Effects of IoT technology on gray market: An analysis based on traceability system design

Computers & Industrial Engineering 136 (2019) 80–94

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Effects of IoT technology on Gray Market: An Analysis Based on Traceability System Design a

a,⁎

b

a

Long Ding , Bin Hu , Changying Ke , Tingting Wang , Shan Chang a b

a

T

School of Management, Huazhong University of Science and Technology, 430074 Wuhan City, Hubei Province, China School of Management, Wuhan Institute of Technology, 430074 Wuhan City, Hubei Province, China

ARTICLE INFO

ABSTRACT

Keywords: Supply chain management IoT technology Gray market Traceability system Game theory

Gray market emerges when the retailer diverts product from a lower-price market to a higher-price market without the manufacturer’s permission. Internet of Thing (IoT) technology enables the manufacturer to track gray-product distribution and monitor gray market activity. In this paper, we aim to introduce IoT technology into gray market management and investigate the impact of IoT technology on gray market and firms' profits. First, we design an IoT-based traceability system which realizes the real-time monitoring of gray market activity. Based on the IoT-based traceability system, then we consider the manufacturer’s two coping strategies and establish three game models to examine how IoT technology affects gray market and firms’ profits. Finally, we present the numerical simulations to perfect and supplement our findings. We find that IoT technology has significant implications which can inhibit gray market. However, it is not always beneficial to the manufacturer or harmful to the retailer. If the manufacturer takes an appropriate coping strategy, a win-win situation can be achieved.

1. Introduction Gray market, also known as parallel importation, has been booming and prevalent in a wide range of industries (Antia, Bergen, Dutta, & Fisher, 2006). For example, gray-market cellphone has nearly a 35% share in China’s mobile market in 2009 and has reached beyond the share of any other handsets (Liao & Hsieh 2013). In recent years, the gray-market iPhone’s growth was remarkable in China. Lots of researches have shown that gray market not only exists in China’s mobile phone industry but also abounds in other industries around the world (Myers 1999; Huang, Lee, & Hsun Ho, 2004). In Malaysia, the share of gray-market mobile phone accounts for almost 70% of the total mobile phone market (Antia, Bergen, & Dutta, 2004). In Germany, over 300,000 gray market cars were sold in 1996. In United Kingdom, almost 20% of the sales are gray-market products in pharmaceutical industry (Kanavos, Holmes, Loudon, & Benedict, 2005). Besides, in global IT industry, the share of gray-market products accounts for nearly 30% of the total IT sales (Ahmadi, Iravani, & Mamani, 2015). With the rapid development of Internet and E-commerce, the hazards of gray market have become increasingly serious. The manufacturers in these industries are facing increasing pressures of gray market. Many global manufacturers are losing much (Bandyopadhyay 2010). For example, Mercedes-Benz and BMW are investigating parallel import of luxury cars, which has recently threatened profits in the world's largest

automobile market (Reuters 2015). In order to relieve negative impact of gray market on brand image, Nikon has set up internet resources with aim to educate the public on the negative impact of gray market products. For Pepsi-Cola, gray market has brought great harm to its normal order and brand image (Quick Consumer Good 2017). Many other companies have also suffered from gray market, including Apple, Canon, and Samsung. To address gray market problem, numerous scholars have made a depth research mainly from three perspectives. From the contract perspective, some scholars argued that the manufacturers should sign contracts with retailers to bind retailer’s activities of gray market (Gallini & Hollis 1999; Myers & Griffith 1999). From the law perspective, some scholars suggested that the government should perfect the law of copyright that prevents firms from conducting gray market (Duhan & Sheffet 1988; Alberts 1991; Hintz 1993; Mohr 1995). In recent years, a few scholars studied gray market from the perspective of marketing (Zhang 2016; Ahmadi et al., 2015). Although these perspectives enrich the methods of gray market management, none of them have involved the core problem, that is, gray market has the characteristic of concealment which makes it difficult to capture the grayproduct information. Traditional technology has fallen short of the needs of the gray market management. With the development of technology, the manufacturers start to apply information technology to monitor the distribution of products. In particular, IoT technology is

Corresponding author. E-mail addresses: [email protected] (L. Ding), [email protected] (B. Hu), [email protected] (C. Ke), [email protected] (T. Wang), [email protected] (S. Chang). ⁎

https://doi.org/10.1016/j.cie.2019.06.038

Available online 28 June 2019 0360-8352/ © 2019 Elsevier Ltd. All rights reserved.

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regarded as an effective method to improve market channel transparency by tracking items and providing real-time product flow information. The Internet of Things (IoT) has been developing for about 20 years. In previous studies, some scholars have investigated the impact of IoT technology on the supply chain, they are looking to use IoT technology to solve the problems of supply chain management (Native and Lee 2012; Chen, Wang, Xie, & Qi, 2014; Chatziantoniou, Pramatari, & Sotiropoulos, 2011; Demiralp, Guven, & Ergen, 2012; Lou, Liu, Zhou, & Wang, 2011; Xu 2011; Yan & Huang 2009). Such as Chatziantoniou et al. (2011) applied IoT technology to support real-time decision making. Besides, Chen et al. (2014) investigated the application of IoT technology to eliminate the misplacement problems in supply chain. Furthermore, some scholars also studied the inventory problem in a multi-level supply chain environment (Gharaei, Karimi, & Shekarabi, 2019a; Gharaei, Hoseini Shekarabi, & Karimi, 2019b; Gharaei, Karimi, & Hoseini Shekarabi, 2019c; Hoseini Shekarabi, Gharaei, & Karimi, 2018; Mogale, Dolgui, Kandhway, Kumar, & Tiwari, 2017; Langroodi & Amiri 2016), they regarded the inventory cost as an integral part of the multi-level supply chain which is the key factor for a successful company (Hoseini Shekarabi et al., 2018). However, they neglected that the multilevel information management is complicated in the multi-level supply chain. So far, few scholars have focused on what role the IoT technology might play in such multi-level supply chains (Ding, Jiang, & Su, 2018). In summary, the previous studies have brought IoT technology into supply chain management. However, there is no study to introduce IoT technology into gray market research. Under this context, we introduce IoT technology into gray market research for the first time, since IoT technology has the potential to solve the core problem of gray market by its characteristics of traceability. In this paper, we attempt to address the following research questions:

