Effects of staff participation, morale, and shortage on organisational performance: An international survey
Jehanzeb R. Cheema and Muhammad Asrar-ul-Haq
University of Illinois at Urbana-Champaign, USA
Prior research has suggested that staff-centred organisational factors such as participation, morale and shortage can have a significant effect on organisational outcomes. However, relatively little attention has been paid to cross-country examination of these relationships specifically for educational organisations such as schools, colleges, and universities. In this study we looked at the link between staff-centred organisational factors and organisational performance, within the special context of high schools across nationally representative samples from 64 countries and economies. Our empirical results indicate large cross-country variations in the effect of factors such as staff participation, morale and shortage, on school performance. Implications are discussed.
Briggs and Richardson (1992) examined the internal and external reactions to low morale using a sample of 78 school teachers and administrators enrolled in educational supervision courses, and found that teacher morale was significantly related with individual and organisational performance. Low teacher morale was associated with internal reactions such as more frustration, lack of confidence, resistance to change, excessive absence from work, lack of teaching quality and high turnover, and external reactions such as backbiting, open hostility, bickering, and high teacher turnover. Causes for low morale included factors such as lack of recognition, overload of extra duties, criticisms, and autocratic administration. Morale has spill-over effects, and a teacher with low morale can cause discouragement and lower morale of other teachers and co-workers around him which has the potential to negatively affect student performance. On the other hand, high morale is associated with improvements in efficiency, quality, cooperation, and productivity among co-workers (Kerlin & Dunlap, 1993). Educational processes and systems depend heavily on teachers, and school performance can suffer if teachers are not happy and satisfied. Hollinger (2010) emphasised the importance of school management in developing and maintaining teacher morale. If teacher morale is high, the school environment will be conducive to learning, collaborative, engaging, and productive. This will in turn reduce teacher attrition and turnover, and consequently improve school performance.
Koys (2001) and Dartey-Baah and Harlley (2010) argued that staff job satisfaction is a key to organisational success and that correct use of human resource management practices enhances the effectiveness of organisations. Satisfied and happy staff members are important because they positively affect organisational performance. Weakliem and Frenkel (2006) examined the relationship between staff morale and workplace productivity using a representative sample of Australian workplaces, and found that morale had a linear and positive relationship with organisational productivity. When morale was high, individual work effort and productivity increased and ultimately improved organisational productivity and performance. However, the degree of influence of morale on productivity varied as a function of work effort, job characteristics, and nature of the organisation.
Previous research suggests that staff participation does not develop organisational commitment among staff members, but rather enhances it. In addition, staff participation plays a pivotal role in organisational change processes. Cohen and Kirchmeyer (1995) examined the relationship between organisational commitment and participation in nursing staff of two Canadian hospitals and found that staff participation played a significant role in fostering staff commitment. Staff involvement in short term decision making processes has a relatively weaker effect on individual organisational commitment as compared to their participation in long term decision-making processes. In order to maximise organisational performance, staff participation in long term decision making processes is thus recommended. Unterrainer, Palgi, Weber, Iwanowa and Oesterreich (2011) argued that staff participation in decision-making processes develops feelings of belongingness to the organisation and increases job satisfaction, staff loyalty and productivity, and decreases turnover. However, such results can only be achieved if the participation practices are embedded with true spirits, otherwise such practices have the potential to backfire and lead to resistance and discouragement among staff members that can in turn lead to poor performance (Heller, 1998, 2003; Strauss, 2006).
Staff participation is related to job satisfaction (Kim, 2003; Wood, van Veldhoven, Croon & de Menezes, 2012), and increase in job satisfaction leads to improvement in both individual and organisational performance (Bhatti & Qureshi, 2007; Brewer & Kellough, 2008; Ouedraogo & Leclerc, 2013; Vermeeren, Kuipers & Steijn, 2014; Wood et al., 2012; Yadollah & Zahra, 2010).
According to Ingersoll (2001) there is an increasing trend of teacher shortage and turnover in the U.S. education sector, especially at the school level, which ultimately affects school performance, with primary factors responsible for such shortage being compensation, work environment, stress, and management behaviour. The author argued that traditional education initiatives such as teacher recruitment programs cannot solve staffing issues (and improve school performance) unless the teacher turnover rate can be lowered. High teacher turnover rate is an indicator of underlying problems that can lead to turmoil and affect organisational functions. High turnover has particularly serious consequences in workplaces such as schools that need intensive interaction among participants and which rely upon commitment, continuity, and cohesion among staff. According to the authors, high turnover rates in academic institutions cause staffing issues that directly affect student achievement, teacher quality, and ultimately lower institutional performance (Ingersoll & Smith, 2003).
In light of the preceding review of past research, and based on the premise that findings from other types of organisations may not be applicable to high schools due to often significant inter- and intra-country differences across organisations, the aim of this study is to examine the effect of staff-centred organisational traits on organisational performance, specifically for high schools. In a way our study is a follow-up of Cheema and Fuller-Hamilton (2017), who showed that school performance was linked to teacher participation, morale, and shortage, albeit in a race-based context. Our specific research questions are: (1) Is there a significant effect of teacher morale on school performance? (2) Is there a significant effect of teacher participation on school performance? and (3) Is there a significant effect of teacher shortage on school performance?
The primary sampling unit in our analysis was school (N = 17,293) with the number of sampled schools ranging between 12 for Liechtenstein and 1,451 for Mexico (M = 270.20, SD = 254.30). The proportion of public (government funded) schools in country/ economy sub-samples ranged from .07 for Hong Kong (China) to 1.00 for Israel, Perm (Russia), and Russia (M = .81, SD = .23). Based on sampling weights provided in the PISA 2012 school data file, the 17,293 surveyed schools are representative of a total of 417,360 schools in the 64 participating countries/economies (M = 6,521, SD = 12,013).
