Optimizing Inventory Management Cost: Case of Simap

Inventory management is an important phase of total quality control. The surgical instruments manufacturing industry is one of the export-oriented sectors in Pakistan; there is a need to improve its business operations. This study focuses on comparison of eight techniques for the inventory management; data for 12 months was curated in 2017, from 75 firms operating in Sialkot. Statistical analysis was conducted to compare the following eight inventory management techniques: Lot for Lot, Wagner-Whitin, Least Unit Cost, Least Total Cost, Silver-Meal, Economic Order Quantity, Periodic Order Quantity, and Part Period Balancing. The Lot for Lot, Least Unit Cost, Least Total Cost, Sliver-Meal, and Wagner-Whiten, were found better than Economic Order Quantity, Periodic Order Quantity, and Part Period Balancing; Periodic Order Quantity is better than Economic Order Quantity which in turn is better than Part Period Balancing. One must keep in mind that statistical out-performance of one technique against the other should not consume the decision-makers, rather the decision should also include the practicality of the business operation.

Introduction
Inventory management is an important factor for maximizing the profits of a firm; firms with higher profit volumes are able to acquire predominant market share in the long run. It is because of the presence of global supply chain and the importance of inventory management in determining the firms' performance that the overall idea of Total Quality Management (TQM) remains incomplete without optimizing the inventory management practices. Although the ideas of Supply Chain Management (SCM) and Total Quality Management (TQM) have different origins; nevertheless, both converge to the ultimatum of enhancing the organizational competitiveness by meeting customer satisfaction goals. The idea of efficient SCM and TQM enables the firms in developing countries to help their countries in strategizing for the achievement of the Sustainable Development Goals; indicator 8a, 9.3, 9.5, 9b, 16.3, and 17.11 are aligned with the global supply chains for the developing countries (ILO, c1996-2019. In this backdrop, inventory management should be considered important by the firms working in Pakistan. According to Kotler (2002), inventory management is a process that encompasses all the activities that are involved in development and management of inventory levels for the raw materials, semi-finished materials, and finished good; this ensures timely availability of adequate supplies while Deveshwar and Dhawal (2013) emphasized the need to minimize the costs of excess or under stocks. The history of inventory management is as old as the early ages, buying and selling has always taken place in one form or the other; nonetheless, the Industrial Revolution and the rapid diffusion of technology in businesses, has enabled the stakeholders to maintain and analyze longitudinal data necessary for adopting cost-efficient strategies. Although there is no paucity of literature on advanced inventory management techniques; however, there is a need to present simple inventory management techniques to small scale firms because they lack the skillset and motivation to understand complex techniques. It must be kept in mind that adoption of innovative inventory management techniques relies upon adoption of technology for digital integration in all the business functions both within and outside the organization; nevertheless, small scale firms are hesitant towards this adoption. Prause (2019) analyzed the determinants of adoption of the Industry 4.0 technology by using data of 38 manufacturing companies working in Japan; it was found that small and medium-sized firms are late in adopting the Industry 4.0. Small and medium-sized firm are unable to evaluate the long-term gains from adoption of advanced information and communication technologies (ICT) (Lu et al. 2019). Given this backdrop, this study is two-pronged i.e., it aims to achieve following two goals: evaluate the optimum inventory management technique and educate the small and medium size surgical firms that there are simple techniques available whose calculations can be done in Microsoft Excel; it has been discussed by Fadhil and Fadhil (2011), that small and medium-sized firms prefer to adopt ICT only if it requires use of simple software, such as Microsoft Excel. Since the optimality check of the techniques discussed in this study, can be checked in Microsoft Excel, firms can recoup the optimality of the techniques; optimality check may be applied at the end of each calendar year or whenever the underlying assumptions of the adopted technique, are violated.
An optimum inventory policy strategizes about the best time to order the input(s) from the supplier(s), and the magnitude of the order, this ensures a cost-efficient mechanism of input into finished products; such a policy circumvents the inventory costs that the firm may face due to physical storage capacity, deterioration of stock or obsolescence, incidence of theft, and hiring of manpower for managing the inventory in terms of receiving, unpacking and inspecting the lots (Muller 2003). The benefits of optimizing the inventory management practices are unequivocally promising because ignoring this important aspect in the supply chain can cause delay disutility i.e., the disutility of the demander when the supplier is unable to deliver the product in time; the delay disutility is directly related to the valuation of the order i.e., larger the order more is the intensity of disutility faced by the demander, in case of the supplier's inability to deliver the order (Chen and Shi 2019). Inventory management is crucial for all suppliers (Muller 2003); nevertheless, its importance is even more inevitable for small-scale firms that face constraints of a diminutive cash flow, lack of resources for technological innovations that can modernize the supply chain, and an intractable lead time (Atnafu, and Balda 2018).
Optimum inventory management ensures better quality of products which in turn establishes a brand image and a sustainable customers base (Lane and Richey 2006;Sung et al. 2010;Banerjee 2016). Inventory management is even more important for the surgical instrument manufacturing firms because these firms serve the demands of a life-saving sector i.e., the health sector; if these firms are able to adopt optimum inventory management techniques, then they can focus more on the quality of products. Many international surgical brands have outsourced manufacturing units to Pakistan (Nazir and Mahmood 2018), Pakistani artisans are appreciated worldwide; nonetheless, it is unfortunate that the surgical sector of the country has failed to develop international brands (Khan 2016). The fact that annually, on average, the surgical industry produces more than 170 million pieces (Khan 2016), alludes towards the export potential of this industry; nevertheless, the surgical industry of Pakistan has merely reduced to a vendor for the international brands that decide to outsource their manufacturing operations, in Pakistan (Barlow 2016;Nazir and Mahmood 2018). In an overly competitive world, no industry can survive without adopting efficient processes in all the phases of production and supply-chain; the position of the surgical sector of Pakistan will be challenged from competition from Mexico and China, both these countries are advantaged in terms of an abundant supply of cheap labor, and innovative technologies required to optimize the production phases (Gerefi 2009).