Furthermore, Ding et al. (2018) proposed an RFID-enabled social manufacturing system to realize the real-time monitoring of inter-enterprise production and transportation. Another stream of study pays more attention to the design of the traceability system based on IoT technology (Zhang, Chai, Weng, & Zhai, 2010; Zhao et al., 2009; Ngai et al. 2007; Liu et al. 2008; Pang, Chen, Han, & Zheng, 2015), most of which focused on the field of food traceability. For instance, Zhang et al., (2010) designed the information traceability system for pork production supply chain based on RFID technology, it enables people to get logistics information precisely and systematically. Later, Pang et al., (2012) addressed a joint design framework of value-centric businesstechnology for the food supply chain. Besides, some scholars have designed traceability systems for other supply chains. For example, Yan, Shi, and Huang (2013) designed a traceability platform for aquatic food supply chain. It can track and trace the information about production, processing, breeding, and sale channels. In this paper, we introduce IoT technology into gray marketing by designing a gray marketing traceability system. Because of the importance of sustainability in gray marketing traceability system, our paper is also related to the field of sustainability in supply chain. For deep reviews, consider the below references (Gharaei et al., 2019a, 2019b, 2019c; Sayyadi & Awasthi, 2018a, 2018b; Hao, Helo, & Shamsuzzoha, 2018; Rabbani, Foroozesh, Mousavi, & Farrokhi-Asl, 2019; Tsao, 2015; Awasthi & Omrani, 2019; Dubey, Gunasekaran, Sushil, & Singh, 2015; Kazemi, Abdul-Rashid, Ghazilla, Shekarian, & Zanoni, 2018; Rabbani, Hosseini-Mokhallesun, Ordibazar, & Farrokhi-Asl, 2018). Our paper enriches the literature about the application of IoT technology. We are the first to expand the application of IoT technology to gray market field. Our paper is closely related to gray market literature. One stream of study has researched the cause and the effect of gray market problem (Knoll 1986; Huang et al., 2004; Liao & Hsieh 2013; Gorelick & Little 1986; Xiao, Palekar, & Liu, 2011; Chen 2009; Ahmadi & Yang 2000; Dasu, Ahmadi, & Carr, 2012). Knoll (1986) proposed a whole set of strategies from the causes and consequences to the responses of the gray-market imports, while also exploring the possibility of private remedies to deter gray market. From a demand perspective, Huang et al. (2004) investigated the relationships between the consumer attitude toward gray market product and their antecedents. In particular, in the above literature, some researchers argued that gray market is harmful because it may erode the brand value of the firm, while others emphasized that gray market may also be beneficial because it has provided a new approach to boost sales. A few researchers have appealed for a comprehensive consideration of positive and negative effects of gray market. One example is Berman and Dong (2016), they found an overall strategy for deterring gray market based on the comprehensive consideration of positive and negative effects. Another stream of study is more concerned about the countermeasure for the gray market problem (Gallini & Hollis 1999; Hintz 1993; Antia et al., 2004; Ahmadi et al., 2015; Mohr 1995; Duhan & Sheffet 1988; Antia et al., 2006; Cavusgil & Sikora 1988; Lowe & McCrohan 1989; Myers & Griffith 1999; Alberts 1991; Autrey, Bova, & Soberman, 2015; Su & Mukhopadhyay 2012). Duhan and Sheffet (1988) offered some suggestions from the perspective of the legal status of parallel importation. However, Gallini and Hollis (1999) insisted that it is inefficient to use trademark and copyright laws against parallel imports, the manager should establish a policy, which combines contract, tort, and antitrust law, to deter gray market. Besides, Su and Mukhopadhyay (2012) suggested that the manufacturer can use a dynamic quantity discount contract or a revenue-sharing contract to inhibit the gray market activities. Hintz (1993) advised that the manufacturer should use copyright law to battle against gray market. There are also several recent papers that study how to fight gray market activities. For instance, Zhang (2016) declared that manufacturer offered consumer rebates has a deterrence effect on gray market. Ahmadi et al. (2015) analyzed the impact of market conditions and product characteristics on the manufacturer’s strategy for gray market. This paper makes a contribution to

a. How does IoT technology work in monitoring gray market? b. How does IoT technology impact the manufacturer’s decisions? c. How does IoT technology affect gray market and firms’ profits? The main contributions of this paper are as follows: (1) IoT technology is introduced into gray market research for the first time; (2) We originally build up an IoT-based gray market traceability system, which realizes the applicaition of IoT technology in gray market; (3) We formulate a two-stage game model based on the traceability system and put forward the new management tool on gray market. Our findings indicate that the IoT technology can help the manufacturer inhibit gray market and potentially resulting in a win-win situation. 2. Literature Review Our paper is closely related to the literature about IoT technology research on the supply chain. One stream of study has researched the benefits of IoT technology for real-time information acquisition in supply chain (Chatziantoniou et al., 2011; Demiralp et al., 2012; Native and Lee 2012; Zhou & Piramuthu 2012; Chen et al., 2014; Xu 2011; Yan & Huang 2009; Ding et al., 2018). Chatziantoniou et al. (2011) paid attention to the unique data capturing characteristics of IoT technology to realize real-time decision-making, they mainly focused on two RFID data management issues: expressibility and performance. Nativi and Lee (2012) analyzed the impact of RFID information-sharing strategies on the supply chain, they applied IoT technology to real-time monitor the inventory information and investigated whether this real-time data can improve the environmental and economic benefits. Similarly, Chen et al., (2014) addressed how the application of RFID technology can be used to get rid of the misplacement problems in supply chain, their results show that an increase in RFID cost will not decrease the manufacturer’s incentive when she gets much risk from the retailer. Besides, Zhou and Piramuthu (2012) considered the availability of item-level RFID information in the background of large-scale manufacture. 81

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this stream of literature from a new angle: applying IoT technology to deal with gray market. We also complement these studies and suggest a unique insight for the manufacturer to control gray market. The overall structure of this paper is as follows. Section 3 presents the IoT-based gray market traceability system. Section 4 describes the modeling of a two-stage supply chain based on the traceability system. Section 5 investigates how IoT technology affects gray market and firms’ profit. Section 6 studies what changes would IoT technology bring to gray market, and the corresponding impacts. Section 7 takes a numerical study. We give a detailed discussion of results in Section 8 and conclude in Section 9.

designed in IoT Devices Executive System. IoT Devices Executive System. It is the practical application process of IoT technology. As illustrated in the bottom part of Fig. 1, manufacturer attaches the RFID tag on the products through the coding system. Here, it is not economical and reasonable for manufacturer to label the RFID tag on the products which will be sold into the high-end market. Except for RFID tags, the RFID readers (fixed RFID portal and handheld RFID readers) and radio communication devices are installed on the retail store2 in the low-end market. These hardware devices of IoT constitute the second application of the traceability system so that the sales data in the low-end market could be crawled when the labeled products are sold and out of the RFID reader’s scanning area3. Once the products arrived at the appointed location, it means the end of the first application layer of the traceability system, and manufacturer could obtain the volume information of the commodities fleeing. Through the second application layer of the traceability system, manufacturer could keep tracing the information of commodities fleeing and capture the real-time demand of the low-end market. It enables manufacturer to recognize whether or not retailer diverts the goods and how many. In summary, the two applications of IoT technology jointly to constitute the solid foundation of the traceability system and realize the real-time monitoring of gray market.

3. Architecture of IoT-based Gray Market Traceability System We build up a gray market traceability system through the application of IoT technology. As illustrated in Fig. 1, the architectural framework of the IoT-based gray market traceability system (IGTS) composes of three layers, i.e. Market Structure, Logistics System, and IoT Technology Executive System. A detailed description of each layer is as follows. Market Structure. It is the research object of this paper. As described in the upper part of Fig. 1, we consider a supply chain with a retailer R and a manufacturer M who sells a single product in two separate markets1: market H and market L. The market H is characterized as a high-end market in which the manufacturer sells products directly to the consumer. Whereas the market L is defined as a low-end market in which the products are sold to the consumer through an authorized retailer R. In general, it is challenging for the consumer in market H (L) to purchase the products from market L (H) as a result of the restriction of time-space and location (such as an average Indian consumer usually can’t buy the American version iPhone), unless there exists a gray market G. It is a typical scenario that consumer’s willingness to pay in high-end market is higher than it in the low-end market. Therefore, retailer R has incentive to resell the product to high-end market with a higher price (higher than the price in low-end market) through the unauthorized channel. Furthermore, the consumers always tend to purchase the product at a lower price. Combine to the above, the motivation of both retailer and consumer together lead to the formation of gray market G. Logistics System. There exist three logistic transportation routes: the first path moves from manufacturer to retailer to low-end market (M→ R→L), the second path moves from manufacturer to high-end market (M→H), the third path moves from manufacturer to retailer to gray market (M→R→G). And these logistic transportation routes are divided into two stages based on the duty partition. Manufacturer is responsible for the first stage, including M→H, M→R. The responsibility for the second stage is placed in retailer, which are from retailer to low-end market and gray market (R→L, R→G). In our IGTS, the application of IoT technology includes two parts: first in the Logistics System and second in the IoT Devices Executive System. In Logistics System, the RFID tags will be connected to the trucks or the plastic pallet (a new type of freight transportation tool). It could trace the position of cargo timely and directly by the real-time monitoring and enable manufacturer to monitor whether the cargo is unloaded at the appoint location or on the halfway. Once retailer changes the logistic route and diverts the products, these data will be saved to the Real-time Database Server by the connected IoT infrastructures and RFID terminal reader which installed on the appointed location. All the recorded data are available to manufacturer. Remarkably, we have not designed and installed the IoT devices in the stage of M→H and M→R, because manufacturer is responsible for this stage and the commodities fleeing will not happen. Besides, the second application of IoT technology is