Organisational performance
We used standardised student literacy scores reported in the PISA 2012 data file as a measure of school performance. This is appropriate because improvement in student outcomes such as literacy and achievement scores is a primary objective of most, if not all, educational policies that are directly or indirectly implemented at the school level. We averaged student literacy scores in mathematics, reading, and science in order to obtain a single literacy score for each student. Literacy score for a school was computed as the average literacy score of all students sampled from that school. This mean school literacy score was standardised with a mean of 500 and standard deviation of 100 for the 34 OECD countries in our sample, and ranged from 347.73 for Peru to 574.91 for Shanghai (China) with M = 458.12 (SD = 51.84) for the 64 countries/economies in our sample.
Staff centred factors: Teacher morale
This scale is based on four items in the school questionnaire that asked the school principal about the morale of teachers employed by the school (OECD, 2013). A sample item included, "Think about the teachers in your school. How much do you agree with the following statement? Teachers work with enthusiasm." Response categories were 1 (strongly agree), 2 (agree), 3 (disagree), and 4 (strongly disagree). The reliability of this scale in our overall sample was .83 with country level mean reliability being .79 (SD = .06). Item responses were inverted and teacher morale was standardised to have a mean of 0 and a standard deviation of 1 for OECD countries included in the sample. Thus, higher scores on this scale are indicative of higher teacher morale. Teacher morale ranged from -0.64 for Japan to 0.70 for Austria (M = -0.01, SD = 0.32).
Staff centred factors: Teacher participation
This scale is based on twelve items in the school questionnaire that asked the school principal about the participation of teachers in decision making at school (OECD, 2013). A sample item asked whether or not the teachers had considerable responsibility for tasks such as, "Deciding on budget allocations within the school." Response categories were 1 (tick), and 2 (no tick). The reliability of this scale in our overall sample was .78 with country level mean reliability being .69 (SD = .10). Item responses were inverted and teacher participation was standardised to have a mean of 0 and a standard deviation of 1 for OECD countries included in the sample. Thus, higher scores on this scale are indicative of greater teacher participation. Teacher participation ranged from -0.56 for Vietnam to 1.98 for Hong Kong (M = -0.06, SD = 0.66).
Staff centred factors: Teacher shortage
This scale is based on four items in the school questionnaire that asked school principals about the shortage of qualified teachers at their school, for mathematics, reading, science, and other subjects (OECD, 2013). A sample item included, "Is your school's capacity to provide instruction hindered by a lack of qualified science teachers?" Response categories were 1 (not at all), 2 (very little), 3 (to some extent), and 4 (a lot). The reliability of this scale in our overall sample was .86 with country level mean reliability being .76 (SD = .14). Teacher shortage was standardised to have a mean of 0 and a standard deviation of 1 for OECD countries included in the sample. Higher scores on this scale are indicative of greater teacher shortage. Mean teacher shortage ranged from -1.04 for Poland to 1.44 for Thailand (M = 0.05, SD = 0.50).
School socioeconomic status (SES)
This variable captures the socioeconomic, cultural, and other differences across schools and countries included in our sample. The value of this variable was computed separately for each school and represents the average economic and sociocultural scores of all students sampled from that school. The student level SES scores were reported in the PISA 2012 student data file and were based on five factors: family wealth, home educational resources, cultural possessions, parental education, and parental occupation (OECD, 2012a). School SES was standardised with a mean of 0 and a standard deviation of 1 for OECD countries, and in our overall sample ranged from -2.40 for Vietnam to 0.61 for Iceland (M = -0.45, SD = 0.65). Higher values of this variable are indicative of higher school SES.
where Y is mean literacy score for school i in country j, α and β are intercept and slope coefficients respectively for country j, and ε is the school- and country-specific error term. The second regression equation added three additional predictors, teacher morale, teacher participation, and teacher shortage to (1), and is represented by the expression in (2).
where Xk represents the K = 3 teacher-centred predictors. For each regression equation we computed the multiple R2 value and evaluated the significance of change in R2 separately for each country. For the second regression equation we also computed adjusted R2 and ƒ2 in order to facilitate effect size interpretation for the full model. Overall, we estimated a total of 128 regression equations, two for each country/economy in our sample. We also computed zero order Pearson product moment coefficients of correlation in order to examine the bivariate relationship of each individual independent variable with school literacy score.
School sampling weights were incorporated in all of our computations, tests of hypothesis were evaluated at 5% level of significance, and underlying assumptions of all estimated regression models were evaluated. All effect size interpretations are based on Cohen (1992).
In order to isolate the effect of the three teacher-related factors on literacy we tested the hypothesis that the difference in R2 between regression equations (1) and (2) was significantly different from zero. Results of this test are summarised in column four of Tables 1 and 2. The ΔR2 values for OECD countries ranged from a minimum of 0.2% for Spain to a maximum of 21.1% for Norway (M = 2.99%, SD = 4.26%). Thus, for Norway the three teacher-related factors explained 21.1% of the total variation in literacy over and above that explained by school SES alone. The comparable figure for Spain was only 0.2%. The ΔR2 values for non-OECD countries ranged from a minimum of 0.3% for Uruguay to a maximum of 25.2% for Liechtenstein (M = 4.11%, SD = 5.30%). The mean ΔR2 value for the entire sample of 64 countries/economies was 3.52% (SD = 4.77%). It is important to note here that not all of the ΔR2 values were significantly different from zero. This is especially true for countries/economies with small sample sizes. For example, although the ΔR2 value of 25.2% was quite large for Liechtenstein, the hypothesis H0: ΔR2 = 0 could not be rejected given the very small number of schools sampled from this country (weighted n = 12). On the other hand, the somewhat very small ΔR2 value of 0.3% for Romania turned out to be significant given the large weighted n value of 2,995 for this country.
