How does firms’ integrated market power affect upstream trade credit and institutional ownership? Evidence from Taiwan

Asia Pacific Management Review xxx (xxxx) xxx

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How does firms’ integrated market power affect upstream trade credit and institutional ownership? Evidence from Taiwan Shih-Sian (Sherwin) Jhang a, *, Winston T. Lin b, I-Hsuan Fang c a

Department of Finance, College of Management, National Sun Yat-sen University, Kaohsiung City, 80424, Taiwan School of Management, University at Buffalo-SUNY, NY, 14260-4000, USA c Department of Business Management, College of Management, National Sun Yat-sen University, Kaohsiung City, 80424, Taiwan b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 3 February 2019 Received in revised form 7 July 2019 Accepted 2 August 2019 Available online xxx

This paper investigates how a firm's integrated market power affects upstream trade credit and institutional ownership in emerging markets. Using data from the Taiwan Economic Journal (TEJ) for the period 1996e2017, and combining factor analysis as well as a variety of regression models, we show that firms' integrated market power (IMP) is negatively correlated to the use of upstream trade credit. We also document that highly integrated market power firms can attract not only foreign institutional investors but also domestic institutional investors. Furthermore, the effect of market power is consistent across the electronic and traditional sectors; however, the impact of market power is larger in the electronic sector than in the traditional sector. © 2019 College of Management, National Cheng Kung University. Production and hosting by Elsevier Taiwan LLC. All rights reserved.

Keywords: Market power Upstream trade credit Institutional ownership Factor analysis

1. Introduction Market power is an essential driver of firms' decision-making processes. A variety of studies show that a firm's market power influences operations and financial configurations of companies in developed countries, such as demand uncertainty, financial leverage, cash management, earnings management, and payout policy (Datta, Iskandar-Datta, & Singh, 2013; Grullon & Michaely, 2007; Jhang, Ogden, & Suresh, 2019). However, there is little evidence on the role of market power outside the US. Since developed countries and emerging markets have different economic development status, the market power may play different roles on firms' decisions. Recognizing the limits in the extant literature, the present paper aims to shed new light on the relationship between firms' integrated market power and upstream trade credit, and between firms' integrated market power and institutional ownership in emerging markets. This study first investigates the influence of firms’ market power on trade credit because it is the largest source of working capital in

* Corresponding author. E-mail addresses: [email protected] (S.-S. Jhang), mgtfewtl@buffalo. edu (W.T. Lin), [email protected] (I.-H. Fang). Peer review under responsibility of College of Management, National Cheng Kung University.

firms and is an important tool of supply chain financing (Lee & Rhee, 2011). We focus on upstream trade credit, a kind of delayed payment offered by suppliers, for discussion. The extant literature already proposes several determinants of trade credit (Dass, Kale, & Nanda, 2015; Fabbri & Klapper, 2016; Giannetti, Burkart, & Ellingsen, 2011; Molina & Preve, 2012; Ng, Smith, & Smith, 1999; Petersen & Rajan, 1997; Yang & Birge, 2018). However, the effects of market power on upstream trade credit are still in debate and unsettled (Dass et al., 2015; Fabbri & Klapper, 2016; Fisman & Raturi, 2004; Giannetti et al., 2011). As such, we begin this paper by examining whether market power affects the use of upstream trade credit. Next, institutional investors gradually play an essential role in Taiwan capital market. The holdings of institutional investors account for around 28% of outstanding shares in 1996 but gradually increase to more than 40% in recent years. Current studies have already demonstrated the drivers of institutional ownership (Bena, Ferreira, Matos, & Pires, 2017; Bushee & Noe, 2000; Chung & Zhang, 2011; Ferreira & Matos, 2008). But how market power impacts institutional investors’ willingness to hold stocks is an issue that has been overlooked. Therefore, our second research question is to investigate whether the change of market power influences decisions made by institutional investors. A two-step procedure is utilized in this study wherein, in the first step, we use factor analysis to retrieve an integrated market

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Please cite this article as: Jhang, S.-S et al., How does firms’ integrated market power affect upstream trade credit and institutional ownership? Evidence from Taiwan, Asia Pacific Management Review, https://doi.org/10.1016/j.apmrv.2019.08.001

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power from the perspective of market competition and integration ability. Then, combining different regression analysis (Newey-West standard error correction, Fama-Macbeth regression, and fixed effect models; see Fama & MacBeth, 1973; Greene, 2003; Newey & West, 1986), we test our two research questions via using data on publicly-traded Taiwanese firms taken from the Taiwan Economic Journal (TEJ) database. The sample period ranges from 1996 to 2017. Our final dataset includes 20,556 firm-year observations, representing 1850 distinct firms. Combining correlation analysis, time series analysis, and regression analysis, we first demonstrate that firms with high (low) integrated market power use relatively low (high) trade credit from suppliers: a finding that favors the information asymmetry hypothesis rather than the exploit hypothesis of the cash conversion cycle. We also find that a firm's market power plays a positive and significant role in the investment decisions of institutional investors, and that these relationships are not otherwise substantially explained by a set of control variables. The posthoc analysis shows that firms increasing market power are able to attract both foreign institutional investors and domestic institutional investors. Lastly, although the effect of market power is consistent across the electronic and traditional sector, the impact of market power is larger in the electronic sector than in the traditional sector. This study contributes to the extant literature in several ways. First, it offers a new perspective on the literature of market power in emerging markets, in which our market power proxy is retrieved from the outcome of industry competition, operations, and finance simultaneously rather than from one single perspective. Second, extending the literature of upstream trade credit, we empirically demonstrate that the negative relationship between market power and accounts payable is driven not only from demand uncertainty but also from the existence of information asymmetry or moral hazard. Third, we complement the literature of institutional investors by showing that a firm's market power or integration ability may also play an essential role in affecting institutional investors' decisions of holding shares. Lastly, our post-hoc analysis explores different types of institutional investors and sector categories as boundary conditions, which gives a more fine-grained understanding of the relationship between integrated market power, supply chain financing, and institutional ownership. The paper is organized as follows: Section 2 reviews the extant literature. Section 3 presents the data and variables. Section 4 exhibits the main empirical results. Section 5 provides theoretical and practical perspectives, and Section 6 provides conclusions, limitations, and future research. 2. Literature review and hypotheses development 2.1. Upstream trade credit A number of studies have investigated the relationship between market power and upstream trade credit. Drawing on data from a survey of small businesses in the US, Petersen and Rajan (1997) find that there exists a negative relationship between upstream/ downstream trade credit and gross margin, a common market power measure (i.e., Lerner Index, Lerner, 1934). Using the same dataset, Giannetti et al. (2011) argue that large firms receive more purchase discounts from suppliers than smaller firms. Using the Compustat database, Dass et al. (2015) find that supplier-industries’ market power can negatively affect a firm's upstream trade credit. However, most evidence discussed above is from developed countries rather than emerging markets. To our knowledge, only a few papers explore how market power affects trade credit in emerging markets or developing countries. For example, using surveys conducted by Chinese Academy of

