Increasing fairness perceptions of government grant applicants: an investigation of justice theory in small business in post-Katrina New Orleans.

Kwun, Obyung ; Mancuso, Louis C. ; Alijani, Ghasem S. 等

BACKGROUND PERSPECTIVES

Hurricane Katrina further exacerbated the serious economic challenges faced by New Orleans even before Katrina. The flooding, wind, rain, and unfortunate looting and arson associated with the storm destroyed or damaged thousands of businesses. Commerce was seriously interrupted in industries such as entertainment, hospitality and tourism, finance, and transportation. Small businesses and entrepreneurial efforts suffered extensive losses stemming from the damages, and the city's sales tax base plummeted. The labor force declined considerably, particularly in the health and education industries (According to FedStats and FEMA in 2006, the population of Orleans Parish decreased by 60%: even today, the population is still down 36%). Unemployment increased, and the city faced significant population losses due to out-migration, particularly of the African-American community. Use of mainly Hispanic workers from outside the state to support the huge construction business, while the African-American residents in New Orleans remained without jobs, raised labor issues (Entertainment, Tourism and Hospitality, U.S. Chamber of Commerce, November 8, 2005).

The severity of Katrina's destruction has made redevelopment of New Orleans, including promoting investments, small businesses and entrepreneurs, job creation and economic growth, a herculean task. The incredible extent of damage due to the disaster should be a matter of great concern to residents, businesses, policy makers, and politicians for the purpose of acquiring and deploying necessary resources to support a smooth and speedy recovery. In particular, it must be kept in mind that the Hurricane Katrina aftermath produced small business environments that were lacking in planning, susceptible to cash flow reductions, lacking inadequate access to capital for recovery, facing difficulties related to federal government aid, and attempting to operate in a devastated infrastructure, slowing early recovery (Runyun, March, 2006). Also, it is important that government agencies assist affected businesses' attempts to survive and motivate new entrepreneurs to start fresh businesses (Zolin & Kropp, January, 2007). Despite the critical nature of governmental assistance, a previous study showed a high level of dissatisfaction with government aid among New Orleans business owners (Mancuso, June, 2006). This dissatisfaction, in turn, may discourage small business owners from applying for government grants, which can speed up the recovery.

LITERATURE REVIEW

Justice theory has been successful in explaining attitudes and behaviors in such diverse domains as resource allocation, conflict resolution, personnel selection, and layoffs. Justice, as a perception of fairness of the decision process and decision outcomes, has been shown to influence attitudes (e.g., satisfaction) and behavior (e.g., turnover) (Greenberg, 1990).

Researchers have developed conceptual models of justice theory that explain the role of fairness in organizations by identifying factors (e.g., Bies, 1987) that account for different dimensions of justice and their effects on attitudes and behaviors ( Andrews, Baker, & Hunt, 2008; Hershcovi, et.al., 2007; McFarlin & Sweeney, 1992). These dimensions include procedural justice, interactional justice, and distributive justice. Procedural justice refers to the fairness of the formal procedures through which outcomes are achieved (Greenberg, 1990). A number of research studies have demonstrated that procedural justice affects attitudes toward the organization and its operations (Korsgaard, Schweiger, & Spienza, 1995). Interactional justice deals with the interpersonal treatment people receive from the decision maker and the adequacy with which formal decision-making procedures are explained (Bies, 1987). Empirical evidence has shown that perceptions of fairness may also be affected by the interpersonal treatment received from the decision-maker, causing affective and behavioral reactions (Donovan, Drasgow, & Munson, 1998). Distributive justice refers to the perceived fairness of the resulting distribution of decision-making outcomes. The fairness of outcomes is evaluated based on distributive rules that include equity, equality, and needs (Deutsch, 1975).

Based on the preceding discussion of justice theory, this study attempts to examine the impacts of perceived fairness on small business owners' satisfaction with government grants and granting agencies. The following hypotheses were developed for this study, as illustrated in Figure 1:

H1: Procedural Justice has a positive effect on satisfaction with government grants for small businesses.

H2: Procedural Justice has a positive effect on satisfaction with grant agencies for small businesses.

H3: Interactional Justice has a positive effect on satisfaction with government grants for small businesses.

H4: Interactional Justice has a positive effect on satisfaction with grant agencies for small businesses.

H5: Distributive Justice has a positive effect on satisfaction with government grants for small businesses.