Country | R12 | R22 | ΔR2 | p | Adj. R22 | ƒ22 | Effect size |
Australia | .556 | .565 | .009 | < .001 | .565 | 1.30 | large |
Austria | .572 | .644 | .072 | < .001 | .644 | 1.81 | large |
Belgium | .583 | .616 | .033 | < .001 | .615 | 1.60 | large |
Canada | .162 | .203 | .041 | < .001 | .202 | 0.25 | large |
Chile | .610 | .653 | .043 | < .001 | .653 | 1.88 | large |
Czech Republic | .618 | .652 | .034 | < .001 | .652 | 1.88 | large |
Denmark | .784 | .805 | .021 | < .001 | .805 | 4.13 | large |
Estonia | .153 | .213 | .059 | < .001 | .206 | 0.27 | large |
Finland | .394 | .398 | .005 | .094 | .396 | 0.66 | large |
France | .529 | .536 | .006 | < .001 | .536 | 1.15 | large |
Germany | .671 | .686 | .014 | < .001 | .686 | 2.18 | large |
Greece | .303 | .311 | .009 | < .001 | .310 | 0.45 | large |
Hungary | .503 | .519 | .016 | < .001 | .519 | 1.08 | large |
Iceland | .137 | .152 | .016 | .512 | .125 | 0.18 | medium |
Ireland | .709 | .713 | .005 | .016 | .712 | 2.49 | large |
Israel | .407 | .421 | .014 | < .001 | .419 | 0.73 | large |
Italy | .520 | .525 | .005 | < .001 | .525 | 1.10 | large |
Japan | .661 | .676 | .015 | < .001 | .675 | 2.08 | large |
Korea | .314 | .461 | .147 | < .001 | .460 | 0.85 | large |
Luxembourg | .788 | .796 | .008 | .702 | .773 | 3.89 | large |
Mexico | .351 | .354 | .004 | < .001 | .354 | 0.55 | large |
Netherlands | .346 | .359 | .013 | < .001 | .357 | 0.56 | large |
New Zealand | .362 | .423 | .062 | < .001 | .418 | 0.73 | large |
Norway | .072 | .283 | .211 | < .001 | .280 | 0.40 | large |
Poland | .484 | .488 | .004 | < .001 | .487 | 0.95 | large |
Portugal | .614 | .625 | .011 | < .001 | .623 | 1.66 | large |
Slovak Republic | .622 | .626 | .004 | < .001 | .625 | 1.67 | large |
Slovenia | .203 | .227 | .025 | .001 | .222 | 0.29 | large |
Spain | .453 | .456 | .002 | < .001 | .455 | 0.84 | large |
Sweden | .622 | .646 | .024 | < .001 | .645 | 1.82 | large |
Switzerland | .177 | .183 | .005 | .012 | .181 | 0.22 | medium |
Turkey | .597 | .623 | .026 | < .001 | .623 | 1.65 | large |
United Kingdom | .650 | .666 | .016 | < .001 | .666 | 1.99 | large |
USA | .710 | .749 | .039 | < .001 | .749 | 2.99 | large |
Mean | .478 | .507 | .030 | .039 | .505 | 1.36 | |
Note. N = 11,222. Subscripts 1 and 2 refer to regression equations 1 and 2 respectively. Effect size interpretation based on ƒ2 as defined by Cohen (1992): Small, 0.02; Medium, 0.15; Large, 0.35. R12 significant for all countries/economies at 5% level of significance. |
Country | R12 | R22 | ΔR2 | p | Adj. R22 | ƒ22 | Effect size |
Argentina | .429 | .443 | .014 | < .001 | .443 | 0.79 | large |
Brazil | .370 | .378 | .007 | < .001 | .378 | 0.61 | large |
Bulgaria | .352 | .361 | .009 | .001 | .359 | 0.56 | large |
Colombia | .488 | .508 | .020 | < .001 | .507 | 1.03 | large |
Costa Rica | .570 | .594 | .025 | < .001 | .592 | 1.47 | large |
Croatia | .322 | .342 | .019 | .013 | .335 | 0.52 | large |
Hong Kong-China | .224 | .232 | .008 | .160 | .226 | 0.30 | large |
Indonesia | .237 | .269 | .031 | < .001 | .269 | 0.37 | large |
Jordan | .123 | .175 | .051 | < .001 | .173 | 0.21 | medium |
Kazakhstan | .322 | .339 | .017 | < .001 | .338 | 0.51 | large |
Latvia | .268 | .276 | .008 | .054 | .272 | 0.38 | large |
Liechtenstein | .271 | .522 | .252 | .369 | .249 | 1.09 | - |
Lithuania | .357 | .376 | .019 | < .001 | .373 | 0.60 | large |
Macao-China | .074 | .102 | .028 | .739 | .012 | 0.11 | - |
Malaysia | .498 | .513 | .015 | < .001 | .513 | 1.05 | large |
Montenegro | .497 | .537 | .039 | .263 | .499 | 1.16 | large |
Perm-Russia | .401 | .459 | .057 | < .001 | .456 | 0.85 | large |
Peru | .562 | .584 | .023 | < .001 | .584 | 1.41 | large |
Qatar | .309 | .484 | .175 | < .001 | .470 | 0.94 | large |
Romania | .576 | .579 | .003 | < .001 | .579 | 1.38 | large |
Russia | .242 | .263 | .021 | < .001 | .263 | 0.36 | large |
Serbia | .493 | .524 | .032 | < .001 | .521 | 1.10 | large |
Shanghai-China | .586 | .603 | .018 | < .001 | .601 | 1.52 | large |
Singapore | .549 | .604 | .055 | < .001 | .595 | 1.53 | large |
Taiwan | .741 | .756 | .015 | < .001 | .755 | 3.09 | large |
Thailand | .002 | .055 | .054 | < .001 | .055 | 0.06 | small |
Tunisia | .114 | .191 | .078 | < .001 | .189 | 0.24 | medium |
UAE | .282 | .317 | .036 | < .001 | .314 | 0.46 | large |
Uruguay | .757 | .760 | .003 | .049 | .759 | 3.16 | large |
Vietnam | .190 | .292 | .101 | < .001 | .292 | 0.41 | large |
Mean | .374 | .415 | .041 | .055 | .399 | 0.91 | |
Note. N = 6,071. Subscripts 1 and 2 refer to regression equations 1 and 2 respectively. Effect size interpretation based on ƒ2 as defined by Cohen (1992): Small, 0.02; Medium, 0.15; Large, 0.35. R12 significant for all countries/economies except Liechtenstein and Macao (China) at 5% level of significance. |
The last column of Tables 1 and 2 summarises the overall effect of school SES and the three teacher-centred factors on literacy for OECD and non-OECD countries respectively. With a few exceptions this effect was large and statistically significant. The only cases where both R12 and ΔR2 were simultaneously not significantly different from zero were those of Liechtenstein and Macao (China). Thus, for only these two countries neither school SES nor the three teacher-centred factors had any effect on literacy.