Social Science in 2000, Ge and Qiu (2007) find that firm size, a measure of market power, is negatively related to the use of upstream trade credit. In addition, using 2003 World Bank Enterprise Survey for Chinese firms, Fabbri and Klapper (2016) measure a firm's power by the percentage of purchase that accounts for sellers' total sales. They argue that suppliers with weak power tend to offer trade credit and better terms to their customers, since their customers are in a better position to ask more delayed payments. However, they conduct their research from a supplier's perspective, and focus on downstream trade credit (i.e., accounts receivable) rather than upstream trade credit (i.e., accounts payable). Overall, these empirical studies gauge the impact of market power on upstream trade credit from a single dimension, such as gross margins, product characteristics, firm size, or percentage of purchase on sales (Dass et al., 2015; Fabbri & Klapper, 2016; Giannetti et al., 2011; Petersen & Rajan, 1997). Few studies consider the effects of integrated market power stemming from industry competition, operational, and financial factors simultaneously on supplier trade credit, except for Jhang et al. (2019). In this study, we contribute to this small yet growing body of research by using data from Taiwan. 2.2. Institutional ownership The extant literature shows that the investment decisions of institutional investors may depend on a set of firm-specific variables, including corporate governance, size, and accounting information disclosure. For example, firms with good corporate governance can attract institutional investors (Chung & Zhang, 2011; Ferreira & Matos, 2008; McKinsey & Company, 2002). In addition, institutional investors invest more heavily in firms with better corporate disclosure practices (Bushee & Noe, 2000), and firms that are larger, pay cash dividends, or engage in stock repurchase (Ferreira & Matos, 2008; Gompers & Metrick, 2001; Grinstein & Michaely, 2005). However, studies on institutional ownership do not address whether increased market power or engagement in supply chain integration are more likely to attract funds from institutional investors. As such, our study will complement this body of research by empirically testing the association between integrated market power and institutional ownership. 2.3. The relationship between upstream trade credit and market power Jhang et al. (2019) argue that a firm's market power is negatively related to trade credit, based on the demand uncertainty hypothesis. Here, we strengthen this relationship by providing another argument about the existence of moral hazard and information asymmetry in emerging markets or developing countries. Specifically, manufacturing companies in developed countries often outsource their work overseas to reduce their in-house production cost, or ask suppliers (i.e., tier-1, tier-2 suppliers, and so on) located in emerging markets or developing countries to provide relatively cheap inputs (Aksin & Masini, 2008; Handley & Benton, 2013; Jiang, Belohlav, & Young, 2007; Kroes & Ghosh, 2010; Yang, Wacker, & Sheu, 2012). The upstream supply network, including suppliers and their upstream suppliers, may pay less attention to their products. They believe that if there is a problem with their product quality, the problem will take a longer time to be found since these firms are located at the relatively up levels of supply chain (Gofman, 2013; Kim & Shin, 2012). As such, the supply network in emerging markets may experience relatively high moral hazard as well as the problem of information asymmetry, leading these firms to utilize relatively high trade credit to guarantee their product quality and their reputation (Long, Malitz, & Ravid, 1993, pp. 117e127; Ng et al.,

Please cite this article as: Jhang, S.-S et al., How does firms’ integrated market power affect upstream trade credit and institutional ownership? Evidence from Taiwan, Asia Pacific Management Review, https://doi.org/10.1016/j.apmrv.2019.08.001

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1999; Smith, 1987). Extant studies document that to increase a firm's market power, a company needs to engage in collaborative activities across firms, known as external integration (Braunscheidel & Suresh, 2009; Danese, 2013; Flynn, Huo, & Zhao, 2010; Frohlich & Westbrook, 2001, 2002; Ma, Zhang, Dai, & Hu, 2016; Mitra & Singhal, 2008; Narasimhan & Kim, 2002; Prajogo & Olhager, 2012). Firms with supply chain integration frequently communicate and share information with suppliers and customers via several practices, such as the collaborative planning, forecasting, and replenishment (CPFR) system (e.g., Ak & Patatoukas, 2016; Cao & Zhang, 2011; Ren, Cohen, Ho, & Terwiesch, 2010; Terwiesch, Ren, Ho, & Cohen, 2005). By integrating information flow and increasing disclosure with parties, coordination activities can effectively reduce the production and procurement lead time, weaken the bullwhip effect across the supply chain (Frohlich & Westbrook, 2002; Lee, Padmanabhan, & Whang, 1997), or even mitigate the problems of information asymmetry and moral hazard. As such, supply chain integration (i.e., the increase of market power) should be enacted as a means to effectively lower the utilization of upstream trade credit, leading to a negative relationship between market power and upstream trade credit. Due to the above arguments above, we build our first hypothesis: Hypothesis 1. Firms with high market power will have relatively low upstream trade credit, and vice versa. In other words, there exists a negative relationship between market power and upstream trade credit.

2.4. The relationship between institutional ownership and market power Increasing market power through integration activities can reduce waste, prevent the use of financial leverage, and increase market evaluation (Jhang et al., 2019). In addition, firms engaging in higher integration activities between customers and suppliers can balance supply and demand and reduce demand uncertainty or production lead time, leading to relatively high operational and financial performance (Frohlich & Westbrook, 2002; Lee et al., 1997; Narasimhan & Kim, 2002). The literature already documents the existence of a positive relationship between institutional investors and firm performance (Cornett, Marcus, Saunders, & Tehranian, 2007; Yuan, Xiao, & Zou, 2008). As such, the supply chain integration (i.e., the increase of market power) should act as a means to attract institutional investors. In this regard, we conjecture that integration ability should be positively related to institutional ownership, and we propose the second hypothesis as follows: Hypothesis 2. Firms with high market power will have relatively high institutional ownership, and vice versa. That is, a positive relationship may exist between market power and institutional ownership.

3. Data, model variables, and research methods

Fahlenbrach & Stulz, 2011; Jhang et al., 2019). We perform this winsorized approach for each fiscal year. We focus on the firms that have a stock price greater than five NTD dollars. Also, following extant research, we include only firms that have an asset turnover rate of at least 25% to exclude extremely inefficient firms (Jhang et al., 2019). Our final sample consists of 20,556 firm-year observations for 1850 distinct firms. 3.2. Model variables 3.2.1. Market power proxy Extant literature utilizes several single proxies to gauge market power, such as a firm's ability to control pricing, firm size, market shares, barriers to entry, market concentration or Lerner Index (Aghion, Van Reenen, & Zingales, 2013; Dass et al., 2015; Datta et al., 2013; Elzinga & Mills, 2011; Fu, Lin, & Molyneux, 2014; Lerner, 1934; Stavins, 2001). In contrast to the extant literature, our definition of market power is based on two dimensions: the market competition of a firm and the extent of internal and external integration activities (Jhang et al., 2019). We use the “integrated” market power proxy because it is more comprehensive and includes different dimensions of economic, operations, and supply chain management, as well as financial management. As to the dimension of market competition, firms with low market power tend to stay in a quasi-competitive environment. They are price takers and have relatively low profit margins in their industry. To maximize the returns on assets (ROA), these firms need to increase their use of assets to create more sales. In contrast, high market power firms are price setters and have relatively high profit margins. Furthermore, they can maximize ROA via using relatively low asset turnover (Besanko, Dranove, Shanley, & Schaefer, 2013; Bodie, Kane, & Marcus, 2013). In terms of internal and external integration activities, high market power firms have relatively low inventory. By adopting enterprise resource planning (ERP) processes, CPFR, lean manufacturing, and just-in-time production, these firms can build cross-functional teams and share information with customers and suppliers so as to reduce their inventory level (Flynn et al., 2010; Prajogo & Olhager, 2012; Yao, Kohli, Sherer, & Cederlund, 2013; Yin, Cheng, Cheng, Wang, & Wu, 2016). Furthermore, high market power and integrated firms can keep more resources on hand by reducing waste and making use of cash reserves to gain market share at the expense of industry rivals (Fresard, 2010). In sum, we argue that market power is an aggregated construct stemming from the outcome of profit margin, asset turnover, inventory, and cash. 3.2.2. Main model variables The main model variables we measure market power include profit margins (PM), assets turnover (ATTO), inventory (INV), and cash (CASH). We define these variables as follows:

PMi;t ¼

NSi;t  CGSBi;t  AEi;t NSi;t

INVi;t ¼

(1)

NSi;t TAi;t

(2)

INVTi;t TAi;t

(3)

CEi;t TAi;t

(4)