Research Problem
To evaluate the optimum inventory management technique for the surgical manufacturing firms in the Sialkot region of Pakistan.

Significance of research
According to the Lancet Commission on Global Surgery, approximately 17 million people lose their lives each year, due to lack of timely surgical care (Meara and Greenberg 2015). The surgical manufacturing industry of Pakistan has fulfilled the global demands for surgical instruments in the global value chain; nevertheless, the recent years have witnessed a significant slowdown due to increased competition from new entrants in the market, production inefficiencies, and suboptimal human capital resources . The significance of this research is that it can enable the surgical equipment manufacturing industry to obtain a dominant share in the global market of surgical equipment. By using data from the surgical manufacturing firms working in Sialkot, this study provides a factual profiling of the inventory management practices in these firms. This study compares following lot-sizing techniques: Lot for Lot, Wagner-Whitin, Least Unit Cost, Least Total Cost, Silver-Meal, Economic Order Quantity, Periodic Order Quantity, and Part Period Balancing, to unveil the appropriate lot-sizing techniques for the surgical equipment manufacturing companies of Sialkot region. Some researchers have compared some of the aforementioned inventory management techniques, however, they used hypothetical data on demand patterns; some examples are Wemmerlöv, (1982); Veral and LaForge (1985); Dellaert and Jeunet, (2003); Ho and Ireland (2012).
There has been evidence that in Pakistan, the demanders of health sector products suffer due to inability of suppliers to deliver the products in time, for instance, Atif et al. (2019) conducted a qualitative enquiry of the factors responsible for medicines shortage in Pakistan, they concluded that one of the key avenues that require improvement, are inventory management system, regulations of manufacturers, and price control. This study attempts to fill the void in the literature, although researchers have investigated the determinants of productivity (Chaudhry 2011;Ikram et al. 2016); nonetheless, there is a dearth of studies that compared a wide variety of inventory management techniques to propose an optimum course of action for the supply chain. This study shall enable the surgical equipment's manufacturing firms to choose the optimum inventory management technique to maximize their profits. Given the fact that technology adoption in the supply chain is limited by barriers, such as technical incapability, unwillingness for technology adoption, human & capital resources, cybercrimes, lack of uniform tagging standard followed by the RFID companies, and government's legal regulations, limit the possibilities of RFID adoption, human assisted inventory management techniques should be a point of discussion for the firms in Pakistan.

