Resource-based view or slack availability of resources: a perception survey of Japanese automotive & electronic companies.

Cortez, Michael Angelo A. ; Nugroho, Katarina Marsha Utama

INTRODUCTION

The resource-based view perspective has been widely espoused as the theoretical foundation of sustainability practices. Through the investments in inimitable internal capabilities like environmental performance that translate to measurable benefits, the first direction of construct relationship is established: environmental innovations impact financial performance.

On the other hand, the slack availability of resources explains that corporate social performance, particularly environmental innovations, could possibly be a result of the availability of financial resources. Without these, it would be difficult for companies to comply with regulations and satisfy other stakeholders' claims to social and environmental issues. Hence, the second direction of relationships is: financial performance in preceding years impact environmental innovations.

While virtuous cycles are observed between these constructs, we attempt to determine the motivation for Japanese automotive and electronics companies, based on their perception for engaging in environmental innovations. Do they see environmental innovations as leading to improved financial performance? Or is it the other way around? Do they perceive enhanced financial performance as facilitating environmental innovations? A number of empirical studies have validated these directions of the relationships. We attempt to capture the perception of Japanese management on CSR, particularly environmental accounting and reporting practices, in order to determine the predominant mindset.

CSR in this context refers to the requirement set by the MOE for companies to adapt more sustainable business practices. This opens to another construct which is Corporate Social Performance (CSP) being the operationalization of CSR: avenues through which the objectives set by CSR are attained. Thus, the concept of environmental accounting, particularly the variable environmental cost, measures the degree of CSP that companies undertake to comply with the sustainability objective of CSR.

A Likert scale was executed by sending paper forms and online survey links to CSR reporting divisions of automotive and electronics companies listed on the Tokyo Stock Exchange. Four out of ten automotive companies and 13 out of 30 electronics companies participated in the survey. To support the descriptive statistics, the following non-parametric analysis are performed: central tendency, dispersion, concentration, peaked-ness and histogram analysis. Shapiro-Wilk test and Runs tests are performed to show normal distribution and randomness (See Annex 1).

THEORETICAL REVIEW

The Resource-Based View

Wernerfelt (1984) was the first to invite business leaders and scholars to look at companies from the perspective of resources, rather than the products in order to form management strategies. Prahalad and Hamel (1990) emphasize the importance of making the most of the core competencies as the sustainable competitive advantage of the companies, rather than merely paying attention to the products and markets while planning business strategies.

Hart (1995) built on the resource-based theory and introduced the concept of a natural resource-based view, which refers to the theory of competitive advantage based upon the relationship of a firm to natural environment. His study suggested the implementation of environmental strategies to utilize the resources for environmental performance, such as pollution prevention, product stewardship, and sustainable development. Through the allocation of resources to improve environmental performance, companies would be able to expect improvement in financial performance.

Russo and Fouts (1997) present the association between high levels of environmental performance and enhanced profitability among 243 firms over two years. The results show that environmental performance and firm financial performance are positively linked. Therefore, the available literature provided the insight that firms might be able to adopt resource-based views, in order to allocate their corporate resources to productive use, to improve their environmental performance and develop close relationships with multiple stakeholders, and eventually enhance their financial performance.

Another strength of the resource-based view theory is that it links corporate responsibility performance as a stimulus to the development of intangibles (innovation, human capital, reputation, and culture) (Surroca, Tribo, and Waddock, 2010), which result in the improvement of corporate financial performance. The firms' intangible resources act as the competitive advantage that increases the competitiveness in the market and are difficult for the competitors to match (Barney, 1991).

Slack Availability of Resources

This theory suggests the notion that better corporate financial performance will lead to more available resources to be allocated and invested into corporate responsibility activities. This theory is the underlying concept that explains the positive impact of a firm's financial performance on CSR and the environmental performance of a company in the following years. Companies with better profits have more capital to invest in R&D (Helfat, 1997), and more opportunities to allocate resources to generating human capital (Wright, Gardner, Moynihan, Allen, 2005), and reputation (Roberts and Dowling, 2002). In short, better corporate financial performances would allow a company to develop new intangibles that become the sources of competitive advantages (Sharma and Vredenburg, 1998).