ATTOi;t ¼

3.1. Data To analyze the role of market power in emerging markets, we utilize data on publicly traded Taiwanese firms taken from the Taiwan Economic Journal (TEJ) database. Our sample include all firms for the years 1996e2017 for the era of completed institutional ownership. To mitigate the influence of outliers, we winsorize the data at the 5% and 95% intervals (Doidge, Karolyi, & Stulz, 2013;

3

CASHi;t ¼

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where NSi,t, CGSBi,t, and AEi,t are net sales, cost of goods sold, and selling, general and administrative expense, respectively, for firm i in year t. INVTi,t is total inventories while CEi,t encompasses both cash on hand and cash equivalent. TAi,t is firm i's total assets at the end of year t. We scale these variables by total assets rather than net sales so that these measures can gauge the burden on the focal firm's asset base. Also, INVTi,t, and CEi,t are variables in the balance sheet, and we believe that it would be suitable to utilize a proxy related to balance sheets (i.e., total assets) instead of a proxy of the income statement (i.e., net sales) to be the deflator. (See also Bates, Kahle, & Stulz, 2009; Foley, Hartzell, Titman, & Twite, 2007; Wu, Firth, & Rui, 2014) The next two variables are the main regressands of our regression model: accounts payable (AP) and institutional ownership (INST). Following the literature (Ferreira & Matos, 2008; Gompers & Metrick, 2001; Jhang et al., 2019), we calculate them respectively as:

APi;t ¼

ANPi;t TAi;t

INSTi;t ¼

(5)

Institutional ownershipi;t ðInstitutional þ IndividualÞownershipi;t

(6)

where ANPi,t is firm i's accounts payable at the end of year t. We define institutional ownership (INSTi,t) as the sum of the shares holdings of all institutions in a firm's stock scaled by its total market capitalization at the end of each fiscal year.

3.2.3. Control variables To gauge the potential drivers that affect regressands of models, we control demand uncertainty in AP equation, while control Tobin's Q and financial leverage in INST equation. We calculate these variables as follows:

sðATTOÞi;t ¼

8
Tobin’s Qi;t ¼

t4



ATTOi;t

91=2 2 =  E ATTOi;t : ; 

TAi;t  BVi;t þ MVi;t TAi;t

LEVERAGEi;t ¼

DEBTi;t TAi;t  BVi;t þ MVi;t

R&DExpi;t SALESi;t

(10)

TAi;t GDPdeflatort

(11)

R&Di;t ¼

SIZEi;t ¼

TANGi;t ¼

PPEi;t TAi;t

(12)

where R&DExpi,t is firm i's year t R&D expenditures scaled by total sales. PPEi,t is firm i's property, plant, and equipment. We scale R&D expenditures by total sales in that some firms with high market power must engage in R&D spending to secure future sales (Flannery & Rangan, 2006). The firm size, SIZEi,t, calculated as the natural log of firm i's total assets, is scaled by GDP deflators. We hypothesize that there exists a positive (negative) relationship between SIZE and INST (AP) because larger firms are more creditworthy and have fewer information asymmetry problems. Therefore, large firms can attract more institutional investors to invest and utilize less trade credit. Consistent with this viewpoint, Gompers and Metrick (2001) document that institutional investors tend to invest in large firms. Lastly, we control tangibility, TANGi,t, since tangibility will be positively associated with size and profitability, and thus market power (Ogden, Jen, & O'Connor, 2003; Williams, 1995). Giannetti et al. (2011) find that accounts payable is unrelated to tangibility, while Dass et al. (2015) document that accounts payable is positively associated with tangibility. Next, it is unclear for the relationship between tangibility and institutional ownership. On one hand, firms with high tangibility may primarily focus on physical investment and create advantages of economies of scale in production, which may attract institutional investors. However, these high capital intensity firms may participate in few innovation activities, leading to a negative relationship between TANG and INST. 3.3. Research methods

(7)

(8)

(9)

where BVi,t is firm i's book value of equity, MVi,t is firm i's market value of equity, and DEBTi,t is firm i's long-term and short-term debt. We measure demand uncertainty (s(ATTO)) based on the standard deviation of asset turnover over the past five years. Firms with high (low) demand uncertainty tend to have relatively high (low) accounts payable based on transactional motives (Jhang et al., 2019). We expect to observe a positive relationship between Tobin's Q and INST since institutional investors are more likely to purchase high growth opportunity stocks. In contrast, firms with high LEVERAGE may encounter financial risk, which may not attract institutional investors (Kang & Stulz, 1997). As such, we predict a negative relationship between LEVERAGE and INST. Lastly, we also control R&D, firm size, and tangibility in calculating the drivers of both accounts payable and institutional ownership, using the following equations:

Conducting formal tests of our model via OLS regression analysis is problematic because of the issue of causality. In this study, we argue that market power is an aggregated construct stemming from PM, ATTO, INV, and CASH. The problem of causality is also manifest across previous empirical studies. On one hand, the extant literature uses profitability as a regressor to explain variations in inventory (Ak & Patatoukas, 2016; Alan, Gao, & Gaur, 2014; Gaur, Fisher, & Raman, 2005; Rumyantsev & Netessine, 2007). On the other hand, another stream of research uses inventory as a regressor to explain variations in financial performance or credit ratings (Bendig, Strese, & Brettel, 2017; Chen, Frank, & Wu, 2005). As such, we adopt the following two-step testing procedure. In the first step, we use factor analysis to determine whether: (a) a strong common factor exists among our principal four variables (i.e., PM, ATTO, INV, and CASH) based on the tenets of managerial economics, operations management, and financial management; and (b) the common factor is a measure of market power consistent with our conjecture. Following that, we regress our main model variables (AP and INST) on this latent variable (as well as other control variables) to examine whether our market power proxy can explain variations in upstream trade credit and institutional ownership. This two-step approach also helps us to test both hypotheses 1 and 2. Other researchers have employed a similar two-step procedure in related contexts (Fullerton, McWatters, & Fawson, 2003; Jhang et al., 2019; Shervani, Frazier, & Challagalla, 2007). Furthermore, we utilize AP and INST at year t and year tþ1 as regressands in

Please cite this article as: Jhang, S.-S et al., How does firms’ integrated market power affect upstream trade credit and institutional ownership? Evidence from Taiwan, Asia Pacific Management Review, https://doi.org/10.1016/j.apmrv.2019.08.001

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subsequent regressions to investigate whether market power can drive these firm-specific variables. In terms of the regression models of panel data analysis, we also report the results based on the Fama-Macbeth procedure and the Newly-West estimation (GMM) (Fama & MacBeth, 1973; Newey & West, 1986; Petersen, 2009; Thompson, 2011). Furthermore, we utilize the Hausman test (Durbin, 1954; Hausman, 1978; Wu, 1973) to examine whether a random-effect or a fixed-effect model should be adopted. If results indicate a fixed effect model is preferred, we analyze the data using fixed-effect models and control for year and firm fixed effects (Aggarwal, Erel, Ferreira, & Matos, 2011; Chung & Zhang, 2011; Fresard, 2010). 4. Main empirical analysis 4.1. Summary statistics In this section, we summarize the model variables in Table 1. We find that for most of our variables, the mean values are comparable to values reported in extant studies. For example, our mean value of 0.213 for PM is very close to the corresponding mean value of 0.223, reported by Huang, Shi, and Zhang (2011) for Chinese listed firms over the years 1998e2006. Additionally, the mean value of AP in Taiwanese firms is around 11.11%, which is slightly higher than the mean value of 7.55% for their Chinese counterparts. Furthermore, we find that the mean value of our sample firms’ institutional ownership is 35.7%, which is lower than the mean of 43% for all countries in the world, but still higher than the 14% mean in all Asia Pacific countries reported by Bena et al. (2017) over the 2001e2010 period. 4.2. Time trends in model variables It is important to examine time trends for model variables since extant studies have documented substantial trends in several model variables, such as INV and CASH. Specifically, due to the lean manufacturing and just-in-time practices in the US, Chen et al. (2005) document substantial reductions in inventory balances over time for US manufacturers between 1981 and 2000. Jhang (2016) demonstrates similar results from the period before 2000; however, he finds that the inventory trend increases, even controlling for market volatility, after 2000. Bates et al. (2009) and Itzkowitz (2013) exhibit substantial increases in corporate cash