Literature Review The Surgical Instruments Manufacturers Association of Pakistan (SIMAP)
The surgical instruments manufacturing firms work under the umbrella of the Surgical Instruments Manufacturers Association of Pakistan (SIMAP) which was established in 1950; nevertheless, the history of these firms is set under the British colonial era in the Indo-Pak subcontinent. The number of firms registered with SIMAP is 3900 while 1.5 million workers are engaged in these firms, meeting national, and international demand. Researchers have analyzed the determinants of penetration in foreign country, productivity, and agglomeration economics for the cluster of surgical equipment's manufacturing firms working in Sialkot. Ikram, et al. (2016) analyzed the technical efficiency of small and medium sized enterprises working in the surgical instruments cluster of Sialkot; by employing the Cobb-Douglas production function, and stochastic frontier analysis, it was established that this cluster is functioning in technically inefficient manner, and it is experiencing constant returns to scale. One of the determinants of this discouraging scenario, is the supply-side inefficiencies; proper supply of intermediate inputs is crucial to the performance of this sector (Ikram et al. 2016). Pakistan exports surgical instruments to different countries; major trading partners are given in Table 1 (TrendEconomy 2020). The top five global exporters of surgical instruments are the European Union, the USA, China, Switzerland, and Mexico; the percentage share in different categories ranges from 80 to 90 percent . Pakistan had an advantage of cheap labor and the historical background of exports during World War II; nonetheless, these factors are now less relevant because of the entry of many competitors in this field. Pakistan has recently experienced that its surgical instruments exports are plateauing . Pakistan needs to explore more potential markets for the exports of surgical instruments; these efforts include a need for efficient inventory management practices. Chaudhry (2011) analyzed the choice of contracting institutions for the purchase of inputs by the surgical equipment manufacturing firms in Sialkot. It was observed that these firms have access to a multiplicity of suppliers; nevertheless, almost fifty percent of the sampled firms would not give a chance to a new supplier even if it is offering lowest price. Firms were more willing to opt for a new supplier, if it was introduced through their business network (Chaudhry 2011). Akhtar (2012) analyzed the scope of surgical instruments business in Japan; by using a qualitative research approach Akhtar (2012) used interviews and questionnaires to derive an idea about the penetration of surgical manufacturing firms of Pakistan, in Japan. By using data from interviews and questionnaires conducted for the key informants in the surgical manufacturing firms of Pakistan, and the surgical and dental associations in Japan, it was established that non-financial factors impede the penetration of Pakistan's surgical sector in Japan, such as the language-barrier, political, social, and legal obstacles.
A brief structure of the surgical industry of Pakistan is given in Table 2.

Controllable Factors for Lot-Sizing Techniques
Firms need to minimize their total production cost, this includes strategies to reduce the variable costs which includes improving the inventory management techniques (Stadtler 2007;Goerler and Voß 2016;Gören, and Tunali 2018). The inventory management cost comprises different dimensions, which includes cost of raw material, holding cost for that raw material, and the ordering cost for the raw material (Mathur 2007)the firms working in the surgical sector of Pakistan rely on imported raw material. While the cost of raw material depends upon the negotiation, and the quality of the material, the ordering cost and the holding cost of the raw material can be controlled at firm level by adopting optimum lotsizing technique, such as Lot for Lot (LL), Wagner-Whitin (WW), Least Unit Cost (LUC), Least Total Cost (LTC), Silver-Meal (SM), Economic Order Quantity (EOQ), Periodic Order Quantity (POQ), and Part Period Balancing (PPB).