4. Model Setup In this section, we establish the models based on the designed IGTS. As illustrated in Fig. 1, we consider a supply chain with one dominant manufacturer and one retailer. The application of the traceability system enables the manufacturer to crawl the information of the gray market. Then, how to launch the coping strategies once the manufacturer obtains the gray market demand information? In this paper, we consider two of the common and effective coping strategies: 1) coping strategy A, punishing retailer based on the accurate volume of the gray products, 2) coping strategy B, punishing the consumers who purchase the gray product. For the coping strategy A, the manufacturer could impose a punitive cost of > 0 on per unit of fleeing goods from the retailer. For the coping strategy B, the manufacturer could cancel the gray product after-service or charge extra fees, and this will entail a loss of > 0 on consumer effect. A higher implies a stronger punishment for purchasing the gray product. We define and as punitive cost and penalty utility respectively. Could not these two coping strategies be launched without IoT technology? Someone may ask. Although these two coping strategies have a wide practical background, they have rarely been investigated in previous research solely because the accurate information of the gray market (gray product) data are not available. 4.1. Notations and Assumptions

w : the wholesale price from the manufacturer to the retailer; pi : the unit product price in each market (i = h , l, g refers to highend market, low-end market and gray market); Di : the product demand in each market (i = h , l, g ); 2 As Assumption 5 showed, the manufacturer is the leader and has the power to implement IoT technology at the downstream stage. In practice, for instance, the power company Apple could re-select retailers, even set up the IoT devices: http://tech.qq.com/a/20121211/000017.htm. Another realistic example: a power retailer requires manufacturer to adopt RFID tags, as described in website at http://www.21ic.com/news/rf/201111/98895.htm. 3 As shown in the bottom part of Fig.1. The technical mechanism is as follows. The active RFID tag could send a certain frequency radio signal, once the labeled products move through the fixed RFID portal or be scanned by the handheld RFID readers, the RFID reader could read and decode the information from the active RFID tag finally send the information to the Real-time Database Server.

1 This market structure is widely studied in the gray market literature (Zhang, 2016; Ahmadi et al., 2015; Ahmadi & Yang, 2000; Iravani et al., 2013)

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Unauthorized channels

Real-time Database Server

Real-time monitoring

Authorized channels

Step 3

RFID Reader

Step 1

Coding System Step 2

Logistics process

Workflow of IoT

IoT Devices Executive System

RFID Tag

Gray Market Structure and Logistics System

Real-time monitoring

Demand information Feedback

Fig. 1. Architectural framework of the IoT-based gray market traceability system. j:

the expected profit ( j = m , r refers to manufacturer and retailer); : punitive cost of unit gray product to retailer; : penalty utility of unit gray product to consumer; : unit cost of RFID tag; : the consumers’ preference for the gray product; Vk : consumers’ willingness to pay for the product (k = h, l refers to high-end product and low-end product); U : the net consumer effect of unit product;

ph > pg > pl > 0 . Assumption 4. We assume vl/ vh < < 2 vl /vh , which implies that the consumers’ gray product preference should not be too small such that retailer never fleeing goods or too large such that all the products are resold to the gray market. Both of these cases are not taken into consideration in this paper. This assumption appears in the related literature frequently (Xiao et al., 2011; Shao, Krishnan, & McCormick, 2016; Zhang 2016; Chiang, Chhajed, & Hess, 2003), which also ensures that the equilibrium results are positive.

Assumption 1. We normalize the unit production cost incurred by the manufacturer and the diversion cost incurred by the retailer to zero, respectively, as these parameters can be easily implement in the model and do not affect the main results in general. It’s also a common hypothesis which is widely used in the previous research (Lyer and Soberman 2016; Li & Jain 2016; Ozer & Zheng 2016; Zhang 2016). Furthermore, we find that there are two types of costs of IoT technology in the IGTS: the fixed costs (includes RFID readers, radio communication devices and plastic pallets) and the variable costs (RFID tag which attached on each product). We also assume that these fixed costs are zero4.

Assumption 5. As scholars were done (Cai & Chen, 2011), we assume vl = 1 andvh = 8 to simplify the model and profoundly analyze its equilibrium results. Different parameters vl and vh do not change our main results.

4.2. Model Formulation The Manufacturer sells a single product in two separate markets: the high-end market and the low-end market. We define the products in the high-end market, low-end market, and gray market as the high-end product, low-end product, and gray product respectively. Consumers are heterogeneous in their willingness to pay for the high-end product and the low-end product, denoted by Vh and Vl , which are distributed uniformly over the interval [0, vh ] and [0, vl ]. As we mentioned above, the low-end market consumers have a lower valuation of the product than the high-end market consumers, thus vh > vl > 0 . Therefore, retailer R has the incentive to resell the product to high-end market with a higher price (higher than the price in low-end market) through the unauthorized channel, so that gray market is formed (as shown in Fig. 1). Consumers’ valuation of the gray product is quantified as Vh (0 < < 1), which means the consumers value the gray product less than the high-end product, simply because the gray products are without guarantees. The Parameter represents the consumers’ preference for the gray product, which we define as the gray product preference.

Assumption 2. Not considering the impacts of inventory in this paper, the retailer sells the products out finally and carries no inventory. This assumption ensures the precise monitoring of the gray market properly. Once the products arrived at the low-end market, the manufacturer could get accurate information of the low-end market sale through the second layer of the traceability system. Thus the manufacturer could calculate the scale of the gray products5. It’s a common hypothesis which has been widely used in the previous study6. Assumption 3. It’s a realistic and straightforward hypothesis that 4 In Section 7, we will investigate the effects of the fixed costs of IoT infrastructures invested by the manufacturer. 5 Because retailer carries no inventory, so the scale of the gray product equals retailer’s ordering quantity minus low-end product sales. 6 As long as the research is not focus on the issue of inventory, most scholars investigating supply chain management issue do not consider the inventory (Ozer & Zheng 2016; Li & Jain 2016; Iyer & Soberman 2016).

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5.1. NI Model We first simply analyze the NI model, a basic model which has been widely studied in the previous literature of the gray market. Similar to Cai et al. (2010), we assume 1/8 < < 2 /2 to ensure equilibrium results are all positive, which also implies that the gray product preference should not be too small or too large such that retailer never diverts the products to gray market or diverts all the products to gray market. This section could be a supplement to previous studies and offer a base model for comparative analysis. A consumer’s utility from purchasing the high-end product is given by

Fig. 2. Decisions Timing.

4.2.1. Manufacturer Coping Strategies Through the traceability system, the manufacturer could reasonably respond to the gray market activities and make proper strategies. For instance, once the manufacturer realizes the gray market information in detail, she could simply impose against the part of fleeing goods instead of groundless charge for all the products including the authorized products. In conclusion, the two coping strategies closely related to IGTS, neither is dispensable. Based on these two coping strategies, we establish three different game models in Section 5.

UH = VH

(1)

ph

Similarly, a consumer purchases a gray market product to get a utility of

UG = VH

pg

(2)

The consumer who selects the low-end product could receive a net utility of

4.2.2. Decisions Timing We set up a two-stage game of complete information in which various stakeholders pursue their own maximum profits. The dominant manufacturer is the price leader, and the retailer is the follower. For the first stage, the manufacturer sets the high-end product price ph and the wholesale price w to the retailer. The information of manufacturer’s decision is common knowledge. Then in the second stage, the retailer sets the low-end product price pl and the gray product price pg based on the common knowledge from the first stage. Given all the firms’ decisions, the consumers make their buying decisions. (Fig. 2)

UL = VL

(3)

pl

Through incentive compatibility constraint, we could acquire the demand for each market: DhNI , DgNI and DlNI , respectively. In the low-end market, consumers will purchase low-end product if UL > 0 . In the highend market and gray market, consumers will purchase high-end product if UH > 0 and UH > UG , otherwise will purchase the gray product if UG > 0 and UH < UG . Therefore, the demands in each market are ph pg ph pg pg DlNI = 1 pl , DhNI = 1 v (1 ) , and DgNI = v (1 ) . The profit vh h h function of the manufacturer contains three parts: the profits generated from the high-end market, gray market and low-end market respectively. Retailer’s objective function consists of two parts, its profits are from the low-end market and gray market. The manufacturer’s and retailer’s objective functions are given as follows:

5. Effects of IoT technology on Gray Market As mentioned in Section 4.1, we establish three different models and comparatively investigate these three cases: 1) the base case where manufacturer does not apply the IGTS (NI model); 2) the case where manufacturer applies the IGTS and choose the coping strategy A (IPR model); 3) the case where manufacturer applies the IGTS and choose the coping strategy B (IPC model). This section mainly investigates how IoT technology affects the gray market and firms’ profits under different coping strategies.

max

r

max

m

pl , pg

ph , w

= (pl

w ) Dl + (pg

w ) Dg

(4)

= ph Dh + w Dl + w Dg

(5)

The equilibrium results could be worked out by backward induction, as summarized in Table 1.