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holdings for the period of 1980e2006 and 1979e2006, respectively. Another important reason to investigate the time trend of variables is that if the variables exhibit the characteristics of time trends, we need to take suitable statistical methods to cope with the empirical panel data. In untabulated results, we find that over the 1996e2017 period, the mean value of firms' profit margins increases from 17.41% to 23.67% during the sample period, approximately representing a 36% increase. Besides, the mean value of a firm's inventory balance declines from 17.07% to 13.82% (around a 19% decrease), while the mean value of cash balance increases from 7.37% to 19.70% (around 167% increase), which is similar to extant studies. Interestingly, even though the mean value of assets turnover increases from 76.88% to 83.57% during the sample period (around a 8.7% increase), this value increases prior to 2007, but loses momentum after 2007. The mean value of firms' institutional ownership increases from 27.78% in 1996 to 41.56% in 2017, suggesting that institutional investors gradually increase the weight of investment in Taiwan. In contrast to findings in the US, we find that the mean value of firms' accounts payable also increases from 6.96% in 1996 to 10.71% in 2017, indicating Taiwanese firms rely more on upstream trade credit for supply chain financing in recent years. 4.3. Correlations among model variables We examine pair-wise correlations among the main model variables and present the results in Table 2. Take the four variables in factor analysis for example. The correlation of traditional Lerner Index, PM, with CASH, ATTO, and INV are 0.334, 0.283, and 0.147, respectively. The signs are all consistent with extant studies of developed countries (Jhang et al., 2019). Furthermore, the PM is negatively related to AP but positively associated with INST, with the correlation coefficients equaling 0.289 and 0.065, respectively. Since the observed correlations for main variables are generally consistent with our predictions, the correlation evidence in Table 2 provides strong preliminary support for our hypotheses. 4.4. Factor analysis results To make sure our four variables (PM, ATTO, INV, and CASH) have the same signs and explain the factor score easily, we first denote ATTO_N and INV_N to equal ATTO and INV multiplied by 1, respectively. Next, we apply the principal components analysis for

Table 1 Presents descriptive statistics of our model variables. PM, ATTO, INV, and CASH are profit margins, assets turnover, inventory to total assets ratio, and cash and cash equivalent to total assets ratio, respectively. AP is accounts payable to total assets ratio. INST is the sum of the shares holdings of all institutions in a firm's stock scaled by its total market capitalization. s (ATTO) is demand uncertainty by calculating the standard deviation of sales scaled by total assets over the past five years. R&D is calculated as research and development expense scaled by total sales. LEVERAGE is the ratio of total debt to the market value of total asset. Tobin's Q is the market to book value of assets. SIZE is firm size, calculated as the natural log of firm's total assets. TANG is the property, plant, and equipment to total assets ratio. Summary statistics. Variable Model variables PM ATTO INV CASH AP INST s (ATO) R&D LEVERAGE Tobin's Q SIZE TANG N ¼ 20,556.

Mean

Std. Dev.

Minimum

Lower Quartile

Median

Upper Quartile

Maximum

0.213 0.904 0.145 0.156 0.111 0.357 0.186 0.028 0.097 1.486 15.314 0.288

0.133 0.451 0.097 0.119 0.083 0.220 0.157 0.038 0.095 0.765 1.401 0.169

0.012 0.289 0.003 0.005 0.009 0.000 0.025 0.000 0.000 0.765 9.795 0.014

0.112 0.567 0.073 0.062 0.045 0.173 0.076 0.000 0.018 1.010 14.340 0.151

0.188 0.806 0.130 0.126 0.088 0.328 0.135 0.014 0.065 1.234 15.134 0.273

0.293 1.127 0.200 0.221 0.158 0.524 0.235 0.038 0.152 1.659 16.071 0.414

0.543 2.429 0.557 0.493 0.364 0.812 0.883 0.168 0.391 7.168 21.949 0.688

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Table 2 Exhibits Pearson correlations among model variables, where PM, ATTO, INV, and CASH are profit margins, assets turnover, inventory to total assets ratio, and cash and cash equivalent to total assets ratio, respectively. AP is accounts payable to total assets ratio. INST is the sum of the shares holdings of all institutions in a firm's stock scaled by its total market capitalization. s (ATTO) is demand uncertainty by calculating the standard deviation of sales scaled by total assets over the past five years. R&D is calculated as research and development expense scaled by total sales. LEVERAGE is the ratio of total debt to the market value of total asset. Tobin's Q is the market to book value of assets. SIZE is firm size, calculated as the natural log of firm's total assets. TANG is the property, plant, and equipment to total assets ratio. Pearson correlations among model variables

PM ATTO INV CASH AP INST

s(ATTO) R&D LEVERAGE Tobin's Q SIZE TANG

Panel corr. (p value) Panel corr. (p value) Panel corr. (p value) Panel corr. (p value) Panel corr. (p value) Panel corr. (p value) Panel corr. (p value) Panel corr. (p value) Panel corr. (p value) Panel corr. (p value) Panel corr. (p value) Panel corr. (p value)

PM

ATTO

INV

CASH

AP

INST

s(ATTO)

R&D

LEVERAGE

Tobin's Q

SIZE

TANG

1.000

0.283*** (<.0001) 1.000

0.147*** (<.0001) 0.306*** (<.0001) 1.000

0.334*** (<.0001) 0.051*** (<.0001) 0.232*** (<.0001) 1.000

0.289*** (<.0001) 0.630*** (<.0001) 0.220*** (<.0001) 0.036*** (<.0001) 1.000

0.065*** (<.0001) 0.024*** (<.0001) 0.123*** (<.0001) 0.053*** (<.0001) 0.030*** (<.0001) 1.000

0.175*** (<.0001) 0.450*** (<.0001) 0.144*** (<.0001) 0.012* (0.0831) 0.281*** (<.0001) 0.024*** (0.0006) 1.000

0.430*** (<.0001) 0.170*** (<.0001) 0.084*** (<.0001) 0.382*** (<.0001) 0.105*** (<.0001) 0.081*** (<.0001) 0.081*** (<.0001) 1.000

0.213*** (<.0001) 0.238*** (<.0001) 0.082*** (<.0001) 0.360*** (<.0001) 0.240*** (<.0001) 0.002 (0.8263) 0.123*** (<.0001) 0.233*** (<.0001) 1.000

0.207*** (<.0001) 0.034*** (<.0001) 0.066*** (<.0001) 0.184*** (<.0001) 0.052*** (<.0001) 0.083*** (<.0001) 0.063*** (<.0001) 0.177*** (<.0001) 0.368*** (<.0001) 1.000

0.173*** (<.0001) 0.015** (0.028) 0.051*** (<.0001) 0.163*** (<.0001) 0.023*** (0.0008) 0.383*** (<.0001) 0.143*** (<.0001) 0.198*** (<.0001) 0.300*** (<.0001) 0.081*** (<.0001) 1.000

0.077*** (<.0001) 0.371*** (<.0001) 0.307*** (<.0001) 0.391*** (<.0001) 0.380*** (<.0001) 0.010 (0.157) 0.228*** (<.0001) 0.177*** (<.0001) 0.439*** (<.0001) 0.076*** (<.0001) 0.147*** (<.0001) 1.000

N ¼ 20,556; ***, **, and * indicate significance at the 0.01, 0.05, and 0.10 levels, resp.

