Abstract:We investigate the predictive power of operational performance on future financial distress in the context of the US airline industry. We focus on four areas of operational performance: revenue management, operational efficiency, service quality, and operational complexity. Using quarterly data from 1988 through 2013, we find that airlines that have inferior revenue management, lower aircraft utilization, and higher operational complexity face higher future financial distress. Interestingly, average service qu… Show more
“…Operational Efficiency. Operational efficiency is the relative efficiency of a firm in its ability to convert organizational resources into business outcomes in comparison with its industry peers (Peng et al, 2008;Alan and Lapré, 2018;Chuang et al, 2019). We use the stochastic frontier estimation (SFE) methodology to measure a firm's operational efficiency in transforming its resources such as the number of employees (EMP), capital expenditure (CEX), and inventory (INV) into its operating income (OI), and measure the efficiency of each firm relative to its competitors in the same industry (Dutta et al, 2005;Li et al, 2010).…”
Although much research has been carried out to examine various contextual issues and moderating factors for successful R&D investments, very little research has been conducted to explore the role of a firm's operational and process characteristics. In this study, we explore how firms could possibly enhance the financial returns of R&D investments through quality management, using Six Sigma implementation as an example, and efficiency improvement, using the stochastic frontier estimation of relative efficiency as a proxy. Based on data from 468 manufacturing firms in the U.S. over the period 2007-2014, we construct a dynamic panel data model to capture the effects of R&D investments on firms' financial returns in terms of Tobin's q. Using the system generalized method of moments estimator, our results indicate that the financial returns of R&D investments are significantly enhanced when firms adopt Six Sigma and improve efficiency in operations. Our additional analyses further suggest that such an enhancement effect through quality and efficiency improvements is more pronounced under high operational complexity as approximated by labor intensity and geographical diversity. Instead of considering innovation activities and process management as contradictory functions, we show that quality and efficiency improvements indeed support firms' R&D investments, leading to higher financial returns.
“…Operational Efficiency. Operational efficiency is the relative efficiency of a firm in its ability to convert organizational resources into business outcomes in comparison with its industry peers (Peng et al, 2008;Alan and Lapré, 2018;Chuang et al, 2019). We use the stochastic frontier estimation (SFE) methodology to measure a firm's operational efficiency in transforming its resources such as the number of employees (EMP), capital expenditure (CEX), and inventory (INV) into its operating income (OI), and measure the efficiency of each firm relative to its competitors in the same industry (Dutta et al, 2005;Li et al, 2010).…”
Although much research has been carried out to examine various contextual issues and moderating factors for successful R&D investments, very little research has been conducted to explore the role of a firm's operational and process characteristics. In this study, we explore how firms could possibly enhance the financial returns of R&D investments through quality management, using Six Sigma implementation as an example, and efficiency improvement, using the stochastic frontier estimation of relative efficiency as a proxy. Based on data from 468 manufacturing firms in the U.S. over the period 2007-2014, we construct a dynamic panel data model to capture the effects of R&D investments on firms' financial returns in terms of Tobin's q. Using the system generalized method of moments estimator, our results indicate that the financial returns of R&D investments are significantly enhanced when firms adopt Six Sigma and improve efficiency in operations. Our additional analyses further suggest that such an enhancement effect through quality and efficiency improvements is more pronounced under high operational complexity as approximated by labor intensity and geographical diversity. Instead of considering innovation activities and process management as contradictory functions, we show that quality and efficiency improvements indeed support firms' R&D investments, leading to higher financial returns.