Over the years, there has been continuous exploration of the relationship between CSR performances and financial performances and the reasons why they are related. Virtuous circles or two-way simultaneous causal relationships between the CSR performance and firm financial performance, with intangibles mediating the indirect relationships in both directions, have also been observed (Surroca, Tribo, and Waddock, 2010).

The Possibility of the Convergence of the Two Theories

There are ongoing debates on whether both theories are really different and opposite from each other. Nevertheless, in a business management review by Smith (2003), he explained one assertion saying that the two theories converge. If one looks deeper into both theories, they do not exactly express totally opposite claims. In fact, they may be stating similar things in different ways. Perhaps they are related and, by any chance, equivalent. An assertion of this argument stated that stakeholder interests are being considered only as a means to the end of profitability, and that managers are using stakeholders to effect the results dictated by the shareholder theory (Smith, 2003). However, to be able to claim that these two theories are stating the same thing in different ways, one has to prove that satisfying other non-investor parties will lead to the satisfaction of investors in the end. If the theories converge, it means that there is a causal relationship between investing in activities to satisfy non-shareholders and the improvement in the firm's financial profitability, which means that investing in CSR and environmental activities will increase the shareholder's value in the long term.

There were further studies supporting the notion that the two theories may converge. The meta-analysis by Orlitzky (2003) confirmed the instrumental stakeholder theory as it proved that there is a positive impact of corporate social and environmental performance on a firm's financial performance. The results of his research and analysis rejected the notion developed by the neo-classical economists that the investments in CSR and environmental performance will disrupt a company's achieving its purpose of maximizing its shareholders' wealth (Friedman, 1962). This study suggested that the inclusion of social and environmental performance into business activities would increase organizational effectiveness, affirming the validity of "enlightened self-interest" in the area of CSR. He also noted that shareholders are legitimate stakeholders. This strengthened the theory that by addressing and balancing the claims of various stakeholders, corporate managers would be able to improve the efficiency of their company's adaptation to the changing external demands (Freeman & Evan, 1990). This theory has also been mentioned as the "good management theory" (Waddock & Graves 1997).

Earlier Empirical Study and Research Gap

Cortez (2010) earlier differentiated the Japanese automotive and electronics companies, using panel data regression analysis. He cites that automotive companies have virtuous cycles between the constructs' environmental innovation and measures of financial performance. He observes that there are no virtuous cycles for electronics companies but there are significant relationships between environmental innovations and revenue generation, and environmental innovations and risk reduction.

We continue on to explore the same construct relationships and validate the findings of Cortez (2010) with our management perception survey. This effectively completes the triangulation process by providing primary data-gathering evidence to his empirical analysis of archival data.

HYPOTHESES

We hypothesize for the first set of constructs between environmental innovations and revenues, using the RBV perspective and the slack availability of resources.

H1a: Japanese automotive and electronics companies perceive that investing in CSR activities and environmental innovations will lead to enhanced revenues.

H1b: Japanese automotive and electronics companies perceive that enhanced revenue will facilitate more CSR and environmental innovations.

We then hypothesize that environmental innovations affect profitability and vice versa.

H2a: Japanese automotive and electronics companies perceive that investing in CSR activities and environmental innovations will lead to improved profitability.

H2b: Japanese automotive and electronics companies perceive that enhanced profitability will facilitate more CSR and environmental innovations.

Environmental innovations involve investments in environmental assets. Hence, we hypothesize for the third set of constructs.

H3a: Japanese automotive and electronics companies perceive that investing in CSR activities and environmental innovations will lead to an increase in firm size / assets.

H3b: Japanese automotive and electronics companies perceive that enhanced firm size / assets will facilitate more CSR and environmental innovations.

Risk minimization has been espoused in the business rationale for sustainability. With accounting risk measures in terms of long-term debt, we hypothesize for the fourth set of constructs.

H4a: Japanese automotive and electronics companies perceive that investing in CSR activities and environmental innovations will lead to decreased accounting risk / long-term debt.

