Telecommunication investment effects on labour productivity.
INTRODUCTIONSince the 1990s, telecommunication development has experienced significant growth. The revenue for telecommunication service alone increased from US$400 billion in 1991 to more than US$1 trillion in 2003 (Heshmati & Yang, 2006). The market revenue of telecommunication equipment grew from US$120 billion in 1991 to US$300 billion in 2003 (Heshmati & Yang, 2006). In spite of such considerable increases in telecommunication spending, its contribution to business performance is not yet empirically attested. Research reveals an 'IT productivity paradox', which refers to a weak association between information technology (IT) investment and performance and prompts a heated debate about the strategic value of IT investment such as the investment in telecommunication (Ray, Muhanna, & Barney, 2007). Some studies show a positive link between IT spending like telecommunication spending and productivity growth with data from the second half of the 1990s while others still find evidence of the paradox (Lin & Shao, 2006; Oliner & Sichel, 2002). Dehning & Richardson (2002) note that "when and why is there a payoff?" are also key questions for the research.
IT capability, i.e. the ability to deliver systems when needed and to effect business strategies through IT implementation, varies from one organization to another (Ross, Beath, & Goodhue, 1996). Previous research using contingency theory posits that environmental, organizational, and technological levels help determine the combination of organizational structure and processes that yields optimal performance (Covaleski et al, 2003; Liu & O'Farrell, 2013; Liu et al., 2014a; Liu et al., 2014b; Liu, Wang, & Yao, 2014; Sisaye & Birnberg, 2010; Yao, Liu, & Chan, 2010). Melville, Kraemer, and Gurbasani (2004) find that IT is valuable, but the extent and dimensions are dependent upon internal and external factors. Because the returns on IT investments appear to be contingent, Dardan, Stylianou, and Kumar (2007) note the need for further research into what determines success when assessing whether IT is effective.
This study builds on recent advances in IT value literature and extends this literature by studying what types of firms are in a better position to benefit from telecommunication investment. In particular, this study examines whether and how telecommunication spending impacts labour and administrative productivity differently in firms with different size and industry type. Empirical results not only reveal a significantly positive relation between telecommunication spending and labour or administrative productivity but also uncover that durable-goods industry firms and larger firms can realize higher business value from telecommunication investment in improved employee productivity.
LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT
In advanced economies, productivity growth depends both on technological innovation and on the organisational changes enabled by technological innovation (Brynjolfsson & Hitt, 2003). Productivity refers to the efficiency with which outputs are produced for a given level of inputs (Sircar, Turnbow, & Bordoloi, 2000). Kraemer and Dedrick (1994) found correlation between IT investment growth and productivity growth with data from 43 countries. CEPII (2003) reports strong gains in output per hour worked associated with information and communication capital growth in France during the period 1996-2000. Dewan & Min (1997) find evidence of excess returns on IT investment relative to labour input. Rai, Patnayakuni, and Patnayakuni (1997) find a significantly positive relation between labour productivity and IT investment. Therefore, the following hypothesis has been developed.
H1: There is a positive and significant relationship between telecommunication spending and Labour productivity.
Many researchers have studied to what extent the application of IT leads to improved organizational performance in terms of financial measures such as income, return on assets (ROA), or return on equity (ROE). Brynjolfsson (1996) analyzes 370 firms over the period 1988-1992 and suggests that IT investment has no correlation with total shareholder return, ROA and ROE. Based on over 2,000 observations of 624 firms over 1988-1993, Sircar, Turnbow, & Bordoloi (2000) find no significant link between IT investments and net income. Oliner and Sichel (1994 & 2000) conclude that investment in information and communication technology are only associated with 0.16-0.28 percent additional economic growth, too small to lead to substantial economic growth. Prior research has often referred to such a weak link as an 'IT productivity paradox'.
One possible explanation for the IT productivity paradox is that IT investment takes time to realize its value. Brynjolfsson and Hitt (2003) found that the productivity and output contributions associated with computerization were greater over long periods with data over 1987-1994. Kobelsky et al. (2008) studied data on IT spending from 1992-1997 to find that firms with higher IT budget levels exhibited higher market returns over the subsequent three years. Im, Dow, and Grover (2001) found the existence of the productivity paradox in the 1980s, but a positive market reaction to IT investment announcements in the early 1990s. David (1990) argues that information and communication technology typically is a general-purpose technology that will spread with a time lag. This study examines more recent data over 1998-2000 when more time is available for organisational learning and adjustment since the development of telecommunication technology.