Table 1 Equilibrium results of each model. NI Model

w

2) 4 2

2(4 1+8

pl

6 2 8 2

1 + 16 2 + 16

ph

16 2 ) 4 2 8 2)

2(2 + 17 1+8

pg

2 (3 + 8

Dh Dg

1 2 + 16 1 2 8

4 + 32

2 2

16 2 38 2 + 160 3 124 4 2 4 2)

r

1+2

m

4(1 + 8 4 + 38 33 2 2 + 16 8 2

4 2 4 2)

2(1 + 8

2

4

12 2 16 2 16 2)

4 + 32

2(2 + 17 + 2

2

4

2

8 2) + (1 + 8 16 2

4 2)

4 + 32 2+2

8

16 + 32

4 + 32 16 32

8(1 + 2

4 2+4 16 2

2

K

+ 32 + 8

2+

8 + 4 2) 8 + 4 2)

( 1

8 (1

32 (1 + 7

9 + 8 2) + ( 1 12 2 + 4 3)

I 2 4 2)

128( 1 + ) (1 + 8

M 64( 1 + ) ( 1

8 + 4 2) 16 2

8 + 4 2)

L 2 4 2)

N 8 + 4 2)

64( 1 + ) ( 1

K = 16 ( 1 + ) (1 + 8 4 2)2 + 2 (1 + 8 4 2)2 ( 1 8 + 8 2 ) J = 32( 1 + ) ( 1 2 + 38 2 160 3 + 124 4 + 2 ( 1 8 + 8 2) 2 (1 + 4 38 2 + 32 3)) M = 32( 1 + ) ( 4 + + 2 38 8 + 33 2) 16 (1 + 7 12 2 + 4 3) + 2 (1 + 16 + 52 2 96 3 + 32 4 ) L = 2 ( + 8( 1 + ) )(1 + 8 4 2)2 + 2 (1 + 8 4 2)2 ( 1 8 + 8 2) + 16 ( 3 5 + 152 2 240 3 + 96 4 ) I = 2 (1 + 40 + 224 2 384 3 + 144 4 ) + 32 (1 + 40 2 + 198 3 284 4 + 124 5) N = 2 (1 + 4 2) 2 ( + 8( 1 + ) )( 1 8 + 4 2) 16 (1 9 + 8 2) + 32 (4 + 34 71 2 + 33 3) + 2 (1 + 16 + 52 84

16 + 12 2)

4 2+ ( 1

J

128( 1 + ) (1 + 8

2

12 2 4 2 16 2 2(2 + 17 + 16 2 ) 1+8 4 2 8 (3 + 8 8 2) + (1 + 8 4 2) + ( 3 4 + 32 16 2 2+

4 + 32 16 + + 16 32 32

8 )( 1 + ) + ( 1 32( 1 + ) ( 1

4

4 + 32

4 2

1+8 8 (3 + 2 + 8

4 2 4 2)

+ 16 + 8 2(1 + 8

2 + + 32 + 8

8 2

1

IPC Model

2 + + 16 + 8

4 2

1+8

Dl

IPR Model

2

8 + 4 2)

96

3

+ 32 4 )

16 + 12 2)

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5.2. IPR Model

demand and gray market demand, but increases high-end market demand. As mentioned above, increases in lead to low-end product price and gray product price increase, of course, the low-end market demand and gray market demand decreases. With the increasing of , although the high-end product price will not be impacted, an increasing number of consumers would like to purchase the high-end product because of the rising gray product price. Besides, it is equally understandable that increases in reduce gray market demand, but increase low-end market demand. Remarkably, the high-end market demand is independent to . Our intuition is that manufacturer’s purpose of imposing the punitive cost of is only to deter gray product demand, but not for improving the high-end product demand. As part (1) demonstrated, the manufacturer raises the high-end product price to balance high-end product demand when increases.

In the previous section, we first investigate the basic model NI, and the numerical simulation of the equilibrium results are given. The simulation results have shown a significant effect of gray product preference on the gray market and firms’ profits. In this section, we study the case where the manufacturer uses the IGTS and chooses the coping strategy A. Manufacturer pays the unit cost of the RFID tag, while she could impose a punitive cost of on per unit of the gray product according to the monitoring data. Similar to Cai et al. (2010), we further 2

16 + 4 2

2 2 and 0 < < assume 0 < < 8 to ensure that equi1 8 +4 2 librium results are all positive, which also means that punitive cost and RFID cost should not be too large, otherwise retailer will never divert the products and manufacturer will never use IoT technology. The purpose of this section is to investigate how punitive cost of gray product and RFID cost affect gray market and equilibrium results. Under such a situation, consumers’ utility values of each type of product do not change, however, both manufacturer and retailer profits will change. The consumers’ utility functions and firms’ profits functions are expressed as follows.

UH = VH

max

r

max

m

pl , pg

ph , w

ph , UG = VH

= (pl

pg , UL = VL

w ) Dl + (pg

= ph Dh + (w

w ) Dg

Dg

)(Dl + Dg ) + Dg

Proposition 2. In the case where manufacturer applies IoT technology and chooses the corresponding coping strategy A, the firms’ profits are impacted by RFID cost and punitive cost as follows: (1) When is relatively low, manufacturer’s profits first decreased then increased with , when is relatively high, manufacturer’s profits always decreased with ; When is relatively low, manufacturer’s profits first increased then decreased with , when is relatively high, manufacturer’s profits always increased with . (2) When is relatively low, retailer’s profits first decreased then increased with , when is relatively high, retailer’s profits always decreased with ; When is relatively low, retailer’s profits increased with , when is relatively high and is relatively low, retailer’s profits first decreased then increased with , when and are both relatively high, retailer’s profits decreased with .

pl (6) (7)

The right side of the Eqs. (6) and (7) Dg represents the total punitive costs for gray product. The RFID costs are contained in the )(Dl + Dg ) . The equilibrium results are middle part of the Eq. (7) (w summarized in Table 1.

Proposition 2 characterizes the monotonicity of the manufacturer’s and retailer’s equilibrium profits. Notice that the analyses of this section are on the basis of Proposition 1. Firstly, we explain the monotonicity of the manufacturer’s equilibrium profits on . When is relatively low, it means manufacturer’s punitive incomes from Dg and high-end product price ph are relatively low, an initial increase of leads to both FRID cost increases and profits decrease from Dg (Dg decreased with ), although it could also little improve the profits generated from ph Dh (Dh increased with , but ph is relatively low), these increased profits are not viable to offset the cost increases and profits decrease. Once increases to a certain extent, make manufacturer profitable because the continued increases in manufacturer’s profits growth from the high-end market outstrips the rising cost of RFID and the profits decreases from the gray market. Thus, the manufacturer’s profits first decreased then increased with when is relatively low. However, when is relatively high, the manufacturer could snatch a high punitive income and retailer has to exert more costs for diverting goods (which also means that fewer products will be diverted to gray market). Increases in could not significantly improve the profits from ph Dh , but dramatically reduce the )(Dl + Dg ) and punitive inprofits from the wholesale channel (w comes Dg (Dl and Dg both decreased with ), and finally result in a decrease of total profits on the manufacturer. Thus, the manufacturer’s profits always decreased with when is relatively high. Then, we explain the monotonicity of the manufacturer’s equilibrium profits on . As Proposition 1 described, a relatively low results in a relatively high gray product demand Dg and a relatively low high-end product demand Dh . Our intuition is that initial increases of bring massive increased punitive profits from Dg (a relatively large Dg ) and also de)(Dl + Dg ) , crease the profits generated from wholesale channel (w however, the magnitude of the increased profits is larger than it of the decreased profits. Once increases to a certain degree, continued increases in lead to a sharp fall in both gray product demand and wholesale price, which represent that there has been a sharp decline in )(Dl + Dg ) + Dg . Although the high-end the profits of the part (w product price increased with , the profits increased on ph Dh could not