H4b: Japanese automotive and electronics companies perceive that decreased accounting risk/long-term debt will facilitate more CSR and environmental innovations.

Finally, hypothesize if environmental innovations enhance shareholder value and vice-versa with the fifth set of constructs.

H5a: Japanese automotive and electronics companies perceive that investing in CSR activities and environmental innovations will lead to improved shareholder value / equity.

H5b: Japanese automotive and electronics companies perceive that improved shareholder value/equity will facilitate more CSR and environmental innovations.

RESULTS AND DISCUSSIONS

Environmental Innovations Impact Enhanced Revenues

Automotive companies unanimously agree that environmental innovations have a positive impact on enhanced sales (See Histogram analysis in Appendix 1). The CSR reporting divisions perceive that their investments in clean technology and pollution prevention are appreciated by their stakeholders, thus translating to improved revenues. This result is consistent with the business rationale for sustainability and empirical findings of Cortez (2010).

Less than half the electronics companies perceive the relationship of constructs the way automotive companies do. In fact, most of them had neutral replies. This is perhaps attributable to the industry's economic condition although empirically, Cortez (2010) shows that there is a positive impact.

Therefore, we accept H1a, and conclude that the management perception of automotive companies is consistent with empirical evidence. However, the management of electronics companies may not agree with this.

Enhanced Revenues Impact Environmental Innovations

The results seem to mirror the first directions of construct relationships for automotive companies. With improved sales as a result of environmental innovations, management belief or positive outcome is reinforced. Therefore, further investments in environmental innovations are facilitated. In this connection, we accept H1b and conclude that this somehow suggests a virtuous cycle in reaffirming management decisions to invest in environmental innovations. Electronics companies are more optimistic from this perspective. Majority agreed that enhanced revenues would affect their environmental innovations, thus suggesting the perspective of slack availability of resources. We also accept H1b for electronics companies.

Environmental Innovations Positively Impact Profits

Automotive and electronics have varied perceptions. Presumably because of their industrial circumstances in the light of the recent global economic crisis, automotive companies perceived the link of environmental innovations to profitability. The sale of fuel-efficient automobiles and the development of new technologies like hybrid and electric cars that reduce C[O.sub.2] emissions have brought profitability to automotive companies.

On the other hand, 75 percent of electronic companies were optimistic that their profitability is positively affected by environmental innovations. Empirical results of Cortez (2010) reveal that there is no significant relationship between these constructs for electronics companies, considering their accumulated losses in the recent decade.

Therefore, we accept H2a for both automotive electronics companies. These results could suggest that the financial position of electronics companies could have been worse had they not invested in environmental innovations.

Enhanced Profits Impact Environmental Innovations

The majority of automotive companies agreed that enhanced profits lead to more investments in environmental innovations. The electronics companies had the same perception. In reality, however, their financial conditions differ. These findings simply point again to the slack availability of resources as a motivation in undertaking environmental innovations. Therefore, we accept H2b.

Environmental Innovations Impact Firm Size / Assets

Only half the automotive companies agreed that environmental innovations affect their firm size. For over a decade now, these companies must have invested heavily in environmental innovations and other intangible assets. According to Mordhart (2009), they must have attained a certain threshold of firm size that corporate social performance becomes independent of. Nevertheless, Cortez (2010) cites the positive impact of environmental innovations on firm size and vice versa.

A little more than half the electronics companies perceive the positive impact of environmental innovations on firm size. Consistent with the findings of Cortez (2010), their perception validates the empirical findings. This is presumably due to the accumulation of losses, and the treatment of the majority of environmental innovations costs as expenses rather than capitalizing into assets. This also depends on whether there is any future benefit of the environmental investments.

We do not find sufficient basis for perception of these Japanese companies for accepting H3A as to the impact of environmental innovations on firm size.

Enhanced Firm Size Positively Impacts Environmental Innovations

The perceptions of automotive companies are similar to the first direction, reaffirming the concept that they have already attained a certain threshold of firm size. Hence, they would not invest more on environmental innovations. In fact, it is observed that their environmental innovations costs have been decreasing over the recent years (Cortez 2010).