The Global Market for Surgical Equipment
With an increase in the prevalence of diseases requiring operative surgeries, such as cardiovascular diseases, neurological disorders, and urological issues; this has increased global demand for surgical instruments. In 2016, the global surgical equipment market was valued at USD 10.5 million, it is expected to double by 2025; moreover, the surgical equipment market is expected to grow at a compound annual growth rate of almost eight percent (Grand View Research 2017). North America dominated the global surgical equipment market, mainly due to the availability of advanced surgical tools, and expert surgeons (Grand View Research 2017). Pakistan major trading partners that receive exports of surgical equipment are the USA, Germany, Mexico, the UK, and France (Cluster Development Initiative c2017). Realizing the importance of the exports-oriented sector, the Government of Pakistan, announced exemption of sales tax on raw materials for leather, textiles, surgical goods, and sports goods (State Bank of Pakistan 2018). In the financial year 2018, a health growth was observed in the exports of medical & surgical instruments; nonetheless, the exports potential is much higher, there is a need for brand development, adoption of universal quality standards & certification is required, and the industry needs to streamline internal conflicts (TDAP, n.d.). Brand personality is an important determinant of customers' loyalty (Lane and Richey 2006;Sung, et al. 2010;Banerjee 2016); universal certifications & authentication are crucial in the context of branding and market share of a product (Starr and Brodie 2016;Banerji 2016). Many international surgical brands have outsourced manufacturing units to Pakistan (Nazir and Mahmood 2018), Pakistani artisans are appreciated worldwide; nonetheless, it is unfortunate that the surgical sector of the country has failed to develop international brands (Khan 2016). The fact that annually, on average, the surgical industry produces more than 170 million pieces (Khan 2016), alludes towards the export potential of this industry; nevertheless, the surgical industry of Pakistan has merely reduced to a vendor for the international brands that decide to outsource their manufacturing operations, in Pakistan (Barlow 2016;Nazir and Mahmood 2018). In an overly competitive world, no industry can survive without adopting efficient processes in all the phases of production and supply-chain; the position of the surgical sector of Pakistan will be challenged from competition from Mexico and China, both these countries are advantaged in terms of an abundant supply of cheap labor, and innovative technologies required to optimize the production phases (Gerefi 2009). Kumar et al. (2008) investigated the supply chain of medical supplies in Singapore; proposed a framework for costs reduction through process reengineering that is based on the idea of warehouse's centralization and controlling the non-production goods. Uthayakumar, and Priyan (2013) analyzed the inventory management practices of a hospital and a pharmaceutical company; they proposed an integrated model with continuous review of the supply chain by incorporating permissible payment delays, variable lead time, customer service level, and constraints on space availability. While the developed countries are adopting advanced inventory management technology, Pakistan's surgical sector is still not adopting optimum thuman assisted solutions for inventory management. Sands et al. (2009) have patented a system for optimizing the supply chain of medical assets that is based on tagging of products through Radio Frequency Identification (RFID). Mall and Mishra (2015) conducted an inquiry of the potential of RFID as an inventory management tool, it was concluded that adoption of RFID is promising; nevertheless, the barriers of technical incapability, unwillingness for technology adoption, human & capital resources, cybercrimes, lack of uniform tagging standard followed by the RFID companies, and government's legal regulations, limit the possibilities of RFID adoption.

Different Lot Sizing Techniques Wagner Whitin Algorithm
The WW Algorithm was proposed by Harvey M. Wagner, and Thomson M. Whitin in 1958 (Wagner and Whitin 1958); it has been used extensively by researchers to analyze the inventory management practices (Evans 1985 By using WW algorithm, firms can control the inventory cost, and setup cost; meeting the demand for the known periods. One of the merits of this algorithm, is its possible use, even if the holding costs, and setups are not constant across different periods. (Wagner and Whitin 1958). The formula for this algorithm is given in equation (1)

Economic Order Quantity Algorithm
The EOQ algorithm was proposed by Ford W. Harris in 1913; R. H. Wilson, who applied it extensively, is often given credit for elaborating this algorithm (Harris 1913;Wilson 1934). Many researchers have discussed the uses of the EOQ inventory management technique (Covert and Philip 1973;Schwaller 1988;Jawad et al. 2015;Perera et al. 2017). EOQ is used to control total ordering, and holding cost of inventory. This model works on certain assumptions, they are as follows: uniform, and known demand, fixed cost of items, and fixed cost of ordering, or holding inventory. To use EOQ, one must know demand rate, how much it costs to place an order, and how much it costs to hold that in inventory. EOQ also assumes that lead time is zero; time interval in placing and receiving order is zero, so delivery or manufacturing of products is instantaneous. EOQ accounts for three types of costs i.e., cost of the units, cost of holding units in inventory, and the fixed order cost i.e., cost of placing, and receiving an order, it also includes the cost of manufacturing setup. EOQ"s goal is to select the order-quantity that minimizes the average inventory-management cost, and time.
Formula for EOQ is given in equation (2) below: After finding optimum quantity i.e., EOQ, this value is incorporated in equation (3), to calculate the annual cost.
where, Q* = Quantity, D = Annual Demand rate, H = Holding cost, and C = Setup Cost. Silver and Meal (1973) introduced the algorithm of the SM for inventory management, to determine the production quantities to meet the requirement of operations at a minimal cost. It requires determining the average cost per period; yields an approximate solution for continuous time varying demand patterns; instead of finding the lot sizes that minimize the total ordering and inventory holding costs up to the time horizon, it determines each lot size sequentially, one at a time, by finding the minimum of the total inventory cost per unit time (Omar and Deris 2001). The SM model has following assumptions: Inventory schedule is required for calculations, demand rate is known, setup cost is fixed, and a known carrying inventory cost. In this technique, we add the holding cost and setup cost of the periods, and then divide the resultant value by the total number of periods, as we add periods the cost per period decreases; we stop adding periods until the cost per period starts to increase. We set the lot size where the period cost was least and start the process again from the next period; continue to repeat the process until lots of all defined period's demand is made (Tersine 1994).