In the non-OECD sample, the effect of school SES on school performance ranged between -0.01 SD for Thailand and 0.88 SD for Taiwan (Mβ = 0.57, SDβ = 0.20); the effect of teacher morale on school performance ranged between -0.24 SD for Liechtenstein and 0.46 SD for Qatar (Mβ = 0.02, SDβ = 0.14); the effect of teacher participation on school performance ranged between -0.20 SD for Liechtenstein and 0.19 SD for Singapore (Mβ = 0.04, SDβ = 0.10); and the effect of teacher shortage on school performance ranged between -0.30 SD for Liechtenstein and 0.19 SD for Perm (Russia) (Mβ = -0.01, SDβ = 0.11).
Country | SES | Morale | Part. | Shrt. | SES | Morale | Part. | Shrt. |
Australia | 0.73*** | -0.06*** | -0.03 | -0.07*** | .75 | -.09 | .21 | -.29 |
Austria | 0.70*** | 0.07*** | -0.19*** | -0.24*** | .76 | .10 | -.15 | -.32 |
Belgium | 0.76*** | 0.08*** | 0.02 | -0.14*** | .76 | .16 | .27 | -.11 |
Canada | 0.38*** | 0.04** | 0.16*** | -0.11*** | .40 | .01 | .21 | -.16 |
Chile | 0.75*** | 0.02* | -0.05*** | -0.22*** | .78 | .06 | .21 | -.38 |
Czech Republic | 0.75*** | 0.01 | -0.07*** | -0.18*** | .79 | .13 | -.01 | -.34 |
Denmark | 0.85*** | -0.06*** | -0.01 | 0.14*** | .89 | -.26 | -.15 | .26 |
Estonia | 0.41*** | 0.14** | 0.15*** | -0.11* | .39 | .10 | .17 | -.10 |
Finland | 0.63*** | -0.06* | 0.03 | -0.01 | .63 | .06 | .11 | -.25 |
France | 0.72*** | -0.03*** | 0.08*** | ~0 | .73 | .04 | .18 | .01 |
Germany | 0.79*** | -0.10*** | -0.03*** | -0.09*** | .82 | -.15 | .09 | -.31 |
Greece | 0.54*** | -0.02 | 0.07*** | -0.06** | .55 | .03 | .08 | -.12 |
Hungary | 0.71*** | -0.12*** | 0.02* | 0.06*** | .71 | -.10 | .11 | -.02 |
Iceland | 0.31*** | -0.04 | 0.09 | -0.08 | .37 | -.13 | .18 | -.18 |
Ireland | 0.86*** | 0.01 | -0.01 | 0.07** | .84 | -.11 | .29 | -.10 |
Israel | 0.66*** | -0.09*** | 0.03 | -0.06* | .64 | ~0 | .16 | .08 |
Italy | 0.73*** | -0.01 | -0.04*** | 0.05*** | .72 | -.02 | .07 | ~0 |
Japan | 0.84*** | -0.13*** | -0.04*** | -0.02** | .81 | -.02 | .25 | -.01 |
Korea | 0.50*** | ~0 | 0.38*** | 0.15*** | .56 | -.02 | .46 | .06 |
Luxembourg | 0.88*** | 0.08 | 0.05 | 0.03 | .89 | .15 | .21 | -.33 |
Mexico | 0.59*** | -0.04*** | 0.04*** | -0.01** | .59 | -.03 | .04 | -.20 |
Netherlands | 0.59*** | 0.02 | 0.05* | 0.12*** | .59 | .01 | -.06 | .11 |
New Zealand | 0.57*** | -0.09* | -0.04 | -0.20*** | .60 | -.16 | .14 | -.37 |
Norway | 0.20*** | 0.01 | 0.47*** | 0.18*** | .27 | .05 | .48 | .01 |
Poland | 0.69*** | 0.02* | 0.04*** | -0.04*** | .70 | -.03 | .14 | -.09 |
Portugal | 0.76*** | 0.01 | 0.10*** | -0.02 | .78 | -.10 | .26 | -.12 |
Slovak Republic | 0.77*** | 0.06*** | -0.01 | -0.02 | .79 | .25 | -.06 | -.24 |
Slovenia | 0.49*** | -0.13*** | 0.04 | -0.09* | .45 | -.03 | .09 | .02 |
Spain | 0.66*** | -0.01 | 0.05*** | -0.01 | .67 | .05 | .26 | -.09 |
Sweden | 0.83*** | -0.05*** | -0.08*** | 0.09*** | .79 | -.09 | .03 | -.15 |
Switzerland | 0.43*** | -0.02 | 0.07** | 0.03 | .42 | .04 | .01 | .07 |
Turkey | 0.75*** | -0.11*** | 0.12*** | 0.09*** | .77 | -.08 | .34 | -.08 |
United Kingdom | 0.76*** | 0.05*** | 0.09*** | -0.05*** | .81 | .04 | .33 | -.34 |
USA | 0.75*** | 0.17*** | 0.08*** | -0.11*** | .84 | .32 | .33 | -.32 |
Mean | 0.66 | -0.01 | 0.05 | -0.03 | .67 | ~0 | .16 | -.13 |
Note. N = 11,222. Part. = Participation. Shrt. = Shortage. Cohen's (1992) effect size guidelines for interpretation of zero-order correlations: Small, .10; Medium, .30; Large, .50. *p < .05. **p < .01. ***p < .001. |