Another possible explanation for the IT productivity paradox is that contingencies were not examined in identifying the effect of IT on performance (Brynjolfsson & Hitt, 2000). Contingency theories proposed that different strategies were appropriate for each competitive business setting (Myers, Kappelman, & Prybutok, 1997). Kettinger et al. (1994) suggested that sustainability of competitive advantage might be achieved by leveraging unique firm attributes with information technology to realize long-term performance gains. The findings of Cron and Sobol (1983) with 138 medical supply wholesalers confirm that the presence of organizational factors might affect returns derived from IT spending (Grover et al., 1998). Melville, Kraemer, and Gurbasani (2004) conclude that IT is valuable, but the extent and dimensions are dependent upon internal and external factors. Therefore, in the right setting, IT spending does matter strategically (Ray, Muhanna, & Barney, 2007). The unique contribution of this study lies in the investigation of whether and how firm attributes such as size and industry type play a role in realizing IT's value in improving productivity with relatively recent data.
Firm size is an important firm characteristic that influences performance (Saunders & Jones, 1992). Firms are expected to adjust their practices (e.g. IT spending) to align with organizational characteristics including size to produce higher performance (Kobelsky et al., 2008). Kivijarvi and Saarinen (1995) showed that the relationship between IT investment and firm performance was somewhat dependent on firm size, as IT investment costs seemed to be related to profitability and growth in larger companies. How firm size moderates IT business value is still an empirical question. Therefore, the following hypothesis has been developed.
H2: The impact of telecommunication spending on labour productivity varies among firms of different sizes.
Industry is another important firm characteristic that influences firm performance (Saunders & Jones, 1992). Industry characteristics influence a firm's capability to achieve and sustain a competitive advantage (Porter, 1985). The amount of industry competition, process or product orientation, and information intensity of an industry have been offered as industrial contingencies in the realization and preservation of competitive advantage resulting from IT (Kettinger et al., 1994).
The findings of quantitative empirical studies that certain industries attain higher IT productivity impact and greater cost reduction than others provide further support for the inclusion of industry characteristics as an important contingent factor influencing the realization of IT business value (Melville, Kraemer, & Gurbasani, 2004). Dehning, Dow, and Stratopoulos (2004) revealed that there were differences in manufacturing and service firms in terms of leveraging technology. In addition, Morrison (1997) found that most of the increase in labour demand for skilled labour from IT implementation was driven by durable-goods industries as compared to nondurable-goods industries in U.S. manufacturing industries. Therefore, the following hypothesis has been developed.
H3: There is a positive and significant relationship between telecommunication spending and labour productivity of durable-goods firms.
RESEARCH DESIGN
Since measurement of productivity and its determinants is more accurate for manufacturing than non-manufacturing industries (Morrison, 1997), this study examines the value of IT in improving labour and administrative productivity in manufacturing industries. The data collection and statistical analysis are discussed in detail in the following subsections.
Sample and Data Collection
To test the hypotheses, an archival empirical study is conducted with data over 19982000. Telecommunication spending data for all three years are obtained from Gartner IT Spending and Staffing Survey Results for US companies. Firm performance data are obtained from the Compustat and Global Researcher databases. Twenty two percent (22%) of sample firms produce electronics and other electrical equipment; eighteen percent (18%) industrial machinery and equipment; fifteen percent (15%) instruments and related products; fourteen percent (14%) chemicals and allied products; six percent (6%) primary metal; six percent (6%) transportation equipment; four percent (4%) printing and publishing; four percent (4%) fabricated metal products; three percent (3%) apparel and other textile products; three percent (3%) rubber and miscellaneous plastics products; two percent (2%) paper and allied products; one percent (1%) stone, clay, and glass products; one percent (1%) food and kindred products; and one percent (1%) textile mill products. Hence, sixty eight (68%) of sample firms are from durable goods industries while thirty two percent (32%) of sample firms are from non-durable goods industries.
Measurements
Telecommunication spending is measured by the Gartner's (year) reports as a percentage of revenue. Industry type is classified into either durable goods industry or non-durable goods industry like Morrison (1997). Measurements of labour productivity and firm size are the same as those used by Rai, Patnayakuni, and Patnayakuni (1997). Firm size is measured by total number of employees in thousands. Labour productivity is the ratio of value added to total number of employees. Value added is the sum of profit before tax, depreciation, rent rates and insurance, wages and salaries, employee benefits, advertising, professional services, interest expense, and other overhead (Wilson, 1971).
Research Models
Following Devaraj & Kohli (2000), Michael (2007), and Kobelsky et al. (2008), the authors model the value of telecommunication in the following way:
Labour productivity = [alpha] + [[beta].sub.1]Telecom + [[beta].sub.2]Size + Industry + [[beta].sub.4]Tele com*Size + [[beta].sub.5]Telecom*Industry + [epsilon]
where Telecom is the telecommunication spending that adds value expressed as a percentage of total revenue. Size refers to the firm size measured by total number of employees. Industry uses dummy variable 1 for durable goods industry and 2 for non-durable goods industry.