the factor procedure to the full-sample panel data for PM, ATTO_N, INV_N, and CASH. Based on the factor analysis, we can extract one major factor and one minor factor. The eigenvalue of the major factor is 1.635, suggesting that the factor can explain 40.87% of the common variance of these four inputs. On the other hand, the eigenvalue of the minor factor is 1.070, implying that this minor factor can explain 26.75% of the common variance of these four inputs. We discard the minor factor in this research and present the results of the major factor in Table 3. Denoting scores for the major factor as integrated market power (IMP), we find that IMP is strongly associated with each of the input variables: PM (0.719), ATTO_N (0.594), INV_N (0.662), and CASH (0.572). In order to allow IMP to follow a normal distribution with mean and standard deviation equaling zero and one, respectively, we also generate an equation for IMP to help the following analysis. In this regard, the value of zero of IMP in equation (13) or (14) is a cutoff point of relatively high or low integrated market power:

IMP ¼ 0:17 þ 3:30ðPMÞ þ 0:81ðATTO NÞ þ 4:19ðINVT NÞ þ 2:94ðCASHÞ

(13)

or

IMP ¼ 0:17 þ 3:30ðPMÞ  0:81ðATTOÞ  4:19ðINVTÞ þ 2:94ðCASHÞ

(14)

We also exhibit the relation between IMP and four inputs by presenting an alternative form of eigenvectors, equaling the square root of the eigenvalue times standardized scoring coefficients. The equation can be described as follows:

IMP1 ¼ 0:56ðPMÞ  0:38ðATOÞ  0:37ðINVÞ þ 0:24ðCASHÞ (15) From equations (13)e(15), we find that IMP decreases with both

Table 3 Presents results of factor analysis. Factor analysis method is principal components with varimax rotation, and is applied to the full-sample panel data, where PM and CASH are profit margins, and cash and cash equivalent to total assets ratio, respectively. We denote ATTO_N and INV_N to equal ATTO and INV multiplied by negative one, respectively. Results of factor analysis. Eigenvalue of Factor 1:

1.635

Correlations of factor scores with input variables (Factor pattern):Input variable

Corr.

p value

PM ATTO_N INV_N CASH

0.719 0.594 0.662 0.572

(<0.0001) (<0.0001) (<0.0001) (<0.0001)

Generating equation for Factor1 scores. IMP ¼ 0.17,258 þ 3.30,682*PM þ0.80,528*ATTO_N þ4.19,114*INV_N þ2.94,291*CASH. IMP ¼ 0.17,258 þ 3.30,682*PM - 0.80,528*ATTO -4.19,114*INV þ2.94,291*CASH. (N ¼ 20,556). ***, **, and * indicate significance at the 0.01, 0.05, and 0.10 levels, resp.

Please cite this article as: Jhang, S.-S et al., How does firms’ integrated market power affect upstream trade credit and institutional ownership? Evidence from Taiwan, Asia Pacific Management Review, https://doi.org/10.1016/j.apmrv.2019.08.001

S.-S. Jhang et al. / Asia Pacific Management Review xxx (xxxx) xxx

ATTO and INVT but increases with both PM and CASH. This suggests that our integrated market power proxy decreases with market competition status of firms but increases with external and internal integration. By using the results of factor analysis, in the next two sections, we further test (a) whether a firm's integrated market power is negatively related to upstream trade credit, and (b) whether a firm's integrated market power is positively related to institutional ownership. 4.5. Regressions of upstream trade credit (AP) Table 4 presents results of various regressions of AP on IMP, some of which also include the control variables s(ATTO), R&D, SIZE, and TANG. We adopt the fixed effect model since the Hausman test suggests a random-effect model should be discarded (the mstatistic of 195.18 exceeding the critical value 11.07 of the Chisquare test for the AP regression). Due to the potential for multicollinearity among the independent variables, we exhibit variance inflation factors (VIFs) in the last two columns of the table. Since all VIF values are far less than the threshold of 10, the multicollinearity problem does not exist here (Kutner, Nachtsheim, & Neter, 2004). The first column shows the results of a GMM regression of AP on IMP. The coefficient of IMP is reliably negative, suggesting a negative association between AP and integrated market power (b ¼ 0.036, p < 0.01),. In addition, the adjusted R2 is modest at 0.19. Next, the second column shows results of GMM regressions of AP on other control variables, and most results are consistent with the extant literature. For example, the coefficient of s(ATTO) increases with AP, while R&D decreases with AP. However, the SIZE is positively related to AP, indicating that firm size may play different roles in developed countries and emerging markets. Furthermore, the regression adjusted R2 is substantial at 0.21. The third column shows the results of the regression, in which the regressors include IMP as well as all control variables. Two results here are particularly noteworthy. First, the coefficient of IMP (b ¼ 0.031, p < 0.01), remains reliably negative, suggesting that our measure of market power is robust to the inclusion of the control variables, especially for s(ATTO), which bridges the negative relation between market power and accounts payable in extant studies (Jhang et al., 2019). Second, the coefficients of s(ATTO) and SIZE decreases in size, from 0.104 to 0.005 respectively in the previous regression to only 0.054 and 0.003 in the present

7

regression. This finding indicates that IMP substantially subsumes the impact of s(ATTO) and SIZE. The fourth and fifth columns of Table 4 exhibit the results of the Fama-MacBeth and year fixed effects, respectively. Furthermore, we not only control for year dummies but also firm dummies, and exhibit the results of year and firm fixed effects in column six. We find that the results for all of these alternative regression methods are very similar to the final GMM regression. Lastly, the column seven of Table 4 exhibits the results of year and firm fixed effects regressions of AP on lagged IMP (i.e., IMPlag1) and other control variables. Since the coefficient of IMPlag1 is still negative and statistically significant (b ¼ 0.019, p < 0.01), results suggest that integrated market power may drive the use of trade credit from suppliers. In sum, our findings support Hypothesis 1, suggesting that firms with more integrated market power can effectively reduce the use of upstream trade credit. 4.6. Regressions of institutional ownership (INST) Next, we discuss results for regressions of Institutional Ownership in Table 5. To analyze the impact of integrated market power on a firm's institutional ownership, we also include several control variables, such as R&D, Tobin's Q, LEVERAGE, SIZE, and TANG. The Hausman test also suggests a fixed-effect model over a randomeffect model (the m-statistic of 327.01 exceeding the critical value 12.59 of the Chi-square test for the INST regression). VIF tests are exercised in the last two columns of the table. Since all VIF values are still far less than the threshold of 10, we do not find any multicollinearity in this model. We exhibit the GMM regression of INST on IMP from column one to column three. In the first column, the positive coefficient of IMP indicates that integrated market power can increase institutional ownership without considering other control variables. The second column shows results of GMM regressions of INST on other control variables. All of the results are consistent with the extant literature. For example, the negative coefficient of R&D and positive coefficient of SIZE imply that institutional investors tend not to invest risky or small companies as a whole. Additionally, the negative coefficient of LEVERAGE provides support for the current literature and suggests that institutional investors may not hold stakes for firms with a high financial burden. In contrast, the positive

Table 4 Presents the regressions of accounts payable, where IMP is the integrated market power. IMPlag1 is lagged IMP. s (ATTO) is demand uncertainty by calculating the standard deviation of sales scaled by total assets over the past five years. R&D is calculated as research and development expense scaled by total sales. SIZE is firm size, calculated as the natural log of firm's total assets. TANG is the property, plant, and equipment to total assets ratio. Regressions of accounts payable (AP). Intercept IMP

GMM 0.111*** (151.80) 0.036*** (-54.75)

GMM 0.082*** (11.07)

GMM 0.103*** (15.56) 0.031*** (-46.83)

Fama-Macbeth 0.115*** (13.56) 0.030*** (-26.64)

Fixed effects

Fixed effects

0.030*** (-54.61)