H6: Distributive Justice has a positive effect on satisfaction with grant agencies for small businesses.

[FIGURE 1 OMITTED]

RESEARCH METHOD

DATA COLLECTION

For this study, owners/managers of small businesses were targeted for data collection throughout post-Katrina New Orleans. Different agencies and businesses use different criteria to determine whether a business is small, such as the number of employees, annual income earned and relative dominance in their industry. Different ranges of employee size (size standards) for small businesses are encountered in the literature. For the purpose of this study, the number of employees was used as the determining factor for classification as a small business: firms that employed 100 or less individuals were considered as small businesses.

The survey questionnaire used in this study was developed by adapting the items from existing justice literature (e.g., Moorman, 1991). Data was gathered by visiting small businesses and asking the owners/managers to complete the questionnaires.

CHARACTERISTICS OF THE SAMPLE

There were 200 respondents in this study (see Table 1). The respondents were evenly distributed by gender. The majority of the respondents reported service and merchandising (63.5% and 29%, respectively) business types and most respondents (98.5%) were from businesses with less than 50 employees. More than 70% of the respondents reported their knowledge level of government grant processes to be average or above. Although 84% responded that government grants would help their businesses, only 60% of the respondents have applied for a government grant at least once. Of those respondents, 36% reported having received a government grant.

DATA ANALYSIS

Partial Least Squares (PLS) analysis was used to test the proposed research model. PLS recognizes two parts of model testing: a measurement model and a structural model (Barclay et al., 1995; Fornell & Larcker, 1981). In order to test a research model, the measurement model first has to be evaluated, and then the structural model has to be tested. The assessment of both models was conducted using SmartPLS 2.0.

The measurement model addresses the relationship between the constructs and the items used to measure them. The test of the measurement model consists of the estimation of the convergent and discriminant validities of the measurement instrument. Convergent validity refers to the extent to which measures of a construct are related to each other. Discriminant validity is the degree to which measures of a construct are not related to measures of other constructs. However, reflective and formative measures should be treated differently (Hulland, 1999). Formative items are considered to form or cause the construct to be measured. Thus, these items are not expected to correlate or show internal consistency, unlike items for reflective constructs (Chin, 1998). For this reason, the item weights for formative measures have been used to test the relevance of the items to the constructs (Barclay et al., 1995; Wixom and Watson, 2001). On the other hand, the item loadings for reflective measures are used to test the validity of the items for the constructs. Table 2 shows the relationship between the constructs and the items in this study.

RESULTS

MEASUREMENT MODEL

Although formative and reflective constructs are treated differently, the loadings are used for interpretive purposes and for the calculation of reliabilities. Although it has been suggested that an absolute value of factor loadings of 0.30 is considered to meet the minimal level, loadings of 0.40 are considered more significant, and loadings of 0.50 or greater are considered very significant (Hair et. al., 1998). Average variance extracted (AVE) of 0.50 or above has also been used to support the convergent validity of the constructs (Fornell & Larcker, 1981).

Table 3 shows individual item loadings and associated weights for the related construct. All of the Cronbach's alphas exceed the 0.70 minimum level suggested by Nunnally (1978). For the reflective constructs (Satisfaction with Grant and Satisfaction with Grant Agent), all of the loadings are 0.89 or above, which is considered very strong. Cronbach's alphas for all constructs are 0.88 or above, which indicates strong reliabilities for the items in measuring their constructs. Also, the AVEs for all constructs are well above the acceptance level of 0.50 (see Table 4). Based on these results, the convergent validity for the measurement items can be considered acceptable.

Discriminant validity is adequate when the average variance extracted from the construct is greater than the variance shared between the construct and other constructs. Table 5 shows correlations between constructs and square root of AVEs (bold faced) for the reflective constructs. The square root of AVE for SG is greater than the correlations with other constructs. Similarly, the square root of AVE for SA is greater than the correlations with other constructs. Also, the cross loadings in Table 6 show that items for SG and SA are loaded higher on their constructs than on other constructs. This also indicates some evidence for discriminant validity.

For the formative constructs, some of the items show negative weights. Formative items are considered to form or contribute to the construct. The negative weights indicate a contradiction to the original expectation suggested by justice theory literature. The results show two items with negative weights (PJ1 and IJ4).