Least Unit Cost (Lu)
The LU a dynamic technique for lot-sizing; aims at achieving the minimal, per unit cost.
(Tibeen-Lembke, and Rogers 2002). Many researchers have used the LU technique for inventory management (Benton 1991;Hariga 1994;Goyal and Giri 2003;Samak-Kulkarni and Rajhans 2013). The LU is similar to the SM; nevertheless, it relies on cumulative holding cost, and the cumulative setup cost to divide the result with the cumulative demand for the reference period (Tersine 1994). As successive periods are added, the cost per unit decreases; addition of periods ceases as the cost per unit starts increasing. Then an optimum lot size is determined for which the unit cost is least; then the process starts again for the next reference period, continue to repeat the process until demand for lots of all reference periods is made. The LU model is parsimonious as compared to the other inventory management techniques; nevertheless, the computations are dependent on the information about the predicted demand patterns for future, therefore, it relies on the quality of estimation procedure for the predicted demand. In addition to this demerit, LU has another consideration that it is a univariate method, it is only effective in minimizing the cost of a particular lot; the costs may differ from period to period so, the overall cost can be higher.

Part Period Balancing (PPB)
The PPB is a dynamic approach designed to balance the setup cost and the holding cost. (Dematteis 1968); balances these costs for the periods with known demand patterns. This technique uses the known demand information of the next period; balances the setup, and holding cost. The PPB computes the ratio of setup cost to holding cost; this ratio is known as Economic Part Period. Successive periods are added to calculate holding cost, or part periods; when the holding costs or part period becomes approximately equal to Economic Part Period, addition of periods is stopped. This iterative procedure is continued till the end of the total periods. (Heizer et al. 2008).

Lot for Lot (LL)
The LL is also termed as Discrete Order Quantity algorithm; it is a stochastic algorithm, used when the net-requirement on a particular day determine the quantity to be ordered. This technique generates a greater volume of orders as compared to the fixed lot-sizing algorithms. Due to its temporal decisions, LL computes relatively smaller quantities per order; consequently, tapering the inventory investment; its use is suited for expensive items, or for those whose demand occurs intermittently. Many researchers have discussed the applications of the LL algorithm, such as Gosrani and Kolekar (2007), Heizer and Render (2008), Bhagoria (2010), andMalakooti (2013).

Research Methodology Sample Composition, and Sample Design
The rationale behind concentrating on the Sialkot region is consequent upon the fact this region is the hub of the surgical sector in Pakistan (Deutsche Gesellschaft für Internationale Zusammenarbeit 2017). We conducted a survey of 75 surgical firms working in Sialkot; monthly data of one year was obtained against each firm's demand and inventory management costs. The population consists of 3900 firms; our sample is roughly 2% of the total population. While the sample size is relatively small; however, this is because this study was carried on without any funding from a third party. Line transect sampling was used to recruit 75 surgical firms working in Sialkot.

Research Instrument
A semi-structured questionnaire consisting of three questions i.e., ordering cost of one order for raw material, holding cost of per unit of raw material, and the third one is related to the demand of the raw material of the last 12 months. The questionnaire is framed to achieve following research objective: to unveil optimum lot-sizing technique to minimize inventory management cost.

Statistical Tests
We Next, we applied Classical Linear Regression Model (CLRM) to check whether the expected cost is lesser for techniques whose sample median was lesser; sample medians for LL, LUC, LTC, SM, and WW was lesser than the EOQ, POQ, and PPB. Given this scenario, we proceeded with the CLRM. The CLRM was applied to predict the cost as a function of the inventory management technique. From here on, LL, LUC, LTC, SM, and WW, are called Set-A, and EOQ, POQ, and PPB, are called Set-B.