0.030*** (-52.05)

IMPlag1

s(ATTO) R&D SIZE TANG Year fixed effects Firm fixed effect Adj. R2, Avg. R2, or R2

No No 0.19

0.104*** (24.96) 0.310*** (-21.09) 0.005*** (9.64) 0.183*** (-48.60) No No 0.21

0.054*** (13.62) 0.021 (0.12) 0.003*** (6.68) 0.162*** (-48.32) No No 0.31

0.056*** (13.69) 0.105 (1.42) 0.002* (1.77) 0.150*** (-21.09) No No 0.33

0.062*** (18.16) 0.002 (-0.13) 0.003*** (9.30) 0.156*** (-51.65) Yes No 0.34

0.015*** (5.94) 0.181*** (-9.67) 0.007*** (10.37) 0.077*** (-21.13) Yes Yes 0.83

Fixed effects

VIF

VIF

1.36 0.019*** (-31.53) 0.016*** (6.09) 0.204*** (-10.05) 0.010*** (13.62) 0.065*** (-16.25) Yes Yes 0.82

1.38 1.18

1.19

1.31

1.31

1.08

1.08

1.12

1.13

N ¼ 20,556; ***, **, and * indicate significance at the 0.01, 0.05, and 0.10 levels, resp.

Please cite this article as: Jhang, S.-S et al., How does firms’ integrated market power affect upstream trade credit and institutional ownership? Evidence from Taiwan, Asia Pacific Management Review, https://doi.org/10.1016/j.apmrv.2019.08.001

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S.-S. Jhang et al. / Asia Pacific Management Review xxx (xxxx) xxx

Table 5 Presents the regressions of institutional ownership, where IMP is the integrated market power. IMPlag1 is lagged IMP. R&D is calculated as research and development expense scaled by total sales. Tobin's Q is the market to book value of assets. LEVERAGE is the ratio of total debt to the market value of total asset. SIZE is firm size, calculated as the natural log of firm's total assets. TANG is the property, plant, and equipment to total assets ratio. Regressions of Institutional Ownership.

Intercept IMP

GMM

GMM

GMM

Fama-Macbeth

Fixed effects

Fixed effects

0.357*** (200.47) 0.019*** (11.94)

0.639*** (-36.33)

0.622*** (-36.13) 0.035*** (20.26)

0.655*** (-23.74) 0.021*** (12.99)

0.031*** (19.63)

0.013*** (7.45)

IMPlag1

No

0.225*** (-5.36) 0.025*** (8.41) 0.219*** (-11.51) 0.065*** (57.68) 0.012 (-1.13) No

0.608*** (-13.36) 0.020*** (7.00) 0.208*** (-11.12) 0.065*** (60.08) 0.051*** (-4.86) No

0.619*** (-7.03) 0.045*** (3.49) 0.134*** (-7.28) 0.064*** (47.15) 0.035*** (-3.30) No

0.01

0.17

0.19

0.20

R&D Tobin's Q LEVERAGE SIZE TANG Year fixed effects Firm fixed effects Adj. R2, Avg. R2, or R2

0.713*** (-17.01) 0.026*** (13.26) 0.194*** (-10.83) 0.065*** (62.78) 0.026*** (-2.78) Yes No 0.21

0.747*** (-13.33) 0.007*** (4.07) 0.114*** (-7.89) 0.021*** (9.75) 0.079*** (-7.04) Yes Yes 0.78

Fixed effects

VIF

VIF

1.28 0.015*** (8.57) 0.727*** (-12.76) 0.005*** (2.72) 0.090*** (-6.09) 0.026*** (11.53) 0.093*** (-8.20) Yes Yes 0.80

1.29 1.32

1.33

1.20

1.21

1.56

1.55

1.12

1.12

1.31

1.32

N ¼ 20,556; ***, **, and * indicate significance at the 0.01, 0.05, and 0.10 levels, resp.

coefficient of Tobin's Q suggests that firms with high market evaluation can attract more institutional investors. From column three to column six, we include IMP and all control variables for analysis. Specifically, columns three and four exhibit the results of GMM and the Fama-Macbeth approach, respectively. Columns five and six exhibit the results of fixed effect models. In column three, we find that the coefficient of IMP is positively related to INST (b ¼ 0.035, p < 0.01), suggesting that firms with high integrated market power can attract more institutional investors. In the same vein, the results for all alternative regression methods are very similar to those for the GMM regression, indicating that our measure of integrated market power is robust to the inclusion of the control variables. In column seven of Table 5, we present the results of year and firm fixed effects regressions of INST on lagged IMP, as well as on the control variables. Unsurprisingly, we still find that the coefficient of IMPlag1 is positive and statistically significant (b ¼ 0.015, p < 0.01), suggesting that firms’ integrated market power may drive the investment decisions of institutional ownership. 4.7. Robustness tests We perform a variety of robustness checks on our empirical results. First, we scale accounts payable via net sales rather than total assets to measure a firm's upstream trade credit (Dass et al., 2015; Ge & Qiu, 2007; Petersen & Rajan, 1997). The year fixed effect model shows that IMP is statistically negatively associated with AP (b ¼ 0.010, p < 0.01). Furthermore, the year and firm fixed effect model also exhibits that IMP is statistically negatively associated with AP (b ¼ 0.006, p < 0.01). These results are consistent with our earlier findings, supporting Hypothesis 1 that firms with better integrated market power rely less on supply chain financing from suppliers. Second, Fabbri and Klapper (2016) suggest that accounts receivable (i.e., downstream trade credit) may play an essential role in accruing upstream trade credit, since firms may utilize downstream trade credit to finance their input purchases. To mitigate this concern, we add one more control variable, AR, measured by the accounts receivable scaled by total assets, to the model. The year fixed effect model shows that IMP is statistically negatively

associated with AP (b ¼ 0.018, p < 0.01). In addition, the year and firm fixed effect model also exhibits that IMP is statistically negatively associated with AP (b ¼ 0.02, p < 0.01). These results are similar to our previous finding, suggesting that the omitted variables from downstream trade credit are unlikely to affect our results. Third, the negative relationship between AP and IMP and positive relationship between INST and IMP may be attributable to reverse causality. The extant literature suggests that a lagged dependent variable could be included in the regression (Brown & Caylor, 2006; Chung & Zhang, 2011). Following these studies, we included one-year lagged AP and lagged INST as additional independent variables in each regression model. Unsurprisingly, based on the year and firm fixed effect model, we find that IMP is still statistically negatively associated with AP (b ¼ 0.02, p < 0.01). In addition, the year and firm fixed effect model shows that IMP is still statistically positively associated with INST (b ¼ 0.01, p < 0.01). Both results support our Hypotheses 1 and 2. Lastly, we exercise regression analyses by using changes in variables, since this approach is less likely to produce spurious relations than those using level variables (Chung & Zhang, 2011). Specifically, we examine whether year-to-year changes in market power can gauge the variations in temperaneous changes in upstream trade credit and institutional ownership. The coefficient of the changes of market power on the changes of upstream trade credit remains negative (b ¼ 0.02, p < 0.01), implying that firms constantly modify their upstream trade credit to respond to changes in their market power. Similarly, we find that there exists a positive coefficient (b ¼ 0.01, p < 0.01) between changes in market power and changes in institutional ownership, suggesting that institutional investors gradually adjust their stakes in response to changes in firms’ integrated market power. 4.8. Post hoc analysis Since institutional investors include foreign and domestic investors, we first examine “whether different institutional investors have difference preference for integrated market power” and exhibit the results in Table 6. Ferreira and Matos (2008) document that foreign investors have different stock preferences than domestic

Please cite this article as: Jhang, S.-S et al., How does firms’ integrated market power affect upstream trade credit and institutional ownership? Evidence from Taiwan, Asia Pacific Management Review, https://doi.org/10.1016/j.apmrv.2019.08.001

S.-S. Jhang et al. / Asia Pacific Management Review xxx (xxxx) xxx

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Table 6 Presents the regressions of institutional ownership for different types and the use of accounts payable across different industry categories. IMP is the integrated market power. IMP*ES is defined as IMP multiplied by an electronic dummy. s (ATTO) is demand uncertainty by calculating the standard deviation of sales scaled by total assets over the past five years. R&D is calculated as research and development expense scaled by total sales. Tobin's Q is the market to book value of assets. LEVERAGE is the ratio of total debt to the market value of total asset. SIZE is firm size, calculated as the natural log of firm's total assets. TANG is the property, plant, and equipment to total assets ratio. Post-hoc Analysis

Intercept IMP

Institutional Investors (INST)

Upstream Trade Credit (AP)

Foreign Inst.