STRUCTURAL MODEL

In order to improve the validity of the results, the items with negative weights were removed when the structural model was tested. As a result, PJ1 and IJ4 were dropped to estimate the structural model. Figure 2 shows the significance and the strength of the relationships between the constructs and [R.sup.2], which indicates the explanatory power of the model. Procedural justice is not a significant factor, as shown by path coefficients of -0.03 and -0.05 for satisfaction with grant and satisfaction with grant agency respectively. Interactional justice shows the highest path coefficients on both dependent variables, with values of 0.58 and 0.69. And distributive justice shows somewhat weak but significant impacts on both dependent variables, with path coefficients of 0.31 on satisfaction with grant and 0.24, on satisfaction with grant agency. Sixty-seven percent of the variance of satisfaction with grant and 75% of the variance of satisfaction with grant agency was explained by the proposed model. Table 7 summarizes the results of the hypotheses tests in this study.

[FIGURE 2 OMITTED]

CONCLUSIONS

This study investigated the effects of fairness perception on small business owners' satisfaction with government grants and grant agencies. The results show that the main issue in applicant satisfaction is not the procedure required to win the grant: rather, the results suggest that both interpersonal treatment and the way grants are awarded are instrumental in increasing the level of applicant satisfaction. In other words, it is more about how the small business owners are treated by the granting agency during the grant application process than about procedural issues of applying for the grants that improve small business owners' satisfaction. These findings suggest that the grant agents should properly treat the business owners with trustfulness, kindness, justification, respect, etc. in order to achieve higher satisfaction levels for the applicants. This conclusion can be used to improve government grant process outcomes when another natural disaster strikes the United States. While government representatives should be trained in all aspects of the aid to be given, they should also be trained to show kindness, respect, trust, and justification for their actions to grant applicants from the small business sector. It may be concluded that proper interpersonal treatment becomes especially important if granting agencies want to establish a long-term relationship with small business owners and stimulate the economy through government grants.

As with most studies in the justice literature, these results should be interpreted with some caution. For example, items used to measure each of the dimensions of justice may differ, depending on the context. The questionnaire used for this study was based on previous studies where measurement items were validated in different contexts. Thus, the questionnaire can be refined for subsequent studies to improve the validity of the results in government grants for the small business context. Also, the respondents for the study are from New Orleans metropolitan area only, which can be characterized by the unique situation created by the natural disaster and the subsequent economic recovery efforts.

Appendix A: List of Items

Construct       Item   Description

Distributive    DJ1    Grant was allocated fairly based on
                       small business owner's time and
                       effort spent during the grant
                       application process.

Justice         DJ2    Grant was allocated fairly based on
                       small business owner's need.

                DJ3    Grant was are allocated fairly to
                       all small business owners
                       regardless of their effort and
                       need.

Interactional   IJ1    The granting agent considered your
                       view point.

Justice         IJ2    The granting agent was able to
                       avoid any personal bias.

                IJ3    The granting agent provided you
                       with timely feedback about the
                       decision and its implications.

                IJ4    The granting agent treated you with
                       kindness and consideration.

                IJ5    The granting agent showed concern
                       for your rights as a small business
                       owner.

                IJ6    The granting agent took steps to
                       deal with you as a small business
                       owner in a truthful manner.

Procedural      PJ1    The process for grant award is
                       designed to collect accurate
                       information necessary for making
                       decisions.

Justice         PJ2    The process for grant award is
                       designed to provide opportunities
                       to appeal or challenge the decision
                       made.

                PJ3    The process for grant award promote
                       standards so that decisions can be
                       made with consistency.

                PJ4    The process for grant award is
                       designed to hear the concerns of
                       all those affected by the decision.

                PJ5    The process for grant award is
                       designed to provide useful feedback
                       regarding the decision and its
                       implementation.

                PJ6    The process for award is designed
                       to allow for requests for
                       clarification or additional
                       information about the decision.

Satisfaction    SA1    How would you rate the grant
                       agent's knowledge about small
                       businesses?

With Agency     SA2    How would you rate the grant
                       agent's understanding of small
                       business needs?

                SA3    How would you rate the grant
                       agent's communication and
                       interpersonal skills?

                SA4    How would you rate the quality of
                       supporting service from the grant
                       agent?

                SA5    How would you rate the attitude of
                       the grant agent?

Satisfaction    SG1    How would you rate the grant
                       amount?

with Grant      SG2    How would rate the timeliness of
                       the grant?