Interestingly, electronics companies perceive that enhanced firm size would impact environmental innovations. This shows their willingness to invest if they had the resources. Empirically, they are not yet equipped as revealed by Cortez (2010).

Therefore, we reject H3b for automotive companies, and accept H3b for electronics companies, on the basis of the concept of the threshold for firm size.

Environmental Innovations Impact Risk Minimization / Long-term Debt

Automotive companies do not perceive environmental innovations as factors that reduce their accounting risk as measured in long-term debt. This is presumably because automotive companies are exposed to different contingencies like quality and safety, and environmental concerns have been addressed in compliance with government guidelines. Cortez (2010) points out that the relationship of variables is not negative; rather it is positive, suggesting that automotive companies engage in long-term debt financing to afford environmental innovations. H4a is rejected, therefore, for automotive companies.

Electronics companies reveal more notable perceptions. They all agree that environmental innovations reduce their accounting risk measured in terms of long-term debt. Cortez (2010) shows descriptive statistics of how long-term debt ratio has decreased in the past decade. Likewise, the negative relationship is established empirically and consistently with the business rationale for sustainability. H4a is accepted, therefore, for electronics companies.

Reduced Accounting Risks / Long-term Debt Impacts Environmental Innovations

Automotive companies do not similarly agree to the earlier relationship established above and consistent with empirical findings. Electronics companies mostly agree, but are not unanimous, as in the first directions of construct relationships. This suggests that their perception is short-term unidirectional and that once accounting risk is reduced, some would not reinforce environmental innovations. H5a is rejected, therefore, for automotive companies and accepted for electronics companies.

Environmental Innovations Impact Shareholder Wealth

Automotive companies unanimously agree that environmental innovations enhance their shareholder wealth and this is consistent with their perception of the impact on profitability. Cortez (2010) has empirical evidence on the satisfaction of stakeholder concerns.

Majority of electronics companies agree with this perception, however, this is not empirically founded which may be interpreted as a means of satisfying stakeholder concerns even if it means negative financial results. Therefore, H5a is accepted for both automotive and electronics companies

Enhanced Shareholder Wealth Impacts Environmental Innovations

The responses of automotive companies mirror the earlier direction of construct relationships, thus suggesting a virtuous cycle. However, the responses of electronics companies agreeing to this perception have decreased. Finally, H5b is accepted for both automotive and electronics companies.

CONCLUSION & RECOMMENDATIONS

This perception survey aims to capture management motivation in engaging in environmental innovations considering the bi-directional relationship of the constructs. Given the impact of environmental innovations on measures of financial performance (measured in revenues, profits, firm size, reduced accounting risk and shareholder wealth), we reveal that the management of the Japanese automotive and electronics companies are partial to the perspective of slack availability of resources.

The automotive companies may appreciate the resource-based view but only as far as sales, profit and shareholder wealth maximization are concerned. They presumably have attained a certain threshold for firm size so that their asset sizes are no longer significantly related to their environmental innovations. In addition, they finance their environmental innovations through long-term debt which runs contrary to expectations that accounting risks are ideally minimized. Automotive companies appear to have more perception responses that are supportive of the slack availability of resources.

Electronics companies have not yet recovered economically from their turn of the century financial performance levels which have been worsened by the recent global crisis. This is felt in their current performance and evident in their responses to the perception survey. There is no clear support for the first direction of construct relationships using the resource-based view perspective. However, they perceive increased sales, improved profitability, enhanced asset size, and maximized shareholder wealth as facilitating factors for investments in environmental innovations. Hence, the slack availability of resources is their predominant perspective.

Scholars advocate that there could be a virtuous cycle or mutually reinforcing variables, and hence, the combination of the two theoretical perspectives. However, notwithstanding an empirical basis, the cycle appears to be broken as perceived by management of Japanese automotive and electronics companies.

