Microsoft Word - 11161-41160-1-SM-writer2-new Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 2 ajfa.macrothink.org 330 Do Firms' Characteristics Make a Difference of the Choice of Capital Budgeting Practices? Evidence from Sri Lanka Lingesiya Kengatharan Department of Financial Management, University of Jaffna, Sri Lanka E-mail: lingesiya@yahoo.com Received: May 2, 2017 Accepted: Nov. 24, 2017 Published: December 24, 2017 doi:10.5296/ajfa.v9i2.11161 URL: https://doi.org/10.5296/ajfa.v9i2.11161 Abstract The aim of this study was to examine the differences of the choice of capital budgeting practices in terms of firms’ characteristics of Sri Lankan companies. The primary data were garnered from 186 financial officers using self-administered questionnaires. Collected data were then analysed using independent sample t- test. The Results of the study revealed that the use of simple capital budgeting practices were mostly preferred by small sized firms and mainly managed by finance professionals with non-MBA educational qualifications and short tenure. Sophisticated and advanced capital budgeting practices were used mostly by large firms; and were mainly managed by finance professionals with master of business administration qualification and long tenure. According to the industry differences, accounting rate of return was primarily applied by non-MBA qualified financial officers and was also preferred by non-manufacturing firms. None of the other methods made any significant differences in terms of type of industry. Sophisticated capital budgeting practices were determined by the size of the capital budget, advanced capital budgeting practices were determined by both the size of the capital budget and the educational qualifications of the finance professionals. In a similar vein, simple capital budgeting practices were determined by the size of the capital budget, the educational qualifications of the financial officers, and type of industry. Overall, this study has made parametric contributions to the choice of capital budgeting practices in terms of firms’ characteristics of Sri Lankan companies. The findings of the study are useful to the investment decision makers when they are appraising investment projects. Keywords: Capital budgeting practices, firm characteristics, Sri Lankan companies Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 2 ajfa.macrothink.org 331 1. Introduction 'Capital budgeting practices are the methods and techniques used to evaluate and select an investment project’ (i.e., the decision-making role of the accounting system) (Verbeeten,2006, p.108). Capital budgeting is the application of the principle of profit maximisation to multi-period projects to pave the way for a firm’s growth, survival and sustainability. Capital budgeting techniques are the best alternative for investment decision makers, to help them decide to invest a fixed amount today in exchange for an uncertain stream of future payoffs. Capital budgeting decisions have been recognized as the most important strategic decisions for an organisation to determine how much to invest in specific assets and when to invest (Verbeeten, 2006).The long-term success of a firm depends on excellent investment decisions more than any other factor (Megginson, Smart & Lucey, 2008). The majority of firms’ investment decisions involve the acquisition of fixed assets, for example, the purchase of land, plant, equipment and buildings. Firms invest hundreds of billions of dollars every year in investment projects. Capital investment decisions are thus of utmost important in determining a firm’s fortunes over many years. The ways of looking at capital budgeting practices are different from country to country, from company to company and from project to project (Akalu, 2003). This scenario places emphasis on seminal studies that capital budgeting practices are influenced by a ‘country effect’ (e.g., Graham & Harvey, 2001; Hermes, Smid & Yao, 2007). Many studies has been conducted on capital budgeting methods and practices, predominantly in: the USA (e.g., Graham & Harvey, 2001; Ryan & Ryan, 2002), the UK (e.g., Arnold & Hatzopoulos, 2000), Australia (e.g., Truong, Partington & Peat, 2008), China (e.g., Chen, 2008), Canada (e.g., Bennouna, Meredith & Marchant, 2010), Japan (e.g., Shinoda, 2010), Sweden (e.g., Sandahl & Sjogren, 2003), Indonesia (e.g., Leon, Isa & Kester, 2008), Ireland (e.g., Kester & Robbins, 2011), South Africa (e.g., Maroyi & Poll, 2012), New Zealand (e.g., Lord, Shanahan & Bogd, 2004), Tennessee (e.g., Sekwat, 1999), Belgium (e.g., Dardenne, 1998), Romania (e.g., Dragota et al., 2010), Nigeria (e.g., Elumilade, Asaolu & Ologunde, 2006), Pakistan (e.g., Zubairi, 2008), Argentina (Pereiro, 2006), Italy (e.g., Cescon, 1998), Singapore (e.g., Kester & Chong,1998), Bahrain (e.g., Al-Ajmi,Al-Saleh & Hussain, 2011), Cyprus (e.g., Lazaridis, 2004), Croatia (e.g., Dedi & Orsag, 2007), Jordan (e.g., Khamees, Al-Fayoumi & Al-Thuneibat, 2010),Taiwan (e.g., Haddad, Sterk & Wu,2010), Nepal (e.g., Poudel et al., 2009), India (e.g., Singh, Jain & Yadav, 2012), Hong Kong (e.g., Lam, Wang & Lam, 2007), Kuwait (e.g., Mutairi , Tain & Tan, 2012), Libya (e.g., Mohammed, 2013), Ghana (e.g., Tufuor & Doku, 2013), Poland (e.g., Wnuk-Pel, 2013), Kenya (e.g., Kitili & Nganda, 2014), Spain (e.g., Andres, Fuente & Martin, 2015). Comparative studies have been conducted in Europe: the UK, France, Germany and the Netherlands (e.g., Brounen, de Jong & Koedijk, 2004); in the Asia-Pacific region: Malaysia, Singapore and Hong Kong (e.g., Wong, Farragher & Leung, 1987), Australia, Hong Kong, Indonesia, Malaysia, the Philippines and Singapore (Kester et al., 1999); between British and Dutch companies (Akalu, 2003), the Netherlands and China (Hermes et al., 2007); and in Central and Eastern Europe (CEE) including Bulgaria, Croatia, the Czech Republic, Hungary, Latvia, Lithuania, Poland, Romania, the Slovak Republic, and Slovenia (Ander,Mohanty & Toth, 2010). All of these studies have made contributions to Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 2 ajfa.macrothink.org 332 extant literature by illuminating the prevailing capital budgeting practices across many countries. But studies focusing to examine the differences of the choice of capital budgeting practices in terms of firms’ characteristics are very rare. Therefore this study identified the differences of the use of capital budgeting practices in terms of firms' characteristics. Statement of the research problem can be posted as Do firms' characteristics make a difference of the choice of capital budgeting practices? 2. Capital budgeting practices Capital budgeting practices help managers to select n out of N investment projects with the highest profits and an acceptable ‘risk of ruin’ (Verbeeten, 2006, p.108). All capital budgeting practices can be considered into the categories of sophisticated, advanced and naive/simple based on empirical studies (e.g., Haka, 1987; Haka, Gordon & Pinches, 1985; Verbeeten, 2006; Wolffsen, 2012). Naive practices includes payback, the adaptation of required payback and accounting rate of return, and the advanced /net present value based, including sensitivity analysis/break-even analysis, scenario analysis, the adaptation of required return/discount rate, internal rate of return, net present value, uncertainty absorption in cash flows, modified internal rate of return and profitability index. Farragher, Kleiman and Sahu (2001) suggested that a degree of sophistication is represented by the use of discounted cash flow techniques and incorporating risk into the analysis. Sophisticated capital budgeting methods generally include Monte Carlo simulations, game theory, real option, using certainty equivalents, decision trees, CAPM analysis / ß analysis, and adjusting expected values (Verbeeten, 2006; Wolffsen, 2012). 2.1 Firm characteristics and capital budgeting practices This study considers firms’ demographic characteristics that are expected to account for the differences in their choice of capital budgeting practices across countries. Although firms have many characteristics, many seminal studies set out three major characteristics viz., firm size, industry differences, and the financial officer’s educational qualification and experience in the field that have a strong influence on the choice of capital budgeting practices (e.g., Ho & Pike, 1992; Trigeorgis, 1993; Rogers, 1995; Ho & Pike, 1998; Payne, Heath & Gale, 1999; Bowman & Moskowitz, 2001; Graham & Harvey, 2001; Williams & Seaman, 2001; Farragher et al., 2001; Ryan & Ryan, 2002; Billington, Johnson & Triantis,2003; Brounen et al.,2004; McGrath & Nerkar, 2004; Verbeeten, 2006; Hermes et al.,2007; Verma et al.,, 2009; Bennouna et al., 2010; Andres et al., 2015) The size of a firm is one of the major determinants of its capital budgeting practices (e.g., Ho & Pike, 1992; Graham & Harvey, 2001; Farragher et al., 2001; Brounen et al.,2004; Verbeeten, 2006; Mutairi et al., 2012; Lakew & Rao, 2014;). Research supports the notion that large firms adopt more innovative capital budgeting methods, e.g. sophisticated capital budgeting practices, to a larger extent than smaller firms (e.g., Rogers, 1995; Williams & Seaman, 2001 ), since larger firms have the capacity and resources to use sophisticated capital budgeting practices (Ho & Pike, 1992). Payne, Heath and Gale (1999) and Ryan and Ryan (2002) documented the fact that large firms are more inclined to use more sophisticated capital budgeting practices. This is due to the fact that larger firms have larger projects and Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 2 ajfa.macrothink.org 333 the use of sophisticated capital budgeting practices becomes less costly (Payne et al., 1999; Hermes et al., 2007). Many studies considered firm size as size of the capital budget in order to focus the impact of firm characteristics on the choice of capital budgeting practices (e.g.,Pike,1986; Ryan & Ryan, 2002). Nonetheless, the nature of the relationship between the size of a firm and its capital budgeting practice has not been clearly established in literature relating to developing countries. Thus, this leads to research question: RQ1: Is there any