REFERENCES

Andrews, M.C., Baker, T.L., & Hunt, T.G. (2008). The Interactive Effects of Centralization on the Relationship Between Justice and Satisfaction, Journal of Leadership & Organizational, 15(2), 135-144.

Barclay, D., Higgins, C., & Thompson, R. (1995). The partial Least Squares (PLS) Approach to Causal Modeling, Personal Computer Adoption and Use as an Illustration, Technology Studies, 2(2), 285-309.

Bies, R.J., (1987). The Predicament of Injustice: The Management of Moral Outrage, In L.L. Cummings and B.M. Staw (Eds), Research in Organizational behavior, Volume 9, 289-319, Greenwich, CT, JAI Press.

Chin, W.W. (1998). The Partial Least Squares Approach to Structural Equation Modeling, in G.A. Marcoulides (ed.), Modern Methods for Business Research, Lawrence Erlbaum Associates, Mahwah, NJ, 295-336.

Deutsch, M. (1975). Equity, Equality, and need: What Determines Which Value Will Be Used as the Basis of Distributive Justice? Journal of Social Issues, 31(3), 137-149.

Donovan, M.A., Drasgow, F., & Munson, L.J. (1998). The Perceptions of Fair Interpersonal Treatment Scale: Development and Validation of a Measure of Interpersonal Treatment in the Workplace, Journal of Applied Psychology, 83(5), 683-692.

Fornell, C., & Larcker, D.F. (1981). Evaluating Structural Equation Model with Unobservable Variables and Measurement Error, Journal of Marketing Research, 18(1) 39-50.

Greenberg, J. (199). Organizational Justice: "Yesterday, Today, and Tomorrow", Journal of Management, 16(2), 399-432.

Hair, J.F., Tatham, R.L., & Black, W. (1998). Multivariate Data Analysis, Prentice-Hall, New Jersey.

Hershcovis, M., Nick Turner, N., Barling, J., Arnold, K., & Dupre, K., Inness, M., LeBlanc, M., & Sivanathan, N. (2007). Predicting Workplace Aggression: A Meta-Analysis, Journal of Applied Psychology, 92(1), 228-23.

Hulland, J., (1999). Use of Partial Least Squares (PLS) in Strategic Management Research: A Review of Four Recent Studies, Strategic Management Journal, 20(2), 195-204.

Korsgaard, M.A., Schweiger, D.M., & Sapienza, H.J. (1995). Building Commitment, Attachment, and Trust in Strategic Decision-making Team: The Role of Procedural Justice, Academy of Management Journal, 38(1), 60-84.

McFarlin, D.B. & Sweeney, P.D. (1992). Distributive and Procedural Justice as Predictors of Satisfaction with Personal and Organizational Outcomes, Academy of Management Journal, 35(3), 626-637.

Moorman, R.H. (1991). Relationship between Organizational Justice and Organizational Citizenship Behaviors: Do Fairness Perceptions Influence Employee Citizenship? Journal of Applied Psychology, 76(6), 845-855.

Nunnally, J. (1978). Psychometric Theory, 2nd ed., McGraw-Hill, N.Y.

Jeffre W. Kassing. (2008). Disagreeing about what's Fair: Exploring the Relationship between Perceptions of Justice and Employee Dissent, Communication research reports, 34-43.

Wixom, B.H., & Watson, H.J. (2001). An Empirical Investigation of the Factors Affecting Data Warehousing Success, MIS Quarterly, 25(1), 17-41.

Obyung Kwun, Southern University at New Orleans

Louis C. Mancuso, Southern University at New Orleans

Ghasem S. Alijani, Southern University at New Orleans

David W. Nickels, University of North Alabama

Table 1: Characteristics of the Sample

Sample Characteristics       N=200     %

Gender

Male                          100    50.0
Female                         97    48.5
Not responding                  3     1.5

Familiarity with Grants

Very High                       5     2.5
High                           55    27.5
Average                        83    41.5
Low                            30    15.0
Very Low                       10     5.0
Not responding                 17     8.5

Type of Business

Manufacturing                   9     4.5
Service                       127    63.5
Merchandising                  58    29.0
Other                           0     0
Not responding                  6     3.0

Number of Employees

Less than 5                    43    21.5
5-10                           55    27.5
11-50                          93    46.5
More than 50                    3     1.5
Not responding                  6     3.0