ANNEX 1. NON-PARAMETRIC STATISTICAL ANALYSIS

Basic Statistical Moments (Central Tendency, Dispersion, Concentration, Peaked-ness) and Histogram Analysis

Automotive Summary Statistics, using the observations 1-4

Variable    Mean     Median    Minimum   Maximum   Std. Dev.     C.V.

h1a        4.50000   4.50000   4.00000   5.00000   0.577350    0.128300
h2a        4.25000   4.50000   3.00000   5.00000   0.957427    0.225277
h3a        3.75000   3.50000   3.00000   5.00000   0.957427    0.255314
h4a        4.75000   5.00000   4.00000   5.00000   0.500000    0.105263
h5a        4.25000   4.00000   4.00000   5.00000   0.500000    0.117647
h1b        4.00000   4.00000   3.00000   5.00000   0.816497    0.204124
h2b        4.00000   4.00000   3.00000   5.00000   0.816497    0.204124
h3b        3.75000   3.50000   3.00000   5.00000   0.957427    0.255314
h4b        3.50000   3.00000   3.00000   5.00000   1.00000     0.285714
h5b        4.50000   4.50000   4.00000   5.00000   0.577350    0.128300

Variable   Skewness    Ex. kurtosis

h1a        0.000000    -2.00000
h2a        -0.493382   -1.37190
h3a        0.493382    -1.37190
h4a        -1.15470    -0.666667
h5a        1.15470     -0.666667
h1b        0.000000    -1.00000
h2b        0.000000    -1.00000
h3b        0.493382    -1.37190
h4b        1.15470     -0.666667
h5b        0.000000    -2.00000

* These statistics were based on the authors' calculations using
gretl1.9.4

As can be seen from the calculated means for all hypothesis which are all above 3.75 (approaching agreement), the four automotive companies agree not only with the single direction of relationships, but also see the bi-directionality of the constructs. Though noticeably, responses to the RBV direction seem to be a lot stronger relative to that of the Slack Availability of Resources. With regard to the dispersion of the responses, they appear to be mildly dispersed (considering, of course, that this is a Likert-scale type survey) around their means, indicating that the responses of the companies do not vary as much and that answers range from neutral to strong agreement (3 to 5). With regards to the concentration of the responses to the hypotheses, the skewness statistic indicates that most distributions are right-tailed, except for h2a and h4a who are left-tailed. The right tail indicates a greater degree of concentration on relatively lower values, whereas a left tail indicates a greater degree of concentration on relatively higher values, but these of course are concentrations around the mean, so in absolute terms, there are really no lower values that go below 3 (or 4 for other hypotheses). The kurtosis statistic post negative values for all hypotheses, indicating that the frequency of responses is relatively flat about the mean. These properties may be graphically verified as well in the histogram analyses as these are graphical representations of the frequency distributions of the responses for each hypothesis.

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Electronics Summary Statistics, using the observations 1-13

Variable    Mean     Median    Minimum   Maximum   Std. Dev.     C.V.

h1a        3.46154   3.00000   3.00000   4.00000   0.518875    0.149897
h2a        3.38462   3.00000   2.00000   4.00000   0.650444    0.192177
h3a        3.61538   4.00000   3.00000   5.00000   0.650444    0.179910
h4a        4.61538   5.00000   4.00000   5.00000   0.506370    0.109713
h5a        4.07692   4.00000   3.00000   5.00000   0.493548    0.121059
h1b        3.76923   4.00000   3.00000   4.00000   0.438529    0.116344
h2b        3.92308   4.00000   3.00000   5.00000   0.640513    0.163268
h3b        3.61538   4.00000   3.00000   4.00000   0.506370    0.140060
h4b        3.61538   4.00000   3.00000   4.00000   0.506370    0.140060
h5b        3.53846   4.00000   3.00000   4.00000   0.518875    0.146638

Variable   Skewness    Ex. kurtosis

h1a        0.154303    -1.97619
h2a        -0.503556   -0.646006
h3a        0.503556    -0.646006
h4a        -0.474342   -1.77500
h5a        0.230525    1.25623
h1b        -1.27802    -0.366667
h2b        0.0468750   -0.388672
h3b        -0.474342   -1.77500
h4b        -0.474342   -1.77500
h5b        -0.154303   -1.97619