Proposition 1. In the case where manufacturer applies IoT technology and chooses the corresponding coping strategy A, the equilibrium prices and demands are impacted by RFID cost and punitive cost as follows: (1) The wholesale price and low-end product price increased with and decreased with ; The gray product price increased with both and ; The high-end product price increased with and independent to . (2) The low-end market demand decreased with and increased with ; The gray market demand decreased with both and ; The high-end market demand increased with and independent to . Proposition 1 characterizes the monotonicity of equilibrium prices and demands and shows the relationship among RFID cost, punitive cost, and equilibrium results. From part (1), we realize that the wholesale price, low-end product price, and gray product price increase as RFID cost increases, while the high-end product price is independent to RFID cost. As the RFID cost increases, the manufacturer has an unambiguous incentive to pass along cost increases by raising the wholesale price. Observing the dominant manufacturer’s action, retailer, as a follower, will make a response by improving the low-end product price and gray product price. Thus, increases in increase wholesale price, low-end product price and gray product price. As we emphasized in Section 3, the manufacturer will not label the RFID tag on the high-end product, so the high-end product price will not be affected by . Part (1) also shows that an increase in reduces wholesale price and low-end product price, but increases gray product price and high-end product price. Through the applying of traceability system, the manufacturer imposes a punitive cost to force the retailer to improve the gray product price, as a result of reducing the gray market demand. Besides, manufacturer, on the one hand, will decrease wholesale price to encourage the retailer to sell more products in the authorized low-end market, on the other hand, she will increase high-end product price to cover losses related to the decreases in gray market demand. Part (2) illustrates that an increase in reduces low-end market 85

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)(Dl + Dg ) + Dg because of the low make up the profit loss of (w high-end product demand caused by a relatively low . Thus, the manufacturer’s profits first increased then decreased with when is relatively low. A relatively high , however, results in a large demand of the high-end product, increase in could substantially improve the profits of part ph Dh ( ph increased with ) which could recoup any profits decreases. Thus, the manufacturer’s profits always increased with when is relatively high. For retailer’s equilibrium profits analysis, part (2) firstly shows that manufacturer profits and retailer profits have the same monotonic characteristic on . Our intuition is that increases in improve the costs of the manufacturer, and the dominant manufacturer could shift part of these costs to the retailer. Thus, the dominant manufacturer and retailer will share elements of the costs increased by RFID finally. That’s the reason why the monotonic characteristic of the retailer’s profits on is the same as the manufacturer’s. For the explanation of the monotonicity of retailer’s equilibrium profits on , the retailer’s profits even increased with once and are relatively low. It means that the manufacturer aggravates the gray market punishment could even improve the retailer’s profits. Our intuition is that a relatively low , which consumers valuation for the gray product is low, gives retailer less motivation to divert products into the gray market, resulting in low gray market demand (a relatively low Dg ). Thus, increases in could not get a higher punitive income from Dg , but improve the profits generated from (pl w ) Dl + (pg w ) Dg because of the increased Dl and (pg w ) . Therefore, the retailer’s profits increased with when and are relatively low. Conversely, it’s not hard to understand why retailer’s profits decreased with when and are both relatively high. Besides, part (2) finally illustrates a non-intuitive result that the retailer’s profits first decreased then increased with when is relatively high and is relatively low. The reason is that a relatively high will lead to a strong incentive to purchase the gray product, and a relatively low will result in a high demand for the low-end market, initial increases of bring great losses for the retailer because of the increasing cost of Dg and the decreasing profits of (pg w ) Dg . While, further increases in reduce the demand for gray market substantially, and result in the decreasing of punitive cost Dg and the increasing profits of the low-end market (pl w ) Dl , which could finally lead to an increase of retailer’s total profits.

The right part of the Eq. (8) represents the utility loss by coping strategy B. The equilibrium results are summarized in Table 1. Proposition 3. In the case where manufacturer applies IoT technology and chooses the corresponding coping strategy B, the equilibrium prices and demands are impacted by RFID cost and penalty utility as follows: (1) The wholesale price, low-end product price, and gray product price increased with and decreased with ; The high-end product price increased with and independent to . (2) The low-end market demand decreased with and increased with ; The gray market demand decreased with both and ; The high-end market demand increased with both and . Proposition 3 illustrates the monotonicity of equilibrium prices and demands and shows the relationship among RFID cost, penalty utility, and equilibrium results. From part (1), we discover that the monotonicity of equilibrium price on is the same as it in Proposition 1. Besides, we also find that increases in reduce wholesale price, lowend product price, and gray product, but increase high-end product price. Our intuition is that the dominant manufacturer is price leader, besides improving the penalty utility is to prevent consumers from purchasing gray product, and corresponding price leverage should also be taken into consideration, she will also lower wholesale price to encourage retailer to sell more products in authorized low-end market and at the same time increase high-end product price to increase the profit margins. The information of manufacturer’s decision is common knowledge, she will react and decrease gray product price to reduce the adverse effect caused by increased and decrease low-end product price to maximize the demand for low-end market. From part (2), we emphasize that the monotonicity of equilibrium demand on has not changed. Besides, increases in decrease gray market demand but increase both low-end market demand and high-end market demand. Indeed, manufacturer improves penalty utility precisely to deter gray market and encourage retailer to sell more product in low-end market. Apparently, that leads to gray market demand decreases, and both lowend market demand and high-end market demand increases. Remarkable, from Proposition (1) we realize that punitive cost has no direct effect on high-end market demand, but penalty utility has a beneficial effect on it. It’s the essential difference between coping strategy A and B.

5.3. IPC Model

Proposition 4. In the case where manufacturer applies IoT technology and chooses the corresponding coping strategy B, the firms profits are impacted by RFID cost and penalty utility as follows:

In IPR model, the manufacturer uses the IGTS to monitor the gray market and adopts the coping strategy A. The results of the analysis show the monotonicity of gray market and firms profits on punitive cost and RFID cost. In this section, the manufacturer chooses the coping strategy B, punishing the consumers who purchase the gray product by canceling the after-service or charging extra fees. Thus, the consumers who purchase the gray product will have utility loss . Besides, the manufacturer also needs to pay the unit cost of the RFID tag. Likewise, to avoid manufacturer never applies IoT technology or consumers never purchase the gray product, it is realistic to assume

(1) When is relatively low, manufacturer’s profits first decreased then increased with , when is relatively high, manufacturer’s profits always increased with ; Manufacturer’s profits always increased with . (2) When is relatively low, retailer’s profits first decreased then increased with , when is relatively high, retailer’s profits always increased with ; When is relatively low, retailer’s profits always decreased with , when is relatively high and is relatively low, retailer’s profits first decreased then increased with , when and are both relatively high, retailer’s profits always decreased with .

16 + 4 2

0 < < 16 4 2 and 0 < < to ensure equilibrium results 1 8 +4 2 are all positive (Cai et al. 2010). This section is for investigating how penalty utility and RFID cost affect the gray market and equilibrium results. In this case, the consumer utility function and firms’ profits function are given as follows. UH = VH

ph , UL = VL

UG = VH

max

r

max

m

pl , pg

ph , w

= (pl

From part (1), we first explain the monotonicity of the manufacturer’s equilibrium profits on . When is relatively low, which means the penalty utility is not very large that gray market demand Dg has not been deeply affected (gray product demand Dg is not very low and high-end product Dh is not very large), initial increases in the

pl

pg

8 +8 2

(0, ) decrease manufacturer’s profits from the interval 1 8 +8 2 )(Dl + Dg ) and increase manufacturer’s profits from the part of (w part of ph Dh . This increased profits will be lower than the decreased profits. However, further increases in the interval

(8)

w ) Dl + (pg

= ph Dh + (w

w ) Dg

)(Dl + Dg )

(9)

8 +8 2

16 + 4 2

( , ) change the situation that the increased 1 8 +8 2 1 8 +4 2 profits from the part of ph Dh will be higher than the decreased profits

(10) 86

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guarantee the profits continuously increase. Part (2) first shows the same monotonicity of the manufacturer’s and retailer’s profits on . As we have stated in Proposition 2, this can be explained by the fact that the dominant manufacturer will partly pass along RFID cost, and the retailer has to share the RFID cost with the manufacturer. Thus, their equilibrium profits share a similar monotonicity on . Then, part (2) shows the monotonicity of the retailer’s equilibrium profits on . When is relatively low, the consumers have a relatively low preference for the gray product, which results in low gray market size. Thus, increases in have not severely affected the retailer’s profits from the gray market, but instead of increasing retailer’s profits from the low-end market. When is relatively high and is relatively low, the retailer’s profits first decreased then increased with . As we explained in part (2) of Proposition 2, a high and a low result in a relatively large gray market size and low cost of IoT application, initial increases of bring great losses for retailer’s profits from the gray market because of the slump in gray market demand. While, further increases in begin to considerably increase retailer’s profits from the low-end market, which could exceed the profits losses from the gray market. Finally, when and are both relatively high, in addition to the profits losses from gray market caused by increasing , the manufacturer will also transfer the relatively high cost of the RFID to the retailer, so retailer’s profits always decreased with . Fig. 3. Comparative analysis of inhibitive effects of coping strategies on gray market.