Grant would help business

Strongly Agree                113    56.5
Agree                          55    27.5
Neutral                        21    10.5
Disagree                        6     3.0
Strongly Disagree               3     1.5
Not responding                  2     1.0

Have Applied for Grant

Yes                           120    60.0
No                             80    40.0

Have Received Grant

Yes                            72    36.0
No                            128    64.0

Table 2. Measurement Model

Constructs                           Relationship

Procedural Justice (PJ)              Formative
Interactional Justice (IJ)           Formative
Distributive Justice (DJ)            Formative
Satisfaction with Grant (SG)         Reflective
Satisfaction with Grant Agent (SA)   Reflective

Table 3. Weights and Loadings

Variables          Weights Loadings

Distributive       Cronbach's Alpha = 0.94
Justice (DJ)

DJ1                   0.40          0.94
DJ2                   0.09          0.90
DJ3                   0.56          0.97

Interactional      Cronbach's Alpha = 0.88
Justice (IJ)

IJ1                   0.19          0.78
IJ2                   0.06          0.77
IJ3                   0.11          0.79
IJ4                  -0.03          0.31
IJ5                   0.48          0.95
IJ6                   0.29          0.93

Procedural         Cronbach's Alpha = 0.90
Justice (PJ)

PJ1                  -0.09          0.67
PJ2                   0.69          0.96
PJ3                   0.25          0.87
PJ4                   0.08          0.74
PJ5                   0.08          0.70
PJ6                   0.11          0.60

Satisfaction       Cronbach's Alpha = 0.95
with Agent (SA)

SA1                   0.22          0.89
SA2                   0.22          0.92
SA3                   0.21          0.93
SA4                   0.23          0.91
SA5                   0.21          0.92

Satisfaction       Cronbach's Alpha = 0.95
with Grant (SG)

SG1                   0.52          0.98
SG2                   0.51          0.98

Table 4. Average Variance Extracted

                     DJ     IJ     PJ     SA     SG

Average Variance    0.87   0.61   0.59   0.84   0.95
Extracted

Table 5. Correlations and Square Root of AVEs

        DJ     IJ     PJ     SA     SG

SA     0.75   0.85   0.52   0.92
SG     0.73   0.80   0.51   0.85   0.98

Table 6. Cross Loadings

        DJ     IJ     PJ     SA     SG

DJ1    0.94   0.73   0.51   0.72   0.66
DJ2    0.90   0.72   0.56   0.65   0.68
DJ3    0.97   0.72   0.56   0.71   0.72
IJ1    0.73   0.78   0.56   0.65   0.63
IJ2    0.70   0.77   0.46   0.68   0.58
IJ3    0.65   0.79   0.61   0.63   0.67
IJ4    0.40   0.31   0.25   0.35   0.16
IJ5    0.66   0.95   0.53   0.82   0.74
IJ6    0.71   0.93   0.58   0.79   0.74
PJ1    0.42   0.51   0.67   0.39   0.30
PJ2    0.50   0.58   0.96   0.50   0.49
PJ3    0.51   0.57   0.87   0.46   0.44
PJ4    0.52   0.54   0.74   0.39   0.37
PJ5    0.46   0.39   0.70   0.41   0.32
PJ6    0.47   0.40   0.60   0.31   0.30
SA1    0.65   0.78   0.48   0.89   0.73
SA2    0.69   0.77   0.54   0.92   0.76
SA3    0.67   0.78   0.44   0.93   0.79
SA4    0.79   0.76   0.52   0.91   0.82
SA5    0.60   0.79   0.42   0.92   0.79
SG1    0.75   0.77   0.50   0.84   0.98
SG2    0.68   0.79   0.50   0.82   0.98

Table 7. Hypotheses Tests

Hypotheses                            t-Statistic     Results

H1: Procedural Justice has a             0.32       Not Supported
positive effect on satisfaction
with government grants for small
businesses.

H2: Procedural Justice has a             0.43       Not Supported
positive effect on satisfaction
with grant agencies for small
business.

H3: Interactional Justice has a          4.39       Supported
positive effect on satisfaction
with government grants for small
businesses.

H4: Interactional Justice has a          4.99       Supported
positive effect on satisfaction
with grant agencies for small
businesses.

H5: Distributive Justice has a           2.46       Supported
positive effect on satisfaction
with government grants for small
businesses.

H6: Distributive Justice has a           2.01       Supported
positive effect on satisfaction
with grant agencies for small
businesses.