* These statistics were based on the authors' calculations using
gretl1.9.4

The means for the different hypotheses in the electronics industry are relatively less than that in the automotive industry. As can be seen in the calculated means, companies approach agreement to strong agreement (approach 4 to 4.5 or above) for most hypotheses, although h1a and h2a approach neutrality (approach 3) more. H2a however has a minimum value of 2, indicating that there are companies that disagree with RBV that increased investment in CSR activities and environmental innovations increase profitability. Other than this, many companies decide to remain neutral for most hypotheses. In terms of dispersion, it is rather apparent that there are relatively small deviations from the mean, indicating that there is very little variance in the answers of the electronics companies. In terms of concentration, most distributions are left-tailed, indicating that most answers lie on the relatively higher values from the mean. Once again, these do not indicate that there are absolutely low values, but rather low values relative to the mean. For example, in h1a the number of frequencies that answered 3 is greater relative to those who answered 4, thus the right-tail indicating that there is concentration in lesser values. These may be further verified in the histogram analysis. With regards to kurtosis, all distributions for the hypotheses except h5a are negative, indicating a relatively flat distribution around the mean. Although h5a has a positive value for its kurtosis, it is still flatly distributed as it is less than 3.

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Shapiro-Wilk Test

The Shapiro-Wilk Test, or the Shapiro-Wilk Test for Normal Data, is a test to check whether or not the samples for each variable are normally distributed, a property that is desirable for statistical inference. The hypothesis is stated as follows:

[H.sub.0]: The data follows a normal distribution

[H.sub.1]: The data does not follow a normal distribution

Shapiro-Wilk W test for normal data for Automotive Industry (prepared
using Stata11.0)

Variable |   Obs      W        V       z      Prob>z
           +
h1a |        4     0.99978   0.002   -3.261   0.99944
h2a |        4     0.95096   0.566   -0.589   0.72209
h3a |        4     0.96093   0.451   -0.788   0.78479
h4a |        4     0.99977   0.003   -3.241   0.99940
h5a |        4     0.83824   1.865   0.877    0.19033
h1b |        4     1.00000     .     10.000   0.00000
h2b |        4     1.00000     .     10.000   0.00000
h3b |        4     0.96093   0.451   -0.788   0.78479
h4b |        4     0.83824   1.865   0.877    0.19033
h5b |        4     0.99978   0.002   -3.261   0.99944

As can be seen from the p-values (evaluated at an [alpha] = 0.05 level of significance) for each variable, most of them follow a normal distribution except for h1b and h2b which do not follow a normal distribution perhaps due to idiosyncratic properties of the data.

Shapiro-Wilk W test for normal data for Electronics Industry
(prepared using Stata11.0)

Variable |   Obs   W         V       z        Prob>z

h1a |        13    0.99361   0.112   -4.280   0.99999
h2a |        13    0.89973   1.766   1.114    0.13259
h3a |        13    0.86128   2.443   1.750    0.04005
h4a |        13    0.96628   0.594   -1.021   0.84626
h5a |        13    0.81475   3.263   2.317    0.01026
h1b |        13    0.77568   3.951   2.692    0.00356
h2b |        13    0.98136   0.328   -2.182   0.98544
h3b |        13    0.96628   0.594   -1.021   0.84626
h4b |        13    0.96628   0.594   -1.021   0.84626
h5b |        13    0.99481   0.091   -4.686   1.00000

For the electronics industry, most variables indicate a normal distribution except for h3a, h5a, and h1b, which do not follow a normal distribution.