6. Comparative Analysis

)(Dl + Dg ) . Conversely, when from the part of (w is relatively high, from Proposition 3 we realize that a growing number of consumers will prefer to choose the high-end product and manufacturer will set a relatively high price ph . With the increasing of , the high-end market demand will keep growing and the total profits of the manufacturer will continue to increase. Then, we explain the monotonicity of the manufacturer’s equilibrium profits on , part (1) shows that in4 2 always increase the creases within a specific range of 0 < < 16 manufacturer’s profits. With the increasing of , the manufacturer could rake in exorbitant profits from the high-end market, meanwhile, she could also use the price leverage to adjust the wholesale price, to

As illustrated in the previous section, we investigate how IoT technology and manufacturer’s coping strategies affect the gray market. We first conclude in the IPR model that IoT technology has significant implications for gray market size, while it is not always beneficial to manufacturer’s profits, sometimes even helpful to retailer’s profits. Besides, the punitive cost is not as higher as better because it sometimes may also harm the manufacturer’s own interests. Then in IPC model, our analysis shows an essential distinction between coping strategy A and B that punitive cost does not directly affect the highend product demand, but penalty utility has a beneficial effect on the demand of the high-end product. The objective of this section is to study what changes would IoT technology bring to the gray market, and the impacts. We save the comparative analysis of firms’ profits for

Fig. 4. IPR model vs. NI model, where 87

= 0.5.

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Fig. 5. IPC model vs. NI model, where

numerical study.

= 0.5.

shows, three boundary lines divide the results into six regions. The 2 2 , are out of conregions, which above the boundary line 8 sideration because of our assumption. And the coping strategy A can inhibit gray market more markedly in Region Ⅰ, while the coping strategy B has a more markedly inhibitive effect in Region Ⅱ and Ⅲ.

Proposition 5. Through the comparison with NI model and IPR model (NI model with IPC model), we find that, under both coping strategies A and B, the application of IoT technology not only lowers the gray market size, but also improves the high-end market size. This proposition demonstrates the effectiveness and practicality of the IGTS from theoretical aspects. It is an important conclusion which implies that the application of IoT technology is an effective method to deter the gray market size. However, it is not always beneficial to the manufacturer’s profits. That may be one of the important reasons why there are now few firms to apply IoT technology to manage the gray market in reality. In summary, this finding provides enlightening value for managers: IoT technology can play an essential role if managers control the costs of IoT technology reasonably. We will further express this more qualitatively in the numerical study.

7. Numerical Study To thoroughly investigate the impacts of IoT technology, we present numerical simulation to supplement our propositions. The main researches of this section are as follows: 1) the comparative analysis of firms’ profits; 3) the monotonicity analyses of the equilibrium results of NI model. 7.1. Comparative Analysis of Firms’ Profits

Proposition 6. Through the comparison with IPC model and IPR model, the results show that the influence extent of coping strategy A and B on gray market are different: When penalty utility and punitive cost are both relatively low, or only penalty utility is relatively high, the inhibitory effect of coping strategy B is more pronounced than coping strategy A, otherwise the inhibitory effect of coping strategy A is more pronounced than coping strategy B.

In this section, we conduct a comparative analysis to investigate the changes in firms’ profits before and after applying IoT technology. We set = 0.5. First, as shown in Fig. 4, it’s a numeric result generated from the comparative analysis of IPR model and NI model. For manufacturer’s profits, we can see that the application of IoT technology decreases manufacturer’ profits when is low enough or high enough (the blank area). Instead, in the shaded area, the application of IoT technology increases manufacturer’ profits. Our intuition is that once the punitive cost is low enough, of course, the application of IoT technology will not be profitable because it has not effectively inhibited the gray market, but raised its costs. While, when the punitive cost is high enough, the application of IoT technology will not be profitable either, because it is unwise to lose the whole gray channel profits. As Proposition 1 part (2) shows, a high enough punitive cost leads to an enough low demand of gray market (the gray market demand decreased with ), and these gray product demand losses have not replaced with high-end product since the high-end market demand independent to . For retailer’s profits, finding shows that the application of IoT technology is not always harmful to retailer, which means that if manufacturer takes the appropriate coping strategy, the use of IoT technology on gray market management could achieve a win-win. And it also shows the possibilities to apply IoT technology on reality management

Proposition 6 analyzes the influence grade of two different coping strategies and the applicable case of each coping strategy is studied through the comparison of the models. The essence of this finding provides enlightening value for the firm’s strategies to use in gray market management issue. Results show that the implementation of coping strategy B is more effective in deterring the gray market when penalty utility and punitive cost are both relatively low or only penalty utility is relatively high. Besides, the coping strategy A can be more effective in the situation where penalty utility is relatively low but punitive cost is relatively high. Our intuition, of course, is that the higher the punishment, the better the inhibition effect on gray market. As mentioned above, however, a better inhibition effect does not always beneficial to the manufacturer. To present the results visually and precisely, we set = 0.5 and summarize these results in Fig. 3. As Fig. 3 88

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Fig. 6. IPC model vs. IPR model, where

of gray market. Then, as Fig. 5 illustrated, comparing IPC model with NI model, the numeric result also shows that IoT technology is not always beneficial to manufacturer or harmful to retailer. In the shaded area, the application of IoT technology is advantageous to both manufacturer and retailer. Instead, both firms will be worse off in the blank space. Finally, we make comparative analyze on IPR model and IPC model. As shown in Fig. 6, manufacturer selects the coping strategy A is more profitable in the blank area where is relatively low and is not too large. Whereas in the shaded area, the coping strategy B is more profitable. Besides, Fig. 6 also shows that the application of IoT technology may also benefit retailer, that could be one of the underlying reasons for

= 0.5.

the successful implementation of IoT technology on gray market management. Remarkably, the simulation result of retailer’s profits comparison between IPR model and IPC model is more mixed because of the and (as shown in Proposition 2 and complicated influence of Proposition 4), manufacturer should develop different strategies to deal with the gray market issue in different situations. In a summary, although the above numerical studies are the partial simulations of results, these illustrate two important things: 1) the application of IoT technology is not always profitable to manufacture or always harmful to retailer, and manufacturer takes the appropriate coping strategy with reasonable variable cost of IoT technology could result in a win-win

Fig. 7. Sensitivity analysis.

89

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situation, and all these prove the applicability of IoT technology on gray market management problem from a market perspective; 2) Mapping out the coping strategies scientifically according to the different situations, manufacturer will raise the gray market managing efficiency and optimize the channel market.

about application of IoT technology in supply chain (Demiralp et al. 2012; Native and Lee 2012; Xu 2011; Yan & Huang 2009), we expand the scope of the application of IoT technology in supply chain with gray markets, while the previous literature mainly focuses on the food supply chain (Pang et al. 2015; Liu et al., 2008; De-an, Cui-feng, & Xianwang, 2009; Yan et al. 2013) or supply chain inventory problem (Chen et al. 2014; Fan et al. 2015; Wang, Hu, Chang, & Ding, 2018). Another difference is that we describe the implementation process of IoT technology in gray market monitoring by the design of a traceability system. However, this has been underestimated in previous studies. Such as, Chen et al. (2014) and Wang et al. (2018) argue that IoT technology can eliminate the misplacement in the supply chain, whereas few of them have introduced the implementation process of IoT technology on the misplacement problem of the supply chain.