Country | SES | Morale | Part. | Shrt. | SES | Morale | Part. | Shrt. |
Argentina | 0.64*** | 0.08*** | 0.08*** | 0.06*** | .65 | .08 | .16 | .07 |
Brazil | 0.58*** | -0.02*** | 0.05*** | -0.06*** | .61 | -.10 | .18 | -.28 |
Bulgaria | 0.60*** | -0.02 | 0.06* | 0.09*** | .59 | .01 | .11 | -.03 |
Colombia | 0.70*** | 0.12*** | -0.01* | -0.07*** | .70 | .04 | .13 | -.20 |
Costa Rica | 0.76*** | 0.08** | 0.10*** | 0.14*** | .75 | .02 | .22 | -.04 |
Croatia | 0.60*** | 0.09* | -0.10* | -0.04 | .57 | .01 | .05 | -.15 |
Hong Kong-China | 0.44*** | -0.02 | 0.02 | -0.08 | .47 | -.14 | .23 | -.22 |
Indonesia | 0.44*** | 0.05*** | 0.09*** | -0.13*** | .49 | .07 | .18 | -.26 |
Jordan | 0.30*** | 0.04 | 0.15*** | -0.12*** | .35 | .14 | .25 | -.22 |
Kazakhstan | 0.58*** | -0.09*** | -0.01 | 0.09*** | .57 | -.08 | .02 | .04 |
Latvia | 0.52*** | 0.07* | -0.05 | 0.02 | .52 | .01 | -.05 | .09 |
Liechtenstein | 0.54 | -0.24 | -0.20 | -0.30 | .52 | -.40 | -.01 | -.48 |
Lithuania | 0.57*** | 0.08** | 0.11*** | -0.02 | .60 | .14 | .19 | -.09 |
Macao-China | 0.22 | -0.10 | 0.11 | -0.03 | .27 | -.12 | .24 | -.10 |
Malaysia | 0.71*** | 0.01 | 0.07*** | 0.11*** | .71 | -.08 | .08 | .03 |
Montenegro | 0.75*** | -0.15 | -0.08 | -0.14 | .71 | -.02 | .08 | -.09 |
Perm-Russia | 0.62*** | 0.10*** | 0.14*** | 0.19*** | .63 | .13 | .17 | .14 |
Peru | 0.69*** | 0.10*** | 0.09*** | -0.06*** | .75 | .22 | .33 | -.27 |
Qatar | 0.37*** | 0.46*** | 0.05 | 0.02 | .56 | .61 | .11 | -.21 |
Romania | 0.76*** | 0.04** | 0.04** | 0.04*** | .76 | -.07 | .11 | -.03 |
Russia | 0.46*** | 0.01* | 0.10*** | -0.09*** | .49 | .11 | .23 | -.13 |
Serbia | 0.77*** | 0.18*** | -0.03 | 0.03 | .70 | .01 | .22 | -.35 |
Shanghai-China | 0.74*** | -0.01 | 0.09*** | -0.08*** | .77 | .13 | .18 | -.26 |
Singapore | 0.74*** | 0.09 | 0.19*** | 0.11* | .74 | .18 | .23 | -.06 |
Taiwan | 0.88*** | -0.12*** | 0.02 | 0.03* | .86 | -.02 | .22 | -.17 |
Thailand | -0.01 | 0.16*** | 0.14*** | ~0 | -.04 | .19 | .17 | -.03 |
Tunisia | 0.37*** | -0.22*** | -0.12*** | 0.13*** | .34 | -.20 | -.09 | .07 |
UAE | 0.46*** | 0.10** | 0.17*** | -0.02 | .53 | .28 | .26 | -.12 |
Uruguay | 0.86*** | 0.05** | 0.02 | 0.01 | .87 | .21 | .19 | -.24 |
Vietnam | 0.50*** | -0.24*** | -0.13*** | -0.17*** | .44 | -.21 | -.01 | -.13 |
Mean | 0.57 | 0.02 | 0.04 | -0.01 | 0.58 | .04 | .15 | -.12 |
Note. N = 6,071. Part. = Participation. Shrt. = Shortage. Cohen's (1992) effect size guidelines for interpretation of zero-order correlations: Small, .10; Medium, .30; Large, .50. *p < .05. **p < .01. ***p < .001. |
In order to get an overall feel of how various predictors in equation (2) affect school performance the direction and significance of effects presented in Tables 3 and 4 are summarised in Table 5. These figures show that school SES had a positive and significant effect on school performance in 61 of the 64 countries/economies in our sample. The effect of teacher-related factors on school performance represented a mixed bag, with the effect of teacher morale being generally negative in OECD countries and positive in non-OECD countries, the effect of teacher participation being generally positive in both OECD and non-OECD countries, and the effect of teacher shortage being generally negative in OECD countries and not significant in non-OECD countries. These results suggest that the effect of teacher-related factors on school performance varies greatly across countries.