Results
The significance value of the Mann-Whitney test came out to be zero; therefore, the null of equal median was rejected. This means that the median cost for different techniques is different; to comment on the optimality we banked on the CLRM. According to the CLRM results, given in Table-1, there appears to be a significant difference between techniques in Set-A, and Set-B. The linear model is Expected Cost= 6992-3072(D); D= 1 for Set-A, and 0 for Set-B. This implies that the cost of techniques in Set-A is 3072 units lesser than cost of techniques in Set-B. To comment on the magnitude of the average cost, we applied Post-Hoc test; results are given in Table-2. It can be observed that in population data, the inventory management algorithm-LL is expected to be 2505.5 units lesser than EOQ, LL is expected to be 1873.3 units lesser than POQ, LL is expected to be 4838.5 units lesser than PPB. EOQ is expected to be 2333 units lesser than PPB, POQ is expected to be 2965.2 units lesser than PPB, LTC is expected to be 4838.5 units lesser than PPB, SM is expected to be 4838.5 units lesser than PPB, and the WW is expected to be 4838.5 units lesser than the PPB. Figure (1) represents a quick comparison of the eight algorithms for inventory management. From Figure (1), it is evident that the WW, SM, LTC, and LUC outperform all the other algorithms, POQ is better than EOQ which in turn is better than PPB. The 95% confidence intervals for each cost can be interpreted as follows: there is a 95% likelihood that LL margin of outperformance of LL over EOQ is between [2190.4, 2820.6].

Discussion
This study attempted to offer solution for inventory management practices for the surgical industry of Pakistan. It must be kept in mind that inventory management is only one dimension of the total quality control process, later dimension aims at optimizing the overall quality metrics by monitoring the volume and quality of output, while the former enables businesses to nip the problems effecting the quality control in terms of demand and supply decisions. Optimizing the inventory management process ensures that the demand & supply forecasts are estimated accurately; this includes decision about the product replenishment. This study has analyzed the inventory management practices of the surgical industry of Pakistan; this industry is crucial in terms of the designing the SDG Industry Matrix; which is a framework for businesses to create value for themselves, and the society (United Nations Global Compact 2015). In this context, one must understand the nexus between the valueaddition for society, and the pyramid of human motivation purported by Maslow (1943), and O'Connor and Yballe (2007); it is less likely that businesses that are not cost-efficient, adopt the SDG Industry Matrix to achieve the Sustainable Development Goals.
Based on the regression findings, if businesses were to choose between Set-A (i.e., LL, LUC, LTC, SM, and WW) with Set-B (EOQ, POQ, and PPB), then any technique in Set-A is an optimum choice; however, a choice within Set-A should not be merely dependent upon the statistical significance depicted in Figure (1), and Table (2). One must keep in mind that statistical outperformance of one technique against the other should not douse the decision-makers, rather the decision should also include the practicality of the business operation. For instance, LL is proven to be less costly then POQ, EOQ, and PPB; nevertheless, LL algorithm, which is designed for intermittent demand patterns, can badly affect the brand-image, a supplier who fails to deliver goods at the time of demand loses the clients' trust. Similarly, due to just-in-time nature of the LL technique, it can seriously get affected by unpredictable natural, and economic shocks, if these shocks stop the supply of inputs, then production process can halt.

Avenues for Future Research
Future studies can focus on explaining the determinants of adoption of inventory management techniques; analyze the characteristics of the firms that adopt a particular technique, for instance, whether younger firms adopt part period balancing technique, or experienced ones do so. Another dimension for future work can be to explore the impact of demand side and supply side shocks; are there any techniques that outperform others in presence of such shocks. Behavioral experts may research upon the personality dynamics of firm's ownership and its link with the choice of the inventory management techniques; risk averse decision takers shall behave different than the risk lovers.


Single Event Transition Risk: A Measure for Long Term Carbon Exposure

Although there is a growing consensus that a low-carbon transition will be necessary to mitigate the accelerated climate change, the magnitude of transition-risk for investors is difficult to measure exactly. Investors are therefore constrained by the unavailability of suitable measures to quantify the magnitude of the risk and are forced to use the likes of absolute emissions data or ESG scores in order to manage their portfolios. In this article, we define the Single Event Transition Risk (SETR) and illustrate how it can be used to approximate the magnitude of the total exposure of the price of a share to low-carbon transition. We also discuss potential applications of the single event framework and the SETR as a risk measure and discuss future direction on how this can be extended to a system with multiple transition events.