7.2. Sensitivity Analysis of vh and vl This section is to conduct a sensitivity analysis of vh and vl of firms’ profit, as we assume that vh = 8 and vl = 1 to simplify the model in Assumption 5. Since the results of sensitivity analysis for each model are similar, we only conduct the sensitivity analysis for NI model. As Fig. 7 shows, the manufacturer’s and the retailer’s profits increase with the increasing of vh and vl . This is because the higher the consumer’s willingness to pay, the higher price and demand of product will be. In addition, we can see that different parameters vl and vh do not change the monotonicity properties of the firms’ profit function, which implies that different parameter setting will not affect our main results.

8.3. Methodological discussions In this paper, we consider the two types of manufacturer coping strategies and establish three game models to investigate the impact of IoT technology. However, in the previous studies (Rekik, Sahin, & Dallery, 2008; Camdereli & Swaminathan 2010; Heese 2007; Chen et al. 2014; Fan et al. 2015; Wang et al. 2018), they usually use a single parameter to model the role of IoT technology on the supply chain. Such as Rekik et al. (2008) showed that IoT technology can reduce the inventory misplacement and they defined a parameter as the effect of misplacement errors. In this paper based on the IoT-based traceability system, the manufacturer has two options: to punish retailer based on the accurate volume of the gray products or to punish the consumers who purchase the gray product. We make a comparative analysis of three different models and generally analyze the impacts of IoT technology. The results show that IoT technology can effectively cut down the gray market size since the manufacturer can obtain gray market information through the application of IoT technology and conduct coping strategies. Coping strategy A increases the cost of parallel importation directly and coping strategy B decreases consumers' surplus from purchasing the gray product. Whichever coping strategy is adopted, it can inhibit gray market demand effectively. Interestingly though, IoT technology is not always harmful to the retailer, since the manufacturer takes the appropriate coping strategy (A or B) based on punitive cost and penalty utility could result in a win-win situation.

8. Discussion The aim of this paper is to introduce IoT technology into the gray market management and to investigate its impacts. An IoT-based traceability system is designed and three game models based on monitoring dates are constructed. This research not only expands a new application field in the use of IoT technology but also broadens the horizons of the gray market research. 8.1. Contributions to the management means of gray market This research enriches the methods for managing the gray market and even solves a core problem. It is generally known that the gray market has its concealment. The difficulty is how to capture information about parallel importation. Despite an increasing number of research on the methods for gray marketing management, previous research has very little involvement in this core question. Some scholars offer suggestions from the perspective of the legal status (Hintz 1993, Duhan & Sheffet 1988), whereas it is very difficult to regulate the gray marketing according to the current legal system, especially in underdeveloped markets (Prince & Davies, 2000; Gallini & Hollis 1999), because the gray market is a legal concept of uncertainty is difficult to give a clear legal definition. Also, some scholars insist that the manufacturer should control gray market activities by signing contracts with the retailer (Su & Mukhopadhyay 2012). In the long-term, it is not enough to safeguard the legitimate interests of the manufacturer, particularly when the gray market information has a nature of concealment. The retailer would have incentives to sell in gray market if profitable (Korobkin 1998). Recently, there are several papers made research on the methods from the perspective of marketing (Zhang 2016; Iravani, Dasu, & Ahmadi, 2016). All these marketing means are efforts to lure more consumers into the authorized channel, but there are still no clear strategies to combat the gray marketer. This paper differs from the foregoing and makes the contribution to gray market management from the IoT technology perspective, which can hit the source of the problem. IoT technology is an effective method to improve market channel transparency by tracking items and providing real-time product flow information (Fan, Tao, Deng, & Li, 2015; Li, Liu, Liu, Lai, & Xu, 2017).

9. Conclusion In this paper, we design an IoT-based traceability system to realize the application of IoT technology in gray market. There are two application layers in our system to ensure the tracking effect. In the Logistics System, once the retailer changes the logistic route and diverts the products, the data will be saved to the Real-time Database Server. In the IoT Devices Executive System, once the labeled products are sold and out of the RFID reader's scanning area, the manufacturer could obtain the real-time demand of the low-end market and calculate whether or not retailer diverts the goods and how many. Based on the supervisor data, then we establish three models to comparatively analyze the impacts of IoT technology on gray market and firms' profits. We consider that the manufacturer has two coping strategies: punishing retailer based on the accurate volume of the gray products or punishing the consumers who purchase the gray product. The main results show that IoT technology can inhibit the gray market. However, IoT technology is not always profitable to manufacture or always harmful to retailer. If the manufacturer takes the appropriate coping strategy, a win-win situation can be achieved. Our findings also reveal the distinction between the manufacturer’s coping strategy A and B. Coping strategy A has no direct effect on high-end market demand while coping strategy B has a beneficial effect on it (as shown in IPC model). Finally, we take a numerical study to intuitively show the impacts of IoT technology. We find that IoT technology is like

8.2. Extensions to the application field of IoT technology We introduce IoT technology into gray market management and design an IoT-based gray market traceability system, which can enable the manufacturer to capture the gray market information. The IoTbased traceability system can solve the difficulty of the gray market problem to a certain extent. To note that, compare with the literature 90

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a double-edged sword, improving the integration efficiency of the supply chain and giving firms opportunity to excavate the potential profits, but also bringing a new problem of how to control the cost of IoT technology. As this paper introduces IoT technology into gray market research for the first time, some future research could be further extended. Firstly, this paper preliminarily designs an IoT-based traceability system, a more detailed design of the system implementation process is still needed. For example, a maintenance model is needed to improve the system traceability (Gharaei, Naderi, & Mohammadi, 2015; Duan, Deng, Gharaei, Wu, & Wang, 2018). Also, the further design of IoTbased traceability system should be based on the perspective of strategic management (Sobhanallahi, Gharaei, & Pilbala, 2016a; Sobhanallahi, Gharaei, & Pilbala, 2016b). Secondly, this paper considers a retailer who conducts the gray markets, the impacts of IoT

technology are likely to be different in the situation where a third party parallel imports (Xiao et al., 2011). Thirdly, this paper mainly focuses on the analysis of the influence of IoT technology on the gray market, a game analysis of strategy for IoT adoption and parallel importation can be conducted in future research. Furthermore, it will be a meaningful extension for multi-level supply chain with a risk-aversion manufacturer (Tsay 2002). Acknowledgement We would like to thank the guest editor and the anonymous referees for their insightful comments and suggestions which improved our paper greatly. This work is supported by the National Natural Science Foundation of China (grant No. 71531009, 71271093)

Appendix Proof of Proposition 1. . Remember that the ranges of parameters are given by the assumptions: 1/8 <

0<

<

2

(1) We simply need to demonstrate such inequalities: w/ ph / = 0 . By algebra,

= pl

1 2 1 4

> 0,

1

= pl

ph

< 0,

4 2

1+8

ph

= 0,

pg

=

1 4

4

Dg

=

1 32( 1 + )

2 + 16

Dg

< 0,

=

1 8 + 4 2)

2( 1

> 0,

pl /

< 0 ; pg /

> 0 , pg /

2 2 , and

> 0 ; ph /

> 0,

4

= > 0, = >0 = > 0, = <0, 1+8 4 2 4 + 32 16 2 (2) For this part of Proposition 1, we also prove that Dl / < 0 , Dl / the results as follows: Dh 1 D D 1 D 1 = 32 32 > 0, h = 0 , l = 4 < 0, l = 2 > 0 2

< 0 ; pl /

<8

>0

4 2

1+8 pg

> 0 , w/

2 /2 , 0 <

<

16 + 4 2 . 1 8 +4 2

> 0 ; Dg /

< 0 , Dg /

< 0 ; Dh /

> 0 , Dh /

= 0 . By algebra, we get

8

<0

Proof of Proposition 2. . (1) The monotonicity properties for manufacturer and retailer profits function are proved by algebra as follows. For manufacturer profits function, +8 2