Sample | Direction of effect | School SES | Teacher-related factors | ||
Morale | Participation | Shortage | |||
OECD | Positive | 34 | 9 | 16 | 9 |
Negative | 0 | 13 | 7 | 17 | |
No effect | 0 | 12 | 11 | 8 | |
Non-OECD | Positive | 27 | 15 | 16 | 10 |
Negative | 0 | 6 | 5 | 8 | |
No effect | 3 | 9 | 9 | 12 | |
Note. The figures in this table represent numbers of countries/economies. Total number of sampled countries/economies = 64. |
Our empirical results have a number of implications. First, school SES turned out to be the single most important predictor of school performance. Since our school SES variable was based on averaged individual student level attributes such as parental education, parental occupation, family wealth, etc., this shows the importance of individual level characteristics (even when averaged by school) in explaining the variation in mean literacy at the school level. The large, positive, and significant effect of school SES on school performance was almost universal which lends credence to the idea that no matter how well-funded schools are, their impact on student learning has a limit and that a significant proportion of variability in literacy is directly attributable to student level factors.
Second, there was large variability in the observed effects of teacher-centred predictors on school performance. For some countries such as the U.S. these effects matched prediction, with teacher morale and participation having positive effects, and teacher shortage having a negative effect on school performance. On the other hand, for some countries these effects were either not significant or had unexpected signs. For example, the estimates for Denmark showed a negative effect of teacher morale on school performance, no effect of teacher participation, and a positive effect of teacher shortage. For this country the combined effect of these three predictors was an increase of 2.1% in the amount of total variation in school performance explained over and above that explained by school SES alone. In layman's terms our results suggest that teacher-centred factors do not have a large effect on high school performance in a country like Denmark. Such cross-country differences in the effect of teacher-related factors on school performance are important as they indirectly point to the existence of country- and organisation-specific differences in socio-cultural practices. In other words, what works for one country or organisation may not be applicable to others. The large variability in cross-country regression estimates in our study certainly supports the importance of country-specific studies of teacher-related sources of variance in school performance.
Third, in spite of the large variability in the effects of teacher-centred factors on school performance, we note that all three teacher-related factors considered in this study (morale, participation, and shortage) had large and significant partial effects in the expected direction for several countries. For example, using 0.1 SD as cutoff for a significant partial effect in the expected direction, teacher morale raised school performance in Estonia, Colombia, Qatar, Thailand, Serbia and USA; teacher participation raised school performance in Canada, Jordan, Korea, Lithuania, Norway, Perm (Russia), Singapore, Thailand, Turkey and UAE; and teacher shortage lowered school performance in Austria, Belgium, Canada, Chile, Czech Republic, Estonia, Indonesia, Jordan, New Zealand, USA and Vietnam. These results point to the importance of staff-centred factors in explaining variation in organisational performance.
Although our study generated significant results it has several limitations. First, the three teacher-centred measures, morale, participation, and shortage, are based on survey items administered to school principals and not to school teachers. It is plausible that teacher responses to some of these questions could have been different, especially in cases where rivalry between teachers and school administration is strong. Unfortunately, given the nature of data used in this study, there is no way for us to test this hypothesis. Thus, the inferences based on these data are reliable only to the extent that school principals provided accurate responses to survey items. Second, although the countries represented in our study form a fairly diverse sample, our empirical results should not be generalised to countries not included in this sample. Given the large variability in the effect of predictors included in this study on school performance, such generalisation may result in inaccurate inferences and predictions. Third, we looked at the relationship between organisational performance and staff-centred factors in the special context of high schools. Thus, our empirical results and corresponding conclusions may not be applicable to other types of educational organisations such as primary or elementary schools, colleges, universities, etc.
There are several directions in which work presented in this study can be extended. One way to do this is to sample schools from additional countries that were not included in our sample. As noted earlier, the presence of large variability in effects of predictors observed in our empirical results points to the importance of country-specific studies that take into consideration the country- and organisation-specific sociocultural factors that affect organisational performance without worrying about validity of cross-country comparisons. A second way is to replicate our method in other educational settings such as primary and elementary schools, and institutions of higher education.