INTRODUCTION
Growing climate consciousness has led to an increased interest in various low-carbon transition policies and scenarios. Social responsibilities aside, to be long on carbon intensive stocks during such a transition can prove to be rather risky for investors, and therefore investors must factor in low-carbon transition risk into their portfolios. Currently, the quantities that an investor can use to hedge their portfolio are limited, and therefore they have to rely on simple quantities like absolute carbon emissions of firms, emissions intensities or quantities derived from composite scores like Environmental, Social and Corporate Governance (ESG) scores [Madhavan et al., 2021] or Brown-Green Scores [Görgen et al., 2020]. Emissions, emissions intensity or ESG reports are usually published by firms under voluntary disclosures, as a part of Corporate Social Responsibility (CSR). These quantities are readily available, making them convenient tools for assessing carbon risks associated with firms. While there is existing literature on portfolio optimization strategies using absolute emissions [Andersson et al., 2016], emissions intensity [Fang et al., 2019] Brown-Minus-Green (BMG) risk factor [Roncalli et al., 2020, Roncalli et al., 2021 and ESG scores [Baker et al., 2018, Pástor et al., 2021, Pedersen et al., 2021 it is imperative to note that these quantities only convey a relative degree of risk between countries and do not quantify or give an approximation of the actual exposure. For instance, an ESG score tells an investor that one firm's production is cleaner than another's, and the former with lesser emissions is likely to have lesser transition risk than the latter. However, it does not give the investor any information about the magnitude of the risk, or even the relationship between the two risks. For instance, just because one firm's emissions are twice as much as another firm's, it does not necessarily mean that the risk too, will be twice as much. Furthermore, the relationship between ESG scores and financial performance is not consistent among different studies done with different sets of data. For instance, a study on firms in Latin America find a negative association between ESG scores and financial performance [Duque-Grisales and Aguilera-Caracuel, 2021]. This, however, disagrees with research on S&P500 firms which finds the impact to be time dependent, i.e., a negative association is found on the short term but a positive association is found for the long term [Nollet et al., 2016]. Other studies find more fundamental inconsistencies in the relationship between ESG scores and financial performance, where market-based and accounting-based financial performance were observed to vary differently with ESG scores for firms in Germany, where a positive association was found with accounting-based performance but no association was found for market-based financial performance [Velte, 2017].
Therefore, the existing measures are at best, proxy measures that help in qualitative comprehension of relative risk between stocks but fail to give insights about the actual exposure of the stock to transition risk. Therefore, despite rich literature on sustainable investing, the models and strategies are limited due to the unavailability of a more direct measure of the transition risk. This problem would be fixed in the presence of a methodology that allowed investors and analysts to measure the exposure of the risk from empirical facts like emissions or emissions intensity, or an approximation of the same. Given that when and how low-carbon transition will take place is likely to vary from place to place and also be influenced by a plethora of events and processes, those occurring presently and those yet to occur, hence, determining an exact measure of the exposure is an ambitious and futile exercise. Nevertheless, it would be useful to make some simplifying assumptions and develop an approximate measure for the exposure. Investors could then use this approximate measure to hedge their portfolios and divest from fossil fuel intensive firms. Accordingly, we propose for one such measure, namely, the Single Event Transition Risk (SETR).
The SETR is a novel quantity developed in the context of the thought experiment that asks what would be the premium-risk relationship if carbon-transition was triggered by a single event. It is a hypothetical quantity estimated based on risk-premiums estimated in the real market, and some ideal market assumptions. Although the SETR is calculated under ideal assumptions, we expect its utility in portfolio management and risk analysis will be significant. This is akin to "fatal shock models" [Marshall andOlkin, 1967, Gut andHüsler, 2005] in the reliability literature which have been used in the context of credit risk and insurance modeling [Lindskog and McNeil, 2003].
In this article, we develop the idea of a single event framework and propose the SETR as a proxy measure for risk, and argue why this is a useful measure to have, when compared to existing quantifiers used in the context of carbon transition risk. We also highlight future direction in the context of the utility and applicability of the single event framework and the SETR. Accordingly, We define the SETR as the total exposure of a share of a firm at any point in time, if the entire loss of value due to a low-carbon transition is incorporated at that time. The single event is a simplification of the real scenario where the total risk is likely to be triggered by a series of events rather than a single one.