significant difference between the size of a firm’s capital budget and its use of capital budgeting practices? And thus, it can be hypothesised that: H1: Simple capital budgeting practices are used when a firm’s capital budget is small. Companies from different industries may vary in their use of capital budgeting practices (e.g., Ho & Pike, 1998). This may, for example, be due to the nature of their business activity, differences in technology, competition and human resource skill, the amount of investment in fixed assets, business risk, and so forth. For instance, widespread use of real option and game theory is more prevalent in the pharmaceutical industry (e.g., Bowman & Moskowitz, 2001; McGrath & Nerkar, 2004), the extraction industry (e.g., Trigeorgis, 1993), the financial services industry and the high-tech industry (e.g., Billington et al.,2003, Verbeeten, 2006). This scholarship explores how industrial types are different in their use of capital budgeting practices, which leads to the research question: RQ2: Is there any significant difference the capital budgeting practices used in different industries? And thus, it can be hypothesised that: H2: Non manufacturing firms use simple capital budgeting practices. Hornstein (2013) found that managers and financial officers significantly influence corporate behavior and performance. In particular, the educational qualifications of financial officers have been recognized as a determinant of capital budgeting practice (Graham & Harvey, 2001). There is a general consensus that a financial officer with a higher level of education will have fewer problems in understanding more sophisticated capital budgeting techniques and thus they will be capable of using them. A positive relationship has been identified between the educational background of financial officers and the use of sophisticated methods (Hermes et al.,2007). Among the U.S. sample, a positive association was found between chief financial officers’ education and the use of sophisticated capital budgeting practices (Graham & Harvey, 2001) and these findings were consistent with those in the Netherlands, Germany and France, but not in the UK (Brounen et al., 2004). There is a dearth of studies in emerging counties on the relationship between financial officers’ educational qualifications and the choice of capital budgeting practices and the results found in developed countries are not consistent with previous studies (e.g., Brounen et al., 2004), leading to a research question: Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 2 ajfa.macrothink.org 334 RQ3: Is there any significant difference between the educational qualification of financial officers’ and firms’ capital budgeting practices? And thus, it can be hypothesised that: H3: Financial officers with higher educational qualifications use more sophisticated capital budgeting practices. Besides the educational qualifications of financial officers, their experience might also determine their choice of capital budgeting practice. However, a handful of research studies have reported that the experience of financial officers will determine the use of capital budgeting methods since over time they become more familiar with more sophisticated capital budgeting methods (e.g., Hermes et al., 2007; Verma et al., 2009) And thus, this study raises the research question: RQ4: Is there any significant difference between years of experience of financial officers and their capital budgeting practices? And thus, it can be hypothesised that: H4: Financial officers with a short tenure use simple capital budgeting practices. 3. Methods Sample 287 companies were listed on the Colombo stock exchange in Sri Lanka as at 31.12.2013. From the 287, 186 companies responded to the survey. Although the people who make capital budgeting decisions in Sri Lanka are named chief financial officers, chief executive officers, financial controllers, finance managers, management accountants, and financial directors, this research commonly refers to them as financial officers. The self-report questionnaire was designed and emailed to the respondents, some were directly distributed and some were posted to financial officers. Data sources The relevant data for the purpose of this study were garnered from primary sources. The primary source of data collection- The questionnaire A questionnaire was administered to collect the primary data. The questionnaire elicited information regarding the company’s demographic information (including the respondent’s qualifications, experience and type of industry) and corporate practices regarding capital budgeting, including the size of the capital budget, the capital budgeting technique and supplementary capital budgeting tools for incorporating risk. The questions measuring the capital budgeting practices were adopted from previous seminal studies (Arnold & Hatzopoulos, 2000; Graham & Harvey, 2001; Brounen et al., 2004; Hermes et al., 2007; Verma et al., 2009). Nonetheless, fundamental changes were made to the questionnaire in order to fit with the Sri Lankan context. The questionnaire survey was carried during the period from June to December 2013. Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 2 ajfa.macrothink.org 335 4. Data Analysis Following paragraphs intend to answer the research question concerning do firms’ characteristics make a difference of the choice of capital budgeting practices. Firstly, descriptive analysis of survey responses is presented including educational qualification of the financial officers, experience of the financial officers, types of industry, and size of the capital budget. Secondly, analysis focuses on the identification of prevailing capital budgeting practices in Sri Lanka. Thirdly, firms’ characteristics are examined to see the differences in the choice of capital budgeting practices. Descriptive analysis of the survey responses Educational qualification of the financial officers Classification of the educational qualification of the financial officers was grouped into: bachelor degree, MBA, non-MBA Master’s, above Master’s degree and professional qualification. Above master degree qualification (e.g., MPhil/PhD or master degree with professional qualification) was held by 52.2% of Financial officers, followed by MBA qualification (29%), non-MBA Master’s (13.4%), Bachelor degree (3.8%) and professional qualification (1.6%). Further to such general classification, the educational qualification of the financial officers was regrouped into two for analytical purposes: MBA-level and above and non-MBA and other. The MBA-level and above includes both MBA and above Master’s degree whereas non-MBA and other includes Bachelor degree, non-MBA Master’s and professional qualification. The classifications were in line with Graham and Harvey (2001) and Brounen et al.(2004). Experience of the financial officers Experience of the financial officers was classified into four groups in terms of number of years they had been in the profession: less than 5 years, 5-9 years, 10-19 years and 20 years and more. The higher number of financial officers had 10 to 19 years’ experience (N=81), followed by 20 years’ and more experience (N=77), 5 to 9 years’ (N=21) and a small number of financial officers had less than 5 years’ experience (N=7). For analytical purposes, experience of Financial officers was regrouped into two in line with Graham and Harvey (2001) and Brounen et al.(2004): short tenure and long tenure. The short tenure includes both less than 5 years’ experience and 5-9 years’ experience whereas long tenure includes both 10-19 years’ experience and 20 and more years’ experience. Types of industry Types of industry were initially classified in terms of their nature (Verbeeten, 2006) as shown in table 1: financial service industry, manufacturing industry, diversified holdings, health care industry, hotel industry and other non-financial industry. As can be seen in the table, 52.7% of industries are manufacturing, followed by other non-financial industry (17.7%), hotel Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 2 ajfa.macrothink.org 336 industry (15.6%), diversified holdings (5.9%), financial service industry (4.8%) and health care industry (3.2%). Table 1. Types of industry Industries No. of Companies (N) Percentage (%) Financial Service Industry (e.g., bank, finance and insurance- FINSERV) 9 4.8% Manufacturing Industry (e.g., beverages, food & tobacco, chemical & pharmaceutical, construction and engineering, foot ware and textile, manufacturing, power and energy motors, oil palms, plantations and trading- MFTG) 98 52.7% Diversified Holdings (DIVERS) 11 5.9% Health care Industry (HEALTH) 6 3.2% Hotel Industry (HOTEL) 29 15.6% Other Non-Financial Industry (e.g., investment trust, land and property, services, stores and supplies and telecommunications (OTNFIN) 33 17.7% Total 186 100.0% Nonetheless, for analytical purposes as suggested by Graham and Harvey (2001) and Brounen et al. (2004), they were regrouped into “manufacturing” and “non-manufacturing”. Size of capital budget Size of capital budget was categorised into five groups in line with Verma et al. (2009): less than LKR 10 million, LKR 1-99 million, LKR 100-499 million, LKR 500 -999 million and LKR 1 billion and over. The large number of Financial officers reported that size of their capital budget is between LKR 100 -499 million (39.2%), followed by LKR 10-99 million (27.4%), LKR 1 billion and over (14%), LKR 500-999 million (10.2%) and less than LKR 10 million (9.1%). For analytical purposes, sizes of capital budget were regrouped into “large” and “small” in line with Graham and Harvey (2001) and Brounen et al.