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Country | n | School literacy score | School SES | Teacher-related factors | |||||||
Morale | Participation | Shortage | |||||||||
M | SD | M | SD | M | SD | M | SD | M | SD | ||
Australia | 758 | 501.13 | 58.44 | 0.15 | 0.47 | 0.06 | 0.96 | 0.33 | 0.99 | 0.23 | 1.07 |
Austria | 186 | 448.47 | 73.05 | -0.22 | 0.61 | 0.70 | 0.78 | 0.03 | 0.98 | 0.08 | 0.95 |
Belgium | 276 | 473.31 | 84.15 | -0.08 | 0.57 | -0.36 | 0.95 | 0.34 | 0.84 | 0.21 | 0.97 |
Canada | 870 | 510.80 | 52.23 | 0.26 | 0.52 | 0.26 | 0.94 | -0.05 | 0.99 | -0.21 | 0.93 |
Chile | 220 | 393.40 | 66.14 | -0.81 | 0.94 | -0.22 | 1.04 | -0.28 | 1.05 | 0.86 | 1.28 |
Czech Republic | 242 | 486.50 | 65.34 | -0.17 | 0.41 | -0.16 | 0.78 | 0.25 | 0.85 | -0.31 | 0.78 |
Denmark | 295 | 502.74 | 67.18 | 0.44 | 0.53 | 0.31 | 0.95 | 0.06 | 1.07 | -0.09 | 0.72 |
Estonia | 205 | 516.60 | 41.83 | -0.14 | 0.52 | 0.13 | 0.96 | 0.18 | 0.99 | 0.08 | 0.75 |
Finland | 307 | 505.41 | 72.04 | 0.18 | 0.48 | 0.23 | 0.83 | 0.12 | 0.91 | -0.35 | 0.76 |
France | 212 | 449.95 | 65.46 | -0.30 | 0.43 | -0.43 | 0.94 | 0.26 | 0.62 | -0.18 | 0.82 |
Germany | 196 | 476.04 | 79.44 | -0.04 | 0.51 | -0.02 | 0.95 | 0.29 | 1.00 | 0.56 | 0.87 |
Greece | 186 | 426.32 | 65.80 | -0.43 | 0.71 | -0.28 | 1.10 | -0.77 | 0.57 | -0.35 | 0.85 |
Hungary | 198 | 443.15 | 71.70 | -0.50 | 0.70 | 0.07 | 1.00 | 0.33 | 0.74 | -0.62 | 0.67 |
Iceland | 127 | 482.33 | 44.78 | 0.61 | 0.42 | 0.48 | 0.87 | 0.12 | 0.83 | 0.29 | 0.82 |
Ireland | 167 | 503.56 | 45.10 | 0.00 | 0.42 | 0.32 | 1.01 | 0.13 | 0.78 | -0.08 | 0.85 |
Israel | 161 | 446.85 | 85.21 | 0.01 | 0.49 | 0.13 | 0.97 | 0.01 | 1.01 | 0.47 | 1.14 |
Italy | 1,097 | 453.94 | 78.39 | -0.25 | 0.69 | -0.60 | 0.95 | 0.33 | 0.68 | 0.13 | 0.97 |
Japan | 190 | 514.16 | 71.38 | -0.22 | 0.39 | -0.64 | 0.94 | -0.90 | 1.13 | -0.25 | 0.86 |
Korea | 156 | 523.95 | 51.31 | -0.03 | 0.37 | -0.31 | 1.03 | 0.16 | 1.06 | -0.08 | 0.95 |
Luxembourg | 42 | 488.85 | 57.12 | 0.13 | 0.61 | 0.04 | 0.77 | -0.09 | 0.86 | 1.01 | 0.93 |
Mexico | 1,451 | 395.56 | 49.83 | -1.47 | 1.10 | 0.19 | 1.01 | -0.82 | 0.76 | 0.62 | 1.05 |
Netherlands | 159 | 493.30 | 79.76 | 0.16 | 0.40 | -0.20 | 0.83 | 0.05 | 0.86 | 0.51 | 0.86 |
New Zealand | 154 | 506.77 | 64.01 | -0.06 | 0.47 | 0.26 | 0.99 | 0.76 | 1.01 | 0.20 | 1.02 |
Norway | 179 | 499.01 | 58.67 | 0.39 | 0.29 | 0.35 | 0.96 | -0.10 | 0.83 | 0.43 | 0.80 |
Poland | 181 | 533.73 | 69.18 | -0.17 | 0.61 | -0.16 | 0.91 | 0.53 | 0.52 | -1.04 | 0.23 |
Portugal | 185 | 466.78 | 55.91 | -0.64 | 0.71 | -0.24 | 1.00 | -0.51 | 0.69 | -0.79 | 0.66 |
Slovak Republic | 231 | 452.36 | 67.86 | -0.38 | 0.59 | -0.19 | 0.82 | 0.50 | 0.75 | -0.16 | 0.76 |
Slovenia | 318 | 449.91 | 63.80 | -0.28 | 0.60 | -0.36 | 0.80 | 0.41 | 0.68 | -0.69 | 0.61 |
Spain | 880 | 492.03 | 41.95 | -0.21 | 0.55 | -0.36 | 0.99 | -0.23 | 0.72 | -0.71 | 0.66 |
Sweden | 207 | 486.43 | 70.07 | 0.29 | 0.49 | 0.48 | 0.88 | 0.54 | 0.82 | -0.05 | 0.87 |
Switzerland | 383 | 507.30 | 52.32 | 0.14 | 0.48 | 0.37 | 0.89 | -0.13 | 1.05 | 0.05 | 0.88 |
Turkey | 169 | 431.00 | 76.46 | -1.83 | 0.78 | -0.28 | 1.21 | -1.32 | 0.94 | 0.95 | 0.95 |
UK | 479 | 508.18 | 60.54 | 0.32 | 0.45 | 0.42 | 0.88 | 0.55 | 0.79 | -0.23 | 0.85 |
USA | 155 | 473.91 | 70.58 | 0.02 | 0.67 | 0.07 | 1.00 | -0.35 | 1.11 | -0.50 | 0.80 |
Mean | 330 | 477.76 | 64.03 | -0.15 | 1.00 | 0.00 | 0.94 | 0.02 | 0.87 | 0.00 | 0.85 |
Country | n | School literacy score | School SES | Teacher-related factors | |||||||
Morale | Participation | Shortage | |||||||||
M | SD | M | SD | M | SD | M | SD | M | SD | ||
Argentina | 220 | 389.91 | 55.94 | -0.88 | 0.69 | -0.02 | 0.92 | -0.38 | 0.65 | -0.08 | 0.95 |
Brazil | 826 | 366.14 | 63.04 | -1.51 | 0.96 | -0.45 | 1.10 | -0.43 | 0.89 | 0.30 | 1.12 |
