the above problems, the primary statistics process has been reengineered (as shown in figure 3). A unified working platform has been built. Survey objects report all statistics data through this platform directly. The data are then processed by industry or by profession. With powerful data resources, the platform also provides functions of data integration, data analysis and data publication. Statistics institutions of different levels and different departments work on this platform with different operational limits. Meta data standard is designed and utilized, which unifies data format for each level and each department.

5 Unified working platform Statistics product publication Publics Survey objects collection Basic data processing Summary data integration Integrated database warehouse analysis Decision makers Figure 3. Statistics process reengineering Street/townlevel District/countylevel institutions City-level institutions 3.3 Organizational Change Instead of accomplishing their own tasks of data collection, data organization, data analysis and data publication, city-level, district/county-level, and street/town-level institutions perform the function of supervision in the new process. They take the responsibility of ensuring the timeliness, accuracy and completeness of statistics data for the corresponding level. Street/town-level institutions: query, verify, evaluate and accept data collected from street/townlevel survey objects. District/county-level institutions: query, verify, evaluate and accept data collected from district/county-level survey objects; supervise data quality from street/town-level; give guidance to street/town-level statistics institutions. City-level institutions: query, verify, evaluate and accept data collected from city-level survey objects; supervise data quality from district/county-level; give guidance to district/county-level statistics institutions. 4 DATA COLLECTION AND ANALYSIS 4.1 Collection and Organization City Survey Centre for Public Opinions helped us to carry out telephone survey. According to the population distribution of organization type and subordination, 200 statistics survey objects were randomly selected from different types. 193 questionnaires were obtained, and 65 questionnaires were excluded due to data missing of perceived process quality before reengineering. 128 complete and valid questionnaires were finally used. All the respondents had been engaging in the work of reporting statistics data for at least several years and familiar with both the processes after reengineering and before reengineering. All the questions were designed according to their actual working experience, for example, clarity of information was phrased as clear information tips. All the items were measured with the scale from one to ten. Descriptive statistics were done by SPSS-17, such as the organization type, subordination, and the respondent s degree, age, years engaging in statistics work, etc. The result showed that 86.7 percent of the respondents have the college or bachelor degree; the average age of the respondents was 41, mainly between 26 and 55; the average years engaging in statistics work were 8.35, mainly between 2 and 12. Internal consistency reliability of survey instruments was also analyzed using SPSS. Cronbach s alpha of the total scale was It would be decreased dramatically if any one of the items was excluded, which indicated that the questionnaire has good reliability.

6 4.2 PLS Path Analysis PLS path analysis was done by SmartPLS-2. Assumed that expected value was 0, variance was 1.0, initial weight was 1.0, stopping criterion for iteration is 1.0E-5. The model tended to be stable after five iterations, which indicated that it has good convergence. Path coefficients are shown in figure 4. Figure 4. Path coefficient diagram The public satisfaction index is , which means the public is very satisfied with the implementation results of statistics process reengineering. The process quality index before reengineering is While after reengineering, it increases to , and the process maturity index is , which means both the process quality and the process maturity are improved dramatically after reengineering. The public trust is very high (93.516) due to the high satisfaction. 4.3 Goodness-of-fit Analysis Goodness-of-fit indexes of latent variables are shown in table 2, which indicate the model has good fitness. All the values of AVE are greater than 0.70, all the values of composite reliability are greater than 0.90, and all the values of Cronbach s Alpha are greater than AVE Composite Reliability R Square Cronbach s Alpha Communality Redundancy η η η ξ ξ Table Significance Test Goodness-of-fit indexes of latent variables Bootstrapping technique was adopted to test significance by SmartPLS-2. Generally, significance tests are passed if t-statistics is greater than 2. Factor loading coefficients and external weights of measurable variables: all t-statistics are greater than 2. Path coefficients of latent variables: t-statistics for path coefficient from ξ 1 to η 1 and path coefficient from ξ 1 to η 2 are less than 2 (0.9175, ), which indicate that both the effect from perceived process quality before reengineering to perceived process quality after reengineering and the effect from perceived process quality before reengineering to public satisfaction are not significant.

7 Total effect of the model: t-statistics for perceived process quality before reengineering (ξ 1 ) to perceived process quality after reengineering (η 1 ), public satisfaction (η 2 ), and public trust (η 3 ) are all less than 2 (0.9175, , ). Therefore, hypotheses H3 and H4 are not supported. 5 MODEL REVISION AND CONCLUSTION Although the empirical results show that perceived process quality before reengineering positively affects perceived process quality after reengineering and negatively affects public satisfaction, the effects are not significant. This is probably because the measurement instruments for public satisfaction have already considered the comparison between after and before. Therefore, perceived process quality before reengineering (ξ 1 ) could be removed from the model. We also tried to add a new path from process maturity (ξ 2 ) to pubic satisfaction (η 2 ) to the model. The revised model (as shown in figure 5) passes the significance test. Figure 5. Revised public satisfaction model for GPR Based on the analysis of the revised public satisfaction model for GPR, we conclude that public satisfaction for GPR is positively and strongly affected by both process quality (including simplicity, efficiency, and cost) and process maturity (including clarity, rationality, and normalization). Public satisfaction for GPR in turn positively and strongly affects public trust. ACKNOWLEDGEMENTS This research work was supported by National Social Science Foundation of China under Grant No. 13AXW010, Social Science Foundation of Beijing under Grant No. 11JGC136, Talent Cultivation Project of Beijing under Grant No. 2012D , and Discipline Construction Foundation of Central University of Finance and Economics. References Abdi, H. (2007). Partial least square regression, in Encyclopedia of measurement and statistics (edited by Neil Salkind). Sage.

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