(2004). Firms with capital budget greater than LKR 1 billion is considered as “large” and less than LKR 1 billion is considered as “small” Capital budgeting practices There is a tendency to use multi-methods in making capital budgeting decisions (e.g., Arnold & Hatzopoulos, 2000). As a caveat, prevalent capital budgeting decision tools, namely capital Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 2 ajfa.macrothink.org 337 budgeting methods and capital budgeting tools incorporating risk in Sri Lanka, were subjected to principal component analysis with Varimax rotation in line with many research scholars (e.g., Verbeeten, 2006). The results are presented in table 2. Table 2. Total variance explained for the factors indicating to the capital budgeting practices Factors Advanced / NPV Based Capital Budgeting Practices Sophisticated Capital Budgeting Practices Simple/ Naïve Capital Budgeting Practices Eigen Value 5.822 2.108 1.365 Proportion of Variance Explained 38.815% 14.052% 9.101% Cumulative Percentage Explained 38.815% 52.867% 61.968% Cronbach’s Alpha - Reliability of factors 0.890 0.809 0.744 As can be seen in table 2, prevalent capital budgeting practices in Sri Lanka were grouped into three, in line with the literature: Advanced capital budgeting practices include probability analysis, internal rate of return, scenario analysis, adjusting required return, uncertainty absorption in cash flows, sensitivity analysis and net present value; sophisticated capital budgeting practices include real option, CAPM/β analysis, game theory decisions and decision trees, and simple/naive capital budgeting practices include discounted payback , accounting rate of return and payback (e.g., Verbeeten, 2006; Wolffsen, 2012). Table 3 shows use of capital budgeting practices in Sri Lanka. Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 2 ajfa.macrothink.org 338 Table 3. Capital budgeting practices in Sri Lanka Never Rarely Sometimes Often Always Mean Naive capital budgeting practices Discounted Payback Period (DPB) 1.1% 10.8% 64.0% 24.2% - 3.11 Accounting Rate of Return (ARR) 3.8% 13.4% 61.8% 19.9% 1.1% 3.01 Payback Period (PB) 2.2% 13.4% 34.4% 37.6% 12.4% 3.45 Advanced capital budgeting practices Probability Analysis (PA) 1.1% 3.2% 13.4% 68.8% 13.4% 3.90 Scenario Analysis (SA) 1.6% 5.4% 12.4% 65.1% 15.6% 3.88 Adjusting required return 2.7% 5.9% 14.0% 63.4% 14.0% 3.80 Internal Rate of Return (IRR) 6.5% 5.9% 8.6% 55.9% 23.1% 3.83 Uncertainty absorption in cash flows 0.5% 1.6% 12.4% 69.9% 15.6% 3.98 Sensitivity Analysis (SA) 1.6% 8.1% 11.3% 52.7% 26.3% 3.94 Net Present Value (NPV) 1.6% 2.2% 12.4% 51.6% 32.3% 4.11 Sophisticated capital budgeting practices Real Options (RO) 78.0% 15.6% 6.5% - - 1.28 Game Theory Decisions (GTD) 83.9% 26.0% 2.2% - - 1.18 Decision Trees (DT) 65.6% 27.4% 7.0% - - 1.41 CAPM/β Analysis 77.4% 11.8% 8.6% 2.2% - 1.35 As can be seen in table 3, NPV was the most preferred method of capital budgeting, 83.9% of the financial officers ‘always and often’ preferred it, yielding a mean value of 4.11. This was followed by Uncertainty absorption in cash flows which was ’always and often’ preferred by 85.5% (M=3.98). Sensitivity Analysis was the next ‘always and often’ preferred method by 78% (M=3.94) followed by Probability Analysis with 82.2% (M=3.90), Scenario Analysis with 80.7 % (M=3.88), Internal Rate of Return with 79% (M=3.83), Adjusting required return with 77.4% (M=3.80), PB with 50% (M=3.45), DPB with 24.2% (M=3.11) and ARR with 21% (M=3.01). Methods such as RO, GTD, decision tree and CAPM/β Analysis were not popular, yielding mean values of less than 2.0. Firms’ characteristics and capital budgeting practices The current survey carefully considered the underlying firm characteristics in order to find out the differences in the use/choice of capital budgeting practices: size of capital budget, educational qualification of financial officers, experience of financial officers and types of industry. The mean difference statistics of independent sample t-test was employed. Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 2 ajfa.macrothink.org 339 Size of capital budgets and use of capital budgeting practices As aforesaid, size of capital budgets were grouped into “small” and “large”. The results of the independent sample t-test between size of capital budgets and use of capital budgeting practices are presented in table 4. Table 4. Size of capital budgets and use of capital budgeting practices Capital budgeting practices Size of the capital budget N df Mean SD SE t sig PB Small 160 3.63 .867 .069 Large 26 184 2.35 .629 .123 9.064 0.000 DPB Small 160 3.18 .578 .046 Large 26 184 2.73 .724 .142 2.977 0.000 ARR Small 160 3.13 .652 .052 Large 26 184 2.31 .788 .155 5.754 0.000 NPV Small 160 3.97 .796 .063 Large 26 184 4.96 .196 .038 -6.312 0.000 IRR Small 160 3.73 1.046 .083 Large 26 184 4.50 .860 .169 -4.125 0.000 RO Small 160 1.13 .357 .028 Large 26 184 2.23 .765 .150 -11.947 0.000 GTD Small 160 1.08 .274 .022 Large 26 184 1.81 .694 .136 -9.516 0.000 Sensitivity Analysis Small 160 3.79 .898 .071 Large 26 184 4.85 .368 .072 -5.886 0.000 Scenario Analysis Small 160 3.84 .797 .063 Large 26 184 4.08 .744 .146 -1.467 0.151 Decision Trees Small 160 1.26 .441 .035 Large 26 184 2.35 .745 .146 -10.379 0.000 CAPM/β Analysis Small 160 1.16 .413 .033 Large 26 184 2.58 1.027 .201 -12.467 0.000 Uncertainty absorption in cash flows Small 160 3.91 .570 .045 Large 26 184 4.46 .761 .149 -4.383 0.000 Adjusting required return Small 160 3.76 .867 .069 Large 26 184 4.08 .688 .135 -2.118 0.041 Probability Analysis Small 160 3.88 .704 .056 Large 26 184 4.04 .662 .130 -1.065 0.288 Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 2 ajfa.macrothink.org 340 As can be seen in table 4, small firms more highly applied the payback method (M=3.63, SE=.06) than large firms (M=2.35, SE=.12). The difference was significant t (184) = 9.064, p < 0.01. A similar pattern was observed in the application of DPB and ARR that small firms more highly applied DPB and ARR (M=3.18, SE=.04; M=3.13, SE=.05) than large firms (M=2.73, SE=.142; M=2.31, SE=.155) and the differences were also significant t (184) = 2.877, p < 0.01 and t (184) = 5.754, p < 0.01 respectively. Therefore, it is fair to say that simple capital budgeting practices PB, DPB and ARR were more highly applied by small firms in comparison with large firms. As to advanced capital budgeting practices, NPV and IRR are more highly significantly applied by large firms (M=4.96, SE=.038; M=4.50, SE=.169) than small firms (M=3.97, SE=.063; M=3.73, SE=.083) and the significant mean differences were found t (184) = -6.312, p < 0.01, t (184) = -4.125, p < 0.01, respectively. As regard to sophisticated capital budgeting practices, RO, GTD are also highly applied by large companies (M=2.23, SE=.150; M=1.81, SE=.136) than small companies (M=1.13, SE=.028; M=1.08, SE=.022) and the significant differences were RO and GTD, respectively t (184) = -11.947, p < 0.01, t (184) = -9.516, p < 0.01. In the case of sensitivity analysis, uncertainty absorption in cash flows and adjusting required return were significantly highly applied by large firms (M=4.85, SE=.072; M=4.46, SE=.149, and M=4.08, SE=.135) than small companies (M=3.79, SE=.071; M=3.91, SE=.045, and M=3.76 , SE=.069) and the significant differences were found t (184) = -5.886, p < 0.01, t (184) = -4.383, p < 0.01 and t (184) = - 2.118 , p < 0.041, respectively. Although scenario analysis and probability analysis were highly applied by large firms, the differences were not statistically significant at p < 0.05. Moreover, CAPM/β analysis and decision trees were also more highly applied by large companies (M=2.58, SE=.201; M=2.35, SE=.146) than small companies (M=1.16, SE=.033; M=1.26, SE=.035) and the differences were significant t (184) = -12.467, p < 0.01, t (184) = -10.379, p < 0.01. Therefore, simple capital (naive) budgeting practices were significantly mostly used by small firms, nonetheless, advanced and sophisticated capital budgeting practices were significantly mostly used by large firms. Consequently, hypothesis (H1) that simple capital budgeting practices are used when a firm’s capital budget is small was supported. Types of industry and use of capital budgeting methods This section draws attention to the differences between types of industry and capital budgeting methods. Types of industry were grouped into non-manufacturing (NMANU) and manufacturing (MANU) in line with Graham and Harvey (2001). The results of an independent sample t-test are presented in table 5. Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 2 ajfa.macrothink.org 341 Table 5. Types of industry and use of capital budgeting practices Capital budgeting practices Industry N df Mean SD SE t sig PB NMANU 88 3.49 .971 .103 MANU 98 184 3.41 .929 .094 .577 .564 DPB NMANU 88 3.15 .635 .068 MANU 98 184 3.08 .604 .061 .727 .468 ARR NMANU 88 3.11 .718 .077 MANU 98 184 2.92 .728 .073 1.839 .048 NPV NMANU 88 4.18 .891 .095 MANU 98 184 4.04 .745 .075 1.175 .242 IRR NMANU 88 3.88 1.059 .113 MANU 98 184 3.80 1.055 .107 .509 .611 RO NMANU 88 1.34 .604 .064 MANU 98 184 1.23 .552 .056 1.252 .212 GTD NMANU 88 1.17 .407 .043 MANU 98 184 1.19 .469 .047 -.362 .718 Sensitivity Analysis NMANU 88 4.00 .935 .100 MANU 98 184 3.89 .907 .092 .831 .407 Scenario Analysis NMANU 88 3.89 .794 .085 MANU 98 184 3.87 .795 .080 .163 .871 Decision Trees NMANU 88 1.44 .641 .068 MANU 98 184 1.39 .603 .061 .608 .544 CAPM/β Analysis NMANU 88 1.32 .670 .071 MANU 98 184 1.39 .782 .079 -.648 .518 Uncertainty absorption in cash flows NMANU 88 3.93 .708 .075 MANU 98 184 4.03 .546 .055 -1072 .285 Adjusting required return NMANU 88 3.83 .887 .095 MANU 98 184 3.78 .819 .083 .432 .666 Probability Analysis NMANU 88 3.88 .724 .077 MANU 98 184 3.93 .677 .068 .294 .603 As can be seen in table 5, only ARR was statistically significant and mostly applied by non-manufacturing firms (M=3.11, SE=.077) than manufacturing firms (M=2.92, SE=.073) at t (184) = 1.839, p < 0.05. Save for ARR, all other capital budgeting practices were not statistically significant with type of industry (p > 0.05). Therefore, in all cases except ARR, type of industry was not significantly different on use of capital budgeting practices. The results only supported the notion that the use of ARR was significantly greater in non-manufacturing firms than in manufacturing firms. Therefore, Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 2 ajfa.macrothink.org 342 hypothesis (H2) that non manufacturing firms use simple capital budgeting practices was supported that ARR was mostly used by non manufacturing companies. Education qualifications of financial officers and use of capital budgeting practices The educational qualifications of financial officers were grouped into MBA and non-MBA qualifications in line with Graham and Harvey (2001). An independent sample t-test was performed to see the difference between educational qualification of financial officers and the use of capital budgeting practices. The results are presented in table 6. Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 2 ajfa.macrothink.org 343 Table 6. Education qualifications and use of capital budgeting practices Capital budgeting practices Educational qualifications N df Mean SD SE t sig PB Non-MBA 35 3.91 .781 .132 MBA 151 184 3.34 .951 .077 3.332 .001 DPB Non-MBA 35 3.37 .598 .101 MBA 151 184 3.05 .609 .050 2.828 .007 ARR Non-MBA 35 3.37 .690 .117 MBA 151 184 2.93 .713 .058 3.412 .001 NPV Non-MBA 35 3.06 .873 .147 MBA 151 184 4.35 .580 .047 -8.356 .000 IRR Non-MBA 35 2.60 1.193 .202 MBA 151 184 4.12 .783 .064 -9.275 .000 RO Non-MBA 35 1.09 .284 .048 MBA 151 184 1.33 .619 .050 -2.287 .023 GTD Non-MBA 35 1.06 .236 .040 MBA 151 184 1.21 .471 .038 -2.802 .006 Sensitivity Analysis Non-MBA 35 2.97 .985 .166 MBA 151 184 4.17 .743 .060 -6.743 .000 Scenario Analysis Non-MBA 35 3.03 .857 .145 MBA 151 184 4.07 .634 .052 -8.182 .000 Decision Trees Non-MBA 35 1.17 .382 .065 MBA 151 184 1.47 .651 .053 -2.608 .010 CAPM/β Analysis Non-MBA 35 1.14 .355 .060 MBA 151 184 1.40 .785 .064 -2.980 .003 Uncertainty absorption in cash flows Non-MBA 35 3.34 .765 .129 MBA 151 184 4.13 .485 .040 -7.682 .000 Adjusting required return Non-MBA 35 2.97 .985 .166 MBA 151 184 3.99 .688 .056 -7.247 .000 Probability Analysis Non-MBA 35 3.26 .701 .118 MBA 151 184 4.05 .609 .050 -6.769 .000 As shown in table 6, simple capital budgeting practices PB , DPB and ARR were more highly applied by non-MBA financial officers (M=3.91, SE=.132; M=3.37, SE=.101, and M=3.37, SE=.117) and the differences were significant t (184) = 3.332, p < 0.01, t (184) = 2.828, p < 0.01 and t (184) = 3.412 , p < 0.01, respectively. In the case of advanced capital budgeting practices, NPV and IRR were highly applied by financial officers who had MBA qualifications (M= 4.35, SE=.047; M=4.12, SE=.064) than non MBA Financial officers (M=3.06, SE=.147; M=2.60, SE= .202) and the differences were significant t (184) = -8.356, Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 2 ajfa.macrothink.org 344 p < 0.01, t (184) = -9.275, p < 0.01, respectively. A similar pattern was observed in sophisticated capital budgeting practices. RO and GTD were also more highly applied by financial officers who had an MBA (M=1.33, SE=.050; M=1.21, SE=.038) than non-MBA financial officers (M=1.09, SE=.048; M=1.06, SE=.040) and the significant differences were found t (184) = -2.287, p < 0.05, t (184) = -2.802, p < 0.01, respectively. As for sensitivity analysis, scenario analysis, uncertainty absorption in cash flows, adjusting required return and probability analysis were significantly more highly applied by financial officers who had an MBA (M=4.17, SE=.060; M=4.07, SE=.052, M=4.13, SE=.040, M=3.99, SE=.052 and M=4.05, SE=.050) than non-MBAs Financial officers (M=2.97, SE=.166; M =3.03, SE =.145, M=3.34, SE=.329, M=2.97, SE=.166 and M=3.26, SE =.118) at t (184) = -6.743, p < 0.01, t (184) = -8.182, p < 0.01, t (184)= -7.682, p <0.01, t (184) = -7.247 , p < 0.01 and t (184) = - 6.769 , p < 0.01, respectively. Moreover, CAPM/β analysis and decision trees were more highly applied by financial officers with MBAs (M=1.40, SE= .064; M=1.47, SE=.053) than financial officers with non-MBAs (M=1.14, SE=.060; M=1.17, SE=.065) and the differences were significant t (184) = -2.980, p < 0.01, t (184) = -2.608, p < 0.01, respectively. Therefore, simple capital (naive) budgeting practices were significantly mostly used by financial officers with non-MBA qualification, nonetheless, advanced and sophisticated capital budgeting practices were significantly mostly used by financial officers with MBA qualifications. Consequently, hypothesis (H3) that Chief Financial Officers with higher educational qualifications use more sophisticated capital budgeting practices was supported. Experience of financial officers and use of capital budgeting practices This section examines the difference in use of capital budgeting practice in terms of the experience of the financial officers (tenure). The tenure was grouped into “short” and “long” in line with Graham and Harvey (2001). Table 7 shows the results of the independent sample t-test. Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 2 ajfa.macrothink.org 345 Table 7. Tenure and use of capital budgeting practices Capital budgeting practices Tenure N df Mean SD SE t sig PB Short 28 3.79 .630 .119 Long 158 184 3.39 .982 .078 2.076 .039 DPB Short 28 3.18 .548 .104 Long 158 184 3.10 .630 .050 0.609 .543 ARR Short 28 3.14 .803 .152 Long 158 184 2.99 .714 .057 1.042 .299 NPV Short 28 3.75 .799 .151 Long 158 184 4.17 .808 .064 -2.546 .012 IRR Short 28 3.64 .870 .164 Long 158 184 3.87 1.083 .086 -1.037 .301 RO Short 28 1.11 .315 .060 Long 158 184 1.32 .609 .048 -2.728 .008 GTD Short 28 1.00 .000 .000 Long 158 184 1.22 .470 .037 -5.755 .000 Sensitivity Analysis Short 28 3.68 .863 .163 Long 158 184 3.99 .924 .073 -1.726 .092 Scenario Analysis Short 28 3.79 .568 .107 Long 158 184 3.89 .826 .066 -0.656 .513 Decision Trees Short 28 1.11 .315 .060 Long 158 184 1.47 .645 .051 -2.897 .004 CAPM/β Analysis Short 28 1.11 .315 .060 Long 158 184 1.40 .773 .062 -3.406 .001 Uncertainty absorption in cash flows Short 28 3.64 .731 .138 Long 158 184 4.04 .590 .047 -3.194 .002 Adjusting required return Short 28 3.64 .911 .172 Long 158 184 3.83 .839 .067 -1.069 .286 Probability Analysis Short 28 3.79 .568 .107 Long) 158 184 3.92 .719 .057 -1.137 .262 As can be seen in table 7, simple capital budgeting practices PB, DPB and ARR were applied more by financial officers with short tenure (M=3.79, SE=.119; M=3.18, SE=.104, and M=3.14, SE=.152) than financial officers with long tenure financial officers (M=3.39, SE=.078; M=3.10, SE=.050, and M= 2.99, SE=.057), however, the difference was only significant for PB t (184) = 2.076, p < 0.05. Similarly, for advanced capital budgeting practices, NPV and IRR were mostly more used by financial officers with long tenure (M=4.17, SE=.064; M=3.87, SE=.086) than financial officers with short tenure (M=3.75, Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 2 ajfa.macrothink.org 346 SE=.151; M=3.64, SE=.164), nonetheless, the difference was only significant for NPV (t (184) = -2.546, p < 0.05. In the case of sophisticated capital budgeting practices, RO and GTD were also mostly more applied by financial officers with long tenure (M=1.32, SE=.048; M= 1.22, SE=.037) than financial officers with short tenure (M=1.11, SE=.060; M=1.00, SE=.000) and the differences were significant t (184) = -2.728, p < 0.01, t (184) = -5.755, p < 0.01, respectively. CAPM/β analysis and decision trees were more highly applied by financial officers with long tenure (M=1.40, SE=.062; M=1.47, SE=.051) than financial officers with short tenure (M=1.11, SE=.060; M=1.11, SE=.060) and the differences were significant t (184) = -3.406, p < 0.01, t (184) = -2.897, p < 0.01, respectively. Uncertainty absorption in cash flows was also more highly significantly applied by financial officers with long tenure (M=4.04, SE=.047) than financial officers with short tenure (M=3.64, SE=.138) at t (184) = -3.194, p < 0.01. Notwithstanding that sensitivity analysis, scenario analysis, adjusting required return and probability analysis were mostly used by financial officers with long tenure, they were not statistically significant. Therefore, in all cases, simple capital (naive) budgeting practices were mostly used by financial officers with short tenure (significantly different only for PB), nonetheless, advanced and sophisticated capital budgeting practices (NPV, RO, GTD, uncertainty absorption in cash flows, decision trees and CAPM/β analysis) were significantly mostly used by financial officers with long tenure. Consequently, hypothesis (H4) that financial officers with a short tenure use simple capital budgeting practices was supported. 5. Conclusion The differences of the firms’ characteristics on the application of capital budgeting practices were examined in this study. 186 Sri Lankan firms were responded to the survey and structured questionnaire was used to collect the data. Independent sample t test was performed to examine the differences on the choices of capital budgeting practices in terms of firms' characteristics. Outcome of the study revealed that the use of the payback method was preferred by small sized firms and mainly managed by financial officers with non- MBA educational qualifications and short tenure. Industry differences did not make any significant difference to the use of pay back period. Discounted payback period was more significantly used by small firms than large firms and managed by financial officers with non-MBA educational qualifications. Accounting rate of return was primarily applied by non-MBA financial officers and was also preferred by non-manufacturing firms. Net present value and internal rate of return were used mostly by large firms than small firms; these were mainly managed by financial officers who have qualified masters in business administration and long tenure. Sophisticated capital budgeting practices, in particular real option and game theory, were significantly preferred by large companies more than by small companies and those were managed by masters in business administration qualified financial officers who had a long tenure. Size of the capital budget increases the application of sophisticated and advanced capital budgeting practices. The relationship is much stronger in the case of sophisticated capital budgeting practices. A larger capital budget reduces the use of naive capital budgeting practices. Therefore, financial officers use sophisticated and advanced capital budgeting practices when the size of the capital budget is large. Overall, this study has Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 2 ajfa.macrothink.org 347 made a parametric contribution. In a nutshell, this study serves as a springboard for future research. References Akalu,M.M. (2001). 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