Bulgaria | 188 | 379.83 | 74.44 | -0.86 | 0.93 | 0.06 | 0.94 | -0.22 | 0.93 | -0.73 | 0.53 |
Colombia | 340 | 374.80 | 54.64 | -1.56 | 0.91 | 0.17 | 0.90 | -0.62 | 1.01 | 0.70 | 1.33 |
Costa Rica | 189 | 418.11 | 53.94 | -1.11 | 1.03 | 0.18 | 1.06 | -0.35 | 0.79 | -0.07 | 0.85 |
Croatia | 163 | 474.05 | 56.56 | -0.30 | 0.45 | -0.20 | 0.91 | -0.01 | 0.88 | -0.46 | 0.76 |
Hong Kong-SAR | 148 | 539.30 | 64.42 | -0.79 | 0.62 | -0.41 | 0.93 | 1.98 | 0.80 | -0.16 | 0.94 |
Indonesia | 208 | 371.07 | 44.67 | -1.97 | 0.70 | 0.54 | 0.91 | 0.48 | 1.13 | 0.46 | 0.88 |
Jordan | 232 | 393.37 | 51.67 | -0.54 | 0.58 | -0.21 | 1.08 | -1.50 | 0.75 | 0.95 | 1.44 |
Kazakhstan | 217 | 423.85 | 45.24 | -0.41 | 0.42 | 0.43 | 0.91 | -1.00 | 0.87 | 0.29 | 1.17 |
Latvia | 203 | 478.87 | 48.47 | -0.53 | 0.62 | 0.20 | 0.82 | 0.61 | 0.96 | -0.42 | 0.79 |
Liechtenstein | 12 | 489.21 | 56.07 | 0.19 | 0.35 | 0.23 | 0.83 | 0.40 | 0.98 | 0.14 | 0.84 |
Lithuania | 216 | 453.20 | 50.98 | -0.44 | 0.49 | 0.20 | 0.91 | 0.61 | 0.99 | -0.59 | 0.62 |
Macao-China | 45 | 508.69 | 54.81 | -0.78 | 0.55 | -0.48 | 0.90 | 0.66 | 1.12 | 0.10 | 1.25 |
Malaysia | 164 | 411.71 | 43.60 | -0.80 | 0.56 | 0.54 | 0.93 | -1.16 | 1.02 | 0.26 | 0.82 |
Montenegro | 50 | 399.13 | 54.50 | -0.41 | 0.56 | 0.06 | 0.91 | -0.85 | 0.71 | -0.51 | 0.72 |
Perm-Russia | 62 | 455.92 | 50.83 | -0.38 | 0.43 | -0.17 | 0.83 | 0.43 | 0.68 | 0.66 | 1.00 |
Peru | 237 | 347.73 | 61.37 | -1.59 | 0.95 | -0.17 | 0.98 | 0.18 | 0.95 | 0.60 | 1.02 |
Qatar | 155 | 379.88 | 72.43 | 0.38 | 0.54 | 0.60 | 0.86 | -0.05 | 2.04 | -0.06 | 1.18 |
Romania | 178 | 430.40 | 66.22 | -0.67 | 0.86 | -0.19 | 0.87 | -0.27 | 0.90 | -0.45 | 0.71 |
Russia | 222 | 466.56 | 46.05 | -0.35 | 0.44 | -0.12 | 0.85 | -0.22 | 0.88 | 0.32 | 1.08 |
Serbia | 140 | 421.69 | 60.85 | -0.42 | 0.51 | -0.32 | 0.93 | 0.18 | 0.74 | -0.63 | 0.69 |
Shanghai-China | 154 | 574.91 | 53.50 | -0.49 | 0.59 | -0.02 | 0.95 | -0.84 | 1.29 | 0.94 | 1.26 |
Singapore | 164 | 548.25 | 61.70 | -0.24 | 0.49 | 0.10 | 0.94 | -0.25 | 1.10 | 0.03 | 0.84 |
Taiwan | 160 | 507.82 | 67.41 | -0.56 | 0.45 | -0.25 | 0.87 | 0.60 | 1.17 | 0.00 | 1.16 |
Thailand | 239 | 437.02 | 53.55 | -1.94 | 0.66 | -0.11 | 0.99 | 1.68 | 1.40 | 1.44 | 0.99 |
Tunisia | 151 | 366.08 | 50.92 | -1.33 | 0.71 | -0.55 | 1.28 | -1.17 | 0.77 | -0.22 | 0.91 |
UAE | 446 | 431.13 | 67.73 | 0.24 | 0.60 | 0.36 | 0.96 | -0.84 | 1.25 | 0.11 | 1.41 |
Uruguay | 180 | 393.64 | 70.98 | -1.01 | 0.84 | -0.18 | 0.92 | -0.45 | 0.87 | 0.25 | 1.05 |
Vietnam | 162 | 443.56 | 59.46 | -2.40 | 0.70 | -0.24 | 0.82 | -1.56 | 0.50 | 0.29 | 1.13 |
Mean | 202 | 435.86 | 57.20 | -0.78 | 0.64 | -0.02 | 0.93 | -0.14 | 0.97 | 0.11 | 0.98 |
Authors: Dr Jehanzeb R. Cheema (corresponding author) is a research methodologist with a background in education and economics. He teaches research methodology courses at the Merrick School of Business, University of Baltimore. His research interests include study of demographic variation in variables related to student and teacher beliefs. Email: jcheema1@masonlive.gmu.edu Web: http://faculty.education.illinois.edu/jrcheema/ Dr Muhammad Asrar-ul-Haq is an Assistant Professor in the Faculty of Business Administration at COMSATS Institute of Information Technology. Pakistan. Currently, he is heading the Department of Management Sciences of Sahiwal Campus. His research interests include CSR in Higher Education, cultural studies, knowledge sharing and management, and leadership studies. Email: asrar.uiuc@gmail.com Please cite as: Cheema, J. R. & Asrar-ul-Haq, M. (2017). Effects of staff participation, morale, and shortage on organisational performance: An international survey. Issues in Educational Research, 27(4), 677-696. http://www.iier.org.au/iier27/cheema-2.html |