Runs Test

The runs test is basically a nonparametric statistical test for randomness which tests whether or not sequences formed in the sample are randomly generated, or are deterministic. A run is the test statistic which is a sequence of like elements that are preceded and followed by different elements or no element at all according to the order of observations. These may be then subject to a threshold of central tendency that aids in classifying the "runs" in a sample. The hypothesis is stated as follows:

[H.sub.0]: Observations are generated randomly

[H.sub.1]: Observations are not randomly generated

Runs test for Automotive Industry using the median as threshold
(prepared using Stata11.0)

N(h1a <= 4.5) = 2                   N(h2a <= 4.5) = 2
N(h1a > 4.5) = 2                    N(h2a > 4.5) = 2
obs = 4                             obs = 4
N(runs) = 2                         N(runs) = 2
z = -1.22                           z = -1.22
Prob> [absolute value of z] = .22   Prob> [absolute value of z] = .22

N(h1b <= 4) = 3                     N(h2b <= 4) = 3
N(h1b > 4) = 1                      N(h2b > 4) = 1
obs = 4                             obs = 4
N(runs) = 2                         N(runs) = 2
z = -1                              z = -1
Prob> [absolute value of z] = .32   Prob> [absolute value of z] = .32

N(h3a <= 3.5) = 2                   N(h4a <= 5) = 4
N(h3a > 3.5) = 2                    N(h4a > 5) = 0
obs = 4                             obs = 4
N(runs) = 2                         N(runs) = 1
z = -1.22                           z = .
Prob> [absolute value of z] = .22   Prob> [absolute value of z] = .

N(h5a <= 4) = 3
N(h5a > 4) = 1
obs = 4
N(runs) = 2
z = -1
Prob> [absolute value of z] = .32

N(h5b <= 4.5) = 2
N(h5b > 4.5) = 2
obs = 4
N(runs) = 2
z = -1.22
Prob> [absolute value of z] = .22

As can be seen from the above results of the runs test for the automotive industry, almost all variables have p-values greater than 0.05 (considering [alpha] = 0.05 significance level) except h4a. This implies that values for the responses to the hypotheses are randomly generated, indicating that there is no certain pattern (bias) with regards to how companies answered the perception survey.

Runs test for Electronics Industry using the median as threshold
(prepared using Stata11.0)

N(h1a <= 3) = 7                   N(h2a <= 3) = 7
N(h1a > 3) = 6                    N(h2a > 3) = 6
obs = 13                          obs = 13
N(runs) = 2                       N(runs) = 2
z = -3.18                         z = -3.18
Prob> [absolute value of z] = 0   Prob> [absolute value of z] = 0

N(h1b <= 4) = 13                  N(h2b <= 4) = 11
N(h1b > 4) = 0                    N(h2b > 4) = 2
obs = 13                          obs = 13
N(runs) = 1                       N(runs) = 2
z = .                             z = -2.91
Prob> [absolute value of z] = .   Prob> [absolute value of z] = 0

N(h3a <= 4) = 12                  N(h4a <= 5) = 13
N(h3a > 4) = 1                    N(h4a > 5) = 0
obs = 13                          obs = 13
N(runs) = 2                       N(runs) = 1
z = -2.35                         z = .
Prob> [absolute value of z] = .02 Prob> [absolute value of z] = .

N(h3b <= 4) = 13                  N(h4b <= 4) = 13
N(h3b > 4) = 0                    N(h4b > 4) = 0
obs = 13                          obs = 13
N(runs) = 1                       N(runs) = 1
z = .                             z = .
Prob> [absolute value of z] = .   Prob> [absolute value of z] = .

N(h5a <= 4) = 11
N(h5a > 4) = 2
obs = 13
N(runs) = 2
z = -2.91
Prob> [absolute value of z] = 0

N(h5b <= 4) = 13
N(h5b > 4) = 0
obs = 13
N(runs) = 1
z = .
Prob> [absolute value of z] = .

Results greatly differ for the electronics industry as all p-values for all hypotheses are less than 0.05, implying that all these variables are not randomly generated. This may imply several things. One is that the companies really have a pattern/bias they follow in answering the survey. Another is that the bias may not exist, but rather there is an innate, idiosyncratic property in the data that indicates the lack of randomness in the data.

AUTHORS' NOTE

Research assistance was provided by Christopher James Cabuay of the De La Salle University Manila, School of Economics.

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Michael Angelo A. Cortez, Ritsumeikan Asia Pacific University

Katarina Marsha Utama Nugroho, Ritsumeikan Asia Pacific University