“…These findings are supported by Jacobs et al [47], who showed that more productive firms can experience less cost and more profits, and better performance in a lower bankruptcy risk and larger market value. In addition to the manufacturing industry mentioned above, the transportation industry was another research area in the previous literature [48,49]. For instance, fleet utilization and the fuel efficiency rate were used as cost-based efficiency measures and replace the roles of costs and waste in the manufacturing industry [48].…”
Section: Efficiency and Financial Performancementioning
confidence: 99%
“…In addition to the manufacturing industry mentioned above, the transportation industry was another research area in the previous literature [48,49]. For instance, fleet utilization and the fuel efficiency rate were used as cost-based efficiency measures and replace the roles of costs and waste in the manufacturing industry [48]. As reducing unnecessary costs aims to provide goods and services with minimum waste and maximum utilization of resources, we propose the following hypothesis:…”
Section: Efficiency and Financial Performancementioning
Quality management practices have become increasingly important as firms seek to obtain quality certifications to dominate markets. To date, adequate research evaluating the effects of quality management is lacking. In this work, we used Chinese quality awards to evaluate a firm's quality level. A PSM-DiD (propensity score matching and difference-in-difference) model describing the relationship between quality award effects and financial benefits in terms of return on assets was developed. We further used a hierarchical regression to examine the influence of operational performance on financial benefits. The results show that quality awards cannot assure their winners a higher return on asset. Indicators of operating performance, such as less lead time and higher inventory turnover, can significantly enhance firms' profitability. The moderating effects of operational performance suggest that firms may focus on how to translate quality management practices into business improvement. This study also contributes to the operation management literature by describing how firms need outstanding financial performance for sustainable development and continuous improvement.Sustainability 2020, 12, 1966 2 of 23 for Just-in-Time (JIT) and Six Sigma, respectively, their products still suffer from serious quality-related problems [7].In addition, operational performance is considered to play an important role in quality management practices. Operational performance is defined as the strategic dimensions of competing firms and consists of operational level indicators, such as flexibility and delivery [8]. The results of quality management systems are also shown in operational levels. However, few studies have explored the effects of operational performance on financial benefits. In addition, researchers may merely consider one dimension of operational performance, such as efficiency [9] or inventory [10]. These studies failed to provide a complete description of the influence of operational performance on financial benefits.This study aims to fill the above research gaps by answering the following two questions: (1) How do quality management practices affect firms' financial benefits? (2) What is the relationship between operational performance and financial benefits?We use quality award winners as the research samples and examine whether these firms can obtain a higher return on assets than their counterparts. The results show that the awarded firms could not achieve significant improvement and that the effects of the awards are not obvious. In addition, we explore the impact of operational performance on the return on assets. The findings demonstrate that less lead time and a higher inventory turnover can enhance the financial benefits. Additionally, decreasing the operating costs and tracking sales more closely can improve the return on assets. Besides this, we also find that shorter lead time and larger inventory turnover can positively moderate the impact of quality awards on the ROA.This study contributes to previous rese...
“…Predicting binary events is a deeply researched topic in finance, for instance to predict bankruptcy or financial distress (Sarkar andSriram 2001, Alan andLapré 2018), but now it is also becoming more common in other areas, including supply chain management. In fact, predicting unauthorized subcontracting was the original motivation of the middleman M. that sparked this research project.…”
Unauthorized subcontracting—when suppliers outsource part of their production to a third party without the retailer’s consent—has been common practice in the apparel industry and is often tied to noncompliant working conditions. Because retailers are unaware of the third party, the production process becomes obscure and cannot be tracked. In this paper, we present an empirical study of the factors that can lead suppliers to engage in unauthorized subcontracting. We use data provided by a global supply chain manager with more than 30,000 orders, of which 36% were subcontracted without authorization. We find that the frequency of unauthorized subcontracting across factories has a pronounced bimodal distribution. Moreover, the degree of unauthorized subcontracting in the past is highly related to the probability of engaging in unauthorized subcontracting in the future, which suggests that factories behave as if they choose a strategic level of unauthorized subcontracting. At the order level, we find that state dependence (i.e., the status of an order carrying over to the next one) and price pressure are the key drivers of unauthorized subcontracting. Buyer reputation and lead time also play a role. Finally, we show that unauthorized subcontracting can be predicted correctly for more than 80% of the orders in out-of-sample tests and for about 70% of suppliers. This indicates that retailers can use business analytics to predict unauthorized subcontracting and help prevent it. This paper was accepted by Vishal Gaur, operations management.
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