Domestic Inst

Electronic Sector

Traditional Sector

All Sector

0.005*** (5.22)

0.008*** (5.22)

0.033*** (-40.77)

0.027*** (-32.50)

0.012*** (3.45) 0.172*** (-6.96)

0.018*** (5.06) 0.156*** (-5.05)

0.027*** (-30.08) 0.006*** (-5.38) 0.014*** (5.81) 0.172*** (-9.11)

0.009*** (9.65) 0.100*** (-17.59) Yes Yes 0.82

0.002** (2.17) 0.058*** (-12.62) Yes Yes 0.79

0.007*** (10.35) 0.078*** (-21.38) Yes Yes 0.83

IMP*ES

s(ATTO) R&D Tobin's Q LEVERAGE SIZE TANG Year fixed effects Firm fixed effects Adj. R2, Avg. R2, or R2

0.069** (2.38) 0.007*** (7.58) 0.080*** (-10.79) 0.036*** (33.17) 0.011* (-1.95) Yes Yes 0.80

0.816*** (-15.97) 0.000 (0.19) 0.034*** (-2.57) 0.016*** (-8.02) 0.068*** (-6.62) Yes Yes 0.74

N ¼ 20,556; ***, **, and * indicate significance at the 0.01, 0.05, and 0.10 levels, resp.

investors: for instance, foreign institutional investors may exhibit a strong preference for firms listed in the Morgan Stanley Capital International World Index or in the US stock exchange (i.e., issuing American Depositary Receipt), or may value stocks rather than growth stocks. As such, we divide the institutional ownership into foreign institutional ownership (FINST) and domestic institutional ownership (DINST), based on the geographic origin of the institutions (Bena et al., 2017). The first (second) column of Table 6 shows the results of a year and firm fixed effect regression of foreign (domestic) institutional ownership on market power, as well as control variables. The coefficient of IMP on FINST is reliably positive (b ¼ 0.005, p < 0.01), suggesting that high market power firms can attract more foreign institutional investors. Furthermore, the coefficient of IMP on DINST is also positive (b ¼ 0.008, p < 0.01), implying that firms with high market power can also attract more domestic institutional investors. We also find that both foreign and domestic institutional investors prefer to invest in firms without financial leverage and with lower capital intensity. Interestingly, we also find that foreign institutional investors prefer firms engaging in R&D activities, firms with high growth opportunities, and large firms. In contrast, domestic institutional investors prefer small firms and are less likely to invest firms that engage in R&D activities. Next, to further explore the boundary conditions of the results, we perform additional analysis and examine the role of industry categories. Past studies indicate that trade credit usage may be related to product characteristics or industry categories (Burkart & Ellingsen, 2004; Dass et al., 2015; Giannetti et al., 2011). In this study, we classify our sample into two main subsets: the electronic sector and the traditional sector, based on Taiwan Stock Exchange Industry Classification. Specifically, the electronic sector includes codes 24e31, and the traditional sector include codes 01e12, 14e23, and 80. The third (fourth) column shows the results of year and firm fixed effect regression of electronic (traditional) sector on IMP, as well as control variables. The IMP has a negative impact on AP in both the electronic sector (b ¼ 0.033, p < 0.01) and the traditional

sector (b ¼ 0.027, p < 0.01). To verify whether the magnitudes of IMP are different between these two sectors, we also include an interaction term, IMP*ES, defined as IMP multiplied by an electronic sector dummy. The interaction term in column five is significantly negative (b ¼ 0.006, p < 0.01), suggesting that market power exerts a stronger effect on the electronic sector than the traditional sector. In short, the evidence suggests that firms in the electronic sector can rely less on upstream trade credit than in the traditional sector, if firms can effectively reduce market competition or take substantial integration activities. Lastly, we examine the question, “How do the variables change across different status of integrated market power?” To do so, we sort firm-year observations into tertiles by IMP, and then calculate the mean values of IMP as well as all model variables in the regression for each tertile. For each variable, we also calculate Diff, defined as the difference between the mean values for tertile one and tertile three, Q1-Q3, the ratio Q3/Q1, and the ratios Diff/FSmean, and Diff/ FSsd, where FSmean and FSsd are the full-sample panel mean and standard deviation, respectively. The results are displayed in Table 7. The first row shows the mean values of IMP. Since IMP is the sorting variable, we do not report the t-statistic of Diff, and the ratios Q3/Q1 and Diff/FSmean are also meaningless for this variable. For the remainder of the table, variables are sorted into two groups: (i) model variables used as inputs to the factor analysis (PM, ATTO, INV, and CASH); (ii) the remaining model variables and control variables (AP, INST, s(ATTO), R&D, LEVERAGE, Tobin's Q, SIZE, and TANG). For the model variables used as inputs to the factor analysis, PM and CASH (ATTO and INV) increase (decrease) monotonically across the IMP tertiles. These results are not particularly surprising because IMP is simply a linear combination of these four variables (see Eqs. (13)e(15)). For this reason, we don't report the t-statistics of Diff for these variables. Nevertheless, for PM, the value of Q3/Q1 is 2.84, suggesting that profit margins are almost three times higher for firms with high integrated market power (Q3) versus low integrated market power (Q1). In the same vein, relative to firms with

Please cite this article as: Jhang, S.-S et al., How does firms’ integrated market power affect upstream trade credit and institutional ownership? Evidence from Taiwan, Asia Pacific Management Review, https://doi.org/10.1016/j.apmrv.2019.08.001

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S.-S. Jhang et al. / Asia Pacific Management Review xxx (xxxx) xxx

Table 7 Presents mean values of model and control variables by tertiles of integrated market power measure (IMP). PM, ATTO, INV, and CASH are profit margins, assets turnover, inventory to total assets ratio, and cash and cash equivalent to total assets ratio, respectively. AP is accounts payable to total assets ratio. INST is the sum of the shares holdings of all institutions in a firm's stock scaled by its total market capitalization. s (ATTO) is demand uncertainty by calculating the standard deviation of sales scaled by total assets over the past five years. R&D is calculated as research and development expense scaled by total sales. LEVERAGE is the ratio of total debt to the market value of total asset. Tobin's Q is the market to book value of assets. SIZE is firm size, calculated as the natural log of firm's total assets. TANG is the property, plant, and equipment to total assets ratio. Mean values of model and control variables by tertiles of integrated market power (IMP) measure Q1-Q3: Variable IMP Factor components: PM ATTO INV CASH Other model variables AP INST s(ATTO) R&D LEVERAGE Tobin's Q SIZE TANG N (Firm Year)

Ratios:

Low MP 1.041

Medium MP 0.030

Large MP 1.071

Diff 2.112

t-statistic n/a

Q3/Q1 n/a

Diff/Fsmean n/a

Diff/Fssd

0.089 1.228 0.223 0.100

0.132 0.820 0.132 0.136

0.253 0.666 0.081 0.232

0.164 0.562 0.142 0.132

n/a n/a n/a n/a

2.84 0.54 0.36 2.32

0.77 0.62 0.98 0.85

1.23 1.25 1.47 1.11

0.151 0.343 0.239 0.014 0.097 1.365 15.386 0.245 6845

0.112 0.353 0.163 0.024 0.112 1.431 15.441 0.322 6858

0.071 0.376 0.156 0.046 0.083 1.661 15.114 0.296 6853

0.079*** 0.033*** 0.083*** 0.031*** 0.014*** 0.297*** 0.271*** 0.051***

60.00 8.74 30.02 48.78 8.73 22.79 11.58 17.96

0.47 1.10 0.65 3.20 0.86 1.22 0.98 1.21

0.71 0.09 0.45 1.12 0.14 0.20 0.02 0.18

0.95 0.15 0.53 0.83 0.15 0.39 0.19 0.30

low market power, firms with high market power generally have: (i) slightly more than half the asset turnover (ATTO: Q3/Q1 ¼ 0.54); (ii) only slightly more than one third of the total inventory (INV: Q3/Q1 ¼ 0.36); and (iii) a cash balance that is over two times higher (CASH: Q3/Q1 ¼ 2.32). Next, we focus on the remaining model variables for discussion. Interestingly, for some variables, their mean values change monotonically across the IMP tertiles. For example, AP and s(ATTO) decrease with IMP, while INST, R&D, and Tobin's Q increase with IMP. The values of Diff are different from zero for these variables. Specifically, relative to firms with low market power, firms with high market power generally have (i) 35% lower demand uncertainty (s(ATTO): Q3/Q1 ¼ 0.65) and (ii) 53% lower upstream trade credit (AP: Q3/Q1 ¼ 0.47). In addition, high market power firms also generally have (i) higher institutional ownership (INST: Q3/ Q1 ¼1.10); (ii) higher R&D (R&D: Q3/Q1 ¼ 3.20); and (iii) higher market evaluation (Tobin's Q: Q3/Q1 ¼ 1.22).

5. Discussion 5.1. Theoretical implications This study contributes to the extant literature in several ways. First, our study is the first to relate a firm's integrated market power to the use of upstream trade credit in emerging markets. We extend the extant literature and find that integrated market power is still negatively related to upstream trade credit in Taiwan, where most firms are located at or near the top of the supply chain, and the information asymmetry or agency problem may be more serious than in developed countries. Our findings suggest that firms pursuing an increase of market power or enhancing integration ability between suppliers and customers can effectively reduce their use of upstream trade credit. In other words, for low market power firms, they may exert to use free financial resource (i.e., accounts payable) from suppliers to enhance their working capital management. Additionally, the effect of market power on AP is consistent across both the electronic sector and the traditional sector, which demonstrates the robustness of such effects. The negative relation between market power and upstream trade credit also offers additional support for extant studies indicating that firms with a

relatively high Lerner Index may utilize less trade credit (Dass et al., 2015; Fabbri & Klapper, 2016; Petersen & Rajan, 1997). However, our findings are inconsistent with the hypothesis related to the cash conversion cycle (CCC), suggesting that firms tend to shorten the time when operating capital is out of reach and firms tend to increase the level of available cash on hand (Gallinger, 1997; Jose, Lancaster, & Stevens, 1996; Moss & Stine, 1993; Schilling, 1996; Soenen, 1993). A plausible explanation is that the CCC considers cash level to be the only dimension of power measure, which is not enough to gauge a firm's integrated market power. Furthermore, the CCC ignores the endogenous problem of downstream and upstream trade credit since firms can extend and use trade credit simultaneously (Fabbri & Klapper, 2016). Second, our study documents that market power is positively related to institutional ownership. Most prior studies explain a firm's institutional ownership from several perspectives of corporate finance rather than its integrated market power. In this study, we show that firms engaging in effective integration or increasing monopoly power could attract more institutional investors. Additionally, the effect of market power on institutional ownership is persistent across foreign institutional investors and domestic institutional investors. This finding highlights the role that a firm's market power plays in enhancing institutional investors' attitudes. Our finding also complements extant literature claiming that institutional investors prefer stocks with certain firm-specific variables (Chung & Zhang, 2011; Ferreira & Matos, 2008; Grinstein & Michaely, 2005).

5.2. Managerial implications Our study provides several practical implications for managers in Taiwan. First of all, our research encourages managers to pay more attention to their firm's market power status. Our results demonstrate that a firm's market power can reduce reliance on supply chain financing from suppliers. Secondly, managers should note that increasing market power or enhancing supply chain integration provides a positive signal to institutional investors and motivates them to invest more. In contrast, reducing cooperation with supply chain partners weakens a firm's monopoly status in the industry and may prompt institutional investors to overlook

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investment opportunities. Finally, managers should be aware of the roles of innovation activities, which may influence different types of institutional investors. Particularly for firms engaging in R&D activities, this innovation action is effective for obtaining foreign institutional investors' attention. 6. Conclusions, limitations, and suggestions for future research This paper contributes to the operations, supply chain, and financial management of firms by presenting and testing a model of upstream trade credit and institutional ownership of such firms as a function of their integrated market power status. Using data on seasoned, publicly traded Taiwanese firms from 1996 to 2017, and methods including correlation analysis, time series analysis, factor analysis, and hierarchical regression analysis, we document that firms with high integrated market power or supply chain integration ability tend to hold relatively low upstream trade credit. These results may not only be attributed to the fact that these firms face less demand uncertainty, but also to the fact that these firms can cooperate with each other to reduce moral hazard or information asymmetry. Furthermore, high integrated market power firms tend to attract both foreign institutional investors and domestic institutional investors. Lastly, the post-hoc analysis exhibits that the sensitivity of upstream trade credit on integrated market power is more important for firms located in the electronic sector than in the traditional sector. We recognize several limitations in this study. For example, we pay attention to upstream trade credit rather than downstream trade credit of emerging markets. Future research can extend these findings by focusing on the financing behavior of downstream customers. In addition, the firm-level and supply chain variables are our concerns to affect the regressand in this study; it may be an interesting issue for future research to include the extent of the supply chain network. Last but not least, researchers can extend our study to the fields of investment management or capital budgeting. We believe that more interdisciplinary research can be done in this area. Acknowledgment We thank the editor, the anonymous reviewers, and conference participants at the 47th Decision Science Institute (DSI) Annual Meeting and the 29th Production and Operations Management Society (POMS) for valuable feedback and comments. Also, we thank research assistants, Chui-Chun Chiu and Sih-Ting Lin, for their help. We acknowledge research grant from Ministry of Science and Technology (107-2410-H-110-001-MY2). References Aggarwal, R., Erel, I., Ferreira, M., & Matos, P. (2011). Does governance travel around the world? Evidence from institutional investors. Journal of Financial Economics, 100(1), 154e181. Aghion, P., Van Reenen, J., & Zingales, L. (2013). Innovation and institutional ownership. The American Economic Review, 103(1), 277e304. Ak, B. K., & Patatoukas, P. N. (2016). Customer-base concentration and inventory efficiencies: Evidence from the manufacturing sector. Production and Operations Management, 25(2), 258e272. Aksin, O. Z., & Masini, A. (2008). Effective strategies for internal outsourcing and offshoring of business services: An empirical investigation. Journal of Operations Management, 26(2), 239e256. Alan, Y., Gao, G. P., & Gaur, V. (2014). Does inventory productivity predict future stock returns? A retailing industry perspective. Management Science, 60(10), 2416e2434. Bates, T. W., Kahle, K. M., & Stulz, R. M. (2009). Why do US firms hold so much more cash than they used to? The Journal of Finance, 64(5), 1985e2021. Bena, J., Ferreira, M. A., Matos, P., & Pires, P. (2017). Are foreign investors locusts? The long-term effects of foreign institutional ownership. Journal of Financial

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