8 +8

8

2

= , m = 2( 1 8 + 4 2) 32 32 2 Through the analysis of the sign of inequalities, we get the corresponding constraints as seen in the results below, m

if 0 < if 0 < if

4

<

4

<

4

1

34 2 + 32 3 &0 8 +8 2

1+2

<

<

34 2 + 32 3 1 8 +8 2

<

8 +8 2 , 8 +8 2

1

34 2 + 32 3 8 +8 2 & 1 8 +8 2 1 8 +8 2

<

<

2 2 &0 <

<8

8

<

2

2

then

m/

16 + 4 2 , 1 8 +4 2

16 + 4 2 , 1 8 +4 2

<0 then

then

m/ m/

which means that manufacturer’s profits first decreased in 0 <

0<

<

4

34 2 + 32 3 . 1 8 +8 2

Then, let

if 0 < if 0 < if 1 <

<

<

1

8 +8 2 8 +8 2

And manufacturer’s profits always decreased in 0 < 1

1 1& 2 (

< 2 ( 1 + 8 ), then 1+8 ) <

16 + 4 2 1 8 +4 2

<

&0 <

1 ( 2

m/

<

2

16 + 4 2 1 8 +4 2

>0

+ 16

8

4

+ 16

8

4

2 2

2),

+4

2),

<0

and

+8 2

8

2

if 0 < if 0 < 4

<

4

<

4

1

34 2 + 32 3 &0 8 +8 2

1+

<

<

34 2 + 32 3 8 +8 2 & 1 8 +8 2 1 8 +8 2

34 2 + 32 3 1 8 +8 2

<

<8

+4 +8

8 +8 2 , 8 +8 2

1

<

2 2 &0 <

< <

2

2

38 2 8 4 2)2

then

2 + 32 3

r/

16 + 4 2 , 1 8 +4 2

16 + 4 2 , 1 8 +4 2

then

m/

It’s also proved the part (3) of the Proposition 1. (2) By the same way, we get the first derivative of retailer profits versus

= , r = 2(1 + 8 64 64 2 we get the inequality results as follows,

+4

then

>0

8 +8

<

1 ( 2

then increased in

m/

r

if

<0

< 0 , we also get the inequality results as follows,

m

< 1&0 < <

>0

<0 then

then

r/ r/

>0 <0

91

1

when

8 +8 2 8 +8 2

<

<

is relatively

2

16 + 4 8 +4 4 high 1 1

2 2

when

34 2 + 32 3 8 +8 2

is relatively low

<

<8

2 2.

Computers & Industrial Engineering 136 (2019) 80–94

L. Ding, et al. 1

if 8 <

< 0.26&0 <

if 0.26 < if 0.26 < if 0.26 <

<

1 2

<

1 2 1 2

<

Where ¯ =

<

&0 < &0 <

16 + 4 2 , 8 +4 2

+ 16

then

r/

<

2 1

8 +8 2 , 8 +8 2

<

2 1

8 +8 2 1+4 , 8 +8 2 1

8 +8 2 8 +8 2

2 1

&

1

<

8

2

<

, 8 +4 2

1

2.

+4

38 2 + 32 3 , 8 +8 2

then

r/

<0

¯ , 2

then

r/

>0

38 2 + 32 3 8 +8 2

16 + 4 2

<

4

0<

>0 1+4 1

<

then

<

r/

<0

This completes the proof.

Proof of Proposition 3. . we first emphasize that 1/8 <

2 /2 , 0 <

<

(1) We simply need to demonstrate such inequalities: w/ ph / = 0 . By algebra, 1 2

= pl

> 0,

1 2(1 + 8

= pl

ph

< 0,

4 2)

ph

= 0,

pg

2

=

Dg

1

1 32( 1 + )

=

Dg

< 0,

Proposition 4.

=

16 2

4 + 32

1 16 + 12 2 32 (1 + 7 12 2 + 4 3)

4 2 , and 0 < < 0 ; pl /

<

> 0 , pl /

1

16 + 4 2 . 8 +4 2

< 0 ; pg /

> 0 , pg /

< 0 ; ph /

> 0,

>0

4 2 3 16 + 12 2 4 + 32 16 2

1+8 pg

1 4

> 0 , w/

= > 0, = = > 0, = <0, 4 + 32 16 2 (2) By the first partial derivatives, we get the results as follows: Dh 1 D 1 D 1 D 1 = 32 32 > 0, h = 32 32 > 0 , l = 4 < 0, l = 1 4

< 16

<0 >0

<0

.

(1) By algebra, we get the first partial derivatives of manufacturer’s profit function as follows: m

+

=

8 +8 32

+8 2 32 2

2

8

First, it’s easily to prove

if 0 <

8 2&0 <

<8

if 0 <

8 2&

8

if 8

2

8

<

1

< 16

,

m

8 +8 2 , 8 +8 2

1

8 +8 2 8 +8 2

<

<

2 &0

<

<

4

4 2)

32( 1 + ) ( 1

8 (1 9 + 8 2) 8 + 4 2)

> 0 . Through the analysis of the sign of inequalities, we also get the follow constraints:

m

<

2 + (1 + 8

+4

=

then

m/

16 + 4 2 , 1 8 +4 2 16 + 4 2

, 8 +4 2

1

<0 then

m/

>0

then

m/

>0

(2) By algebra, we get the first partial derivatives of retailer’s profit function as follows: +

8 +8

+8 2

2

8

2 + (1 + 8

+4

, r = = 32( 32 32 2 we get the inequality results as follows, r

if 0 <

<

if 0 < if

1

< 8 +8 2

Where

1

if 0 <

if 0.40 < if 0.40 < Where

2

<

<

=

<

+8

+8 &0 8 +4 2

1

+ 16

8 <

4 1

8

8

16 + 4 2

< 0.20&0 <

if 0.40 <

<

8 +8 2 & 1 8 +8 2

8 +8 2 1

8 +8 2 &0 8 +8 2

8

< 2

<

1,

then

then

r/

10 + 89 8 + 57

<

1 2

&0 <

10 + 89 8 + 57

120 2 + 36 3 & 2 96 2 + 36 3

<

1 2

10 + 89 & 8 + 57

120 2 + 36 3 96 2 + 36 3

24

40 2

=

16

<

2,

+8 <

r/

<

then 1,

r/

then

<0 r/

>0

>0

<0

&0 <

1 2

2

+8

120 2 + 36 3 &0 96 2 + 36 3

<

2

8

8 (1 9 + 8 2) 8 + 4 2)

2.

+4

16 + 4 2 , 8 +4 2

2

4 2)

1+ ) ( 1

<

< <

<

2,

<

16 + 4 2 , 1 8 +4 2

then

3,

r/

then

then

r/

16

4 + 768 5

56 2 + 1216 3 + 64 3 1920 4 1 + 40 + 224 2 384 3 + 144 4 4 2 + 4 2.

<0

r/

>0

<0

+ 16 8 3 = This completes the proof.

Proof of Proposition 5. . Let Di j (i = g , h , j = IPR, IPC , NI ) denote the demand of market i in model j . By algebra, we can easily get results as follows:

DhIPR

DhNI =

32

32 + 32 32

> 0 , DgIPR

= > 0, This completes the proof. DhIPC

DhNI

DgIPC

DgNI = DgNI

=

8 + 4 2) < 8 + 4 2) 4 2 12 2

16 ( 1 + ) + ( 1 32( 1 + ) ( 1 + + 8 + 16 32 (1 + 7

12 2 + 4 3)

0

<0

Proof of Proposition 6. As the proof process of Proposition 3, we can get the following result,

DgIPC

DgIPR =

16 ( 1 + ) + ( 1 16 + 12 2 ) 32( 1 + ) ( 1 8 + 4 2)

Through the analysis of the sign of inequalities, we get the following constraint inequalities,

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L. Ding, et al.

if 0 <

<

if 0 < if

128 2 + 160 3 32 4 &0 1 16 + 12 2 128 2 + 160 3 32 4 & 1 16 + 12 2

128 2 + 160 3 32 4 1 16 + 12 2

<

< 16

<

< 16 + 12 16 + 16 2

4 2&0 <

16 + 12 16 + 16 2 2

< <8

2

<8

, thenDgIPC

DgIPR < 0

2 2, thenDgIPC 2 2, thenDgIPC

DgIPR > 0

DgIPR < 0

This completes the proof.

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