The direct impact of telecommunication spending on labour productivity or administrative productivity can be estimated by examining the coefficient [[beta].sub.1]. The fit between telecommunication spending and firm characteristics is indicated by the sign and significance of the coefficients ([[beta].sub.4]~[[beta].sub.5]) associated with the interaction terms in the model. No significant correlation is found among Telecom, Size, and Industry.
RESULTS AND DISCUSSION
Empirical results are presented in Table 1. Empirical tests support hypotheses H1 and H3. H1 predicts a positive correlation between telecommunication spending and labour productivity. The relationship is found to be significantly positive with [[beta].sub.1] (53.617) significant at a critical value of .05. Such a finding confirms findings of prior studies such as Rai, Patnayakuni, & Patnayakuni (1997). Empirical results also support H3. H3 predicts that labour productivity of durable-goods firms (denoted as 1 vs. 2 for non-durable goods) is more positively related to telecommunication spending. [[beta].sub.5] (-56.819) is significantly negative as predicted. The findings are in agreement with the findings of Morrison (1997) that most of IT resultant increases in skilled labour are in durable-goods industries.
Empirical results do not support H2 which predict a significant coefficient for [[beta].sub.4]. [[beta].sub.4] is positive but not significantly different from zero. Economists such as Dosi (1988) argue that IT results in such a radical shift in technology that the economic advantages from large size and high volume are reduced. Small firms are even alleged to have superiority of programmable technologies (Kelly, 1994). The finding empirically supports these arguments.
Many researchers measure firm size by the natural log of sales (Michael, 2007; Kobelsky et al., 2008). As a robustness check, the total number of employees is replaced by the natural log of sales to measure firm size. The conclusions still hold true when firm size is measured by the natural log of sales. The conclusions of our model appear to be robust.
RECOMMENDATION FOR FUTURE RESEARCH
There are several directions in which this work could be extended. First, the author's study highlights that firm characteristics influence how effective telecommunication may lead to improved productivity in manufacturing industries. Further research could focus on testing the relationship between contingent organizational characteristics and the value of telecommunication spending for other industries, such as the service sector and the government sector. Another possible area for future research is to explore other internal factors that may influence the value of information and communication technology. Further research could test how firm characteristics affect other non-financial performance variables such as Customer Satisfaction Index (CSI) or financial performance variables such as ROA or EVA.
CONCLUSIONS
Analysis is conducted on data which include over 3,000 observations on labour productivity, telecommunication spending, and firm characteristics at the firm level for 1998-2000. Findings support theoretical arguments (Barney, 1991; Mata, Fuerst, Barney, 1995) that IT can yield positive performance effects. Empirical evidence reveals that the value of telecommunication spending is contingent upon firm characteristics such as industry type.
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Chunhui Liu
Grace O'Farrell
University of Winnipeg
Chunhui Liu is an Associate Professor in the Department of Business and Administration at the University of Winnipeg. Her current research interests include international accounting harmonization, accounting information systems, emerging markets, ecommerce, and computer-user interface. Dr. Liu has been published in Decision Support Systems, Information & Management, Journal of Accounting, Auditing, and Finance, International Journal of Accounting Information Systems, Journal of Accounting and Public Policy, International Journal of Human Computer Studies, Electronic Markets, and Issues in Accounting Education. She has presented papers in these fields in numerous academic conferences including the IABPAD conference.
Grace O'Farrell is an Associate Professor in the Department of Business and Administration at the University of Winnipeg. Her current research interests include international accounting harmonization, cost/benefit analysis of employee benefit programs, and person-organization fit. She has published in Decision Support Systems; Ivey Business Journal; International Journal of Business, Accounting and Finance; HR Professional Magazine; and, the Journal of Drug Issues. She has presented papers at a multitude of academic conferences including the IABPAD, ASAC, and Western Academy of Management conferences. She is the correspondence author for this manuscript.
Table 1
Results of Model Testing
Labour productivity = [alpha] + [[beta].sub.1] Telecom +
[[beta].sub.2] Size + [[beta].sub.3] Industry + [[beta].sub.4]
Telecom * Size + [[beta].sub.5] Telecom * Industry + [epsilon]
Expected Parameter Estimates
Independent and Control Variables Sign (Standard Error)
10.015
Intercept None (7.712)
53.617
Telecom + (23.364) *
0.230
Size + (0.182)
6.042
Industry None (5.831)
0.526
Telecom * Size None (1.505)
-56 819
Telecom * Industry - (22.931) *
F-statistic 3.26 **
* denotes p<0.05 and ** denotes p<0.01
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| Author: | Liu, Chunhui; O'Farrell, Grace |
|---|---|
| Publication: | International Journal of Business Research and Information Technology |
| Geographic Code: | 1USA |
| Date: | Sep 22, 2014 |
| Words: | 3530 |
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