DEFINITION
The SETR is defined in the context of a single event framework, where the transition risk is incorporated into the value of the stock due to a single "fatal" shock. Fatal shocks are defined as shocks which are stronger than some critical level, after which a component of the system is entirely killed [Marshall and Olkin, 1967]. This is analogous to our system where a single shock kills the carbon-associated component of the share value. This is equivalent to considering a hypothetical market, which is identical to real markets in all ways except that in this market, the low-carbon transition happens due to a single shock. Nevertheless, the single event scenario gives a worst-case estimate and therefore finds utility in portfolio considerations. An important point to note is that the SETR does not only quantify the value of the risk for a transition to a zero-carbon level. In fact, the framework can be used for a transition to any given carbon level, as long as the market is not expected to transition any more after that. In spite of the SETR being estimated under hypothetical single event considerations, there are several real scenarios which would correspond almost exactly to a single event risk. For instance, unexpected government regulation on carbon emissions of firms. If the government of a country passes legislation restricting the emissions of a firms, one would expect the value of the firm to take a large hit based on the loss of productivity due to the newly introduced carbon-cap. Most of this loss of share value would be incorporated in a small interval of time. Measures like carbon taxes which are expected to force firms to transition in more passive ways do not correspond exactly to the single event framework, but this only demands for models which can translate from carbon tax rates to firm revenue and profits, and therefore, for a given tax rates, if it is possible to calculate the shortfall of profits and revenue and other financials, it is possible to quantify the magnitude of the SETR. In other types of government legislation like carbon rationing or carbon allowances, where firms are allowed to emit a maximum amount of emissions (which may even be firm or sector specific), the SETR is nearly an exact model. In the subsequent sections, we will develop a method where we can calculate the SETR for firms based on price movements and financials, and also demonstrate its applications to pricing risks in the paradigm of firm-level carbon allowance legislation.

THE CARBON RISK PREMIUM
A major breakthrough in the climate finance literature has been the discovery of a significant carbon premium in multiple contexts across different countries. The carbon premium can be defined as the abnormal excess return on a stock or a portfolio, attributed entirely to the carbon(or other greenhouse gas) emissions of the firm or set of firms in the context of a portfolio. The carbon premium has been estimated in several empirical studies, both at the firm level [Bolton andKacperczyk, 2021a, Bolton andKacperczyk, 2021b] and portfolio level [Oestreich and Tsiakas, 2015]. Since we are interested in estimating the exposure for a firm, we concern ourselves with the firm level carbon premium. The firm level carbon premium is an artefact of price patterns in financial markets, where firms with disproportionately large carbon emissions earn returns higher than one would expect for a firm with the same financials but lower emissions. This premium can be thought of as an incentives for investors to buy or hold shares of "dirty" (carbon intensive) firms. The incentive is necessary in order to offset the higher transition risk present with carbon intensive firms. In the absence of such an incentive, a rational trader would sell the shares of dirty firms as they come with a risk, but no additional benefit. We use this heuristic to arrive at an appropriate relationship between a correctly priced carbon premium and a SETR.

FROM CARBON PREMIUM TO CARBON RISK
The SETR is not a real quantity. It is a synthetic measure that we are constructing, in order to get a quantifiable measure for long term carbon exposure. In order to do this, we begin from the definition of a carbon premium. Investors perceive that there is some long term transition risk present in the shares of "dirty" stocks. Therefore, a carbon premium is necessary for investors to continue buying and holding the "dirty" stocks despite the large risk. In the single event frame work, we construct a thought experiment. We ask what would be the value of long exposure to transition risk on the price of a stock, from a single event transition, that would be offset by the carbon premium that we actually observe in the market. In essence, if the measured value of the carbon premium in real markets is some p, we quantify the SETR φ(t) as the corresponding value of the risk in a hypothetical single event market, which would be offset be the same premium p. It would be useful to note that this process is the transpose of how the relationship between premiums and risks are usually determined, where the value of risk is known (or estimated) and the value of the premium is calculated accordingly. However, our methodology does not violate any causal relationship between the risk and the premium. Instead, we consider a hypothetical market where the risk is actually quantifiable, unlike real markets, and ask what the pricing of the risk must have been in the hypothetical single event market in order to generate the same valuation of the carbon premium that we observe in real markets. Since the SETR is calculated for a hypothetical market, it is a "real" pricing of carbon transition risk in real markets. Rather, it is a synthetic measure that we argue will be useful to investors as a proxy of the actual exposure, due to no known methods of calculating the "real" risk exactly or approximately. In subsequent sections, we will develop the idea of what the hypothetical "single event market" is and calculate the SETR in terms of the premium. An example of the price movements of the risk-free price and the corresponding price under the paradigm of carbon premium and SETR has been simulated for different random realizations in

MARKET ASSUMPTIONS
In order to be able to be able to derive an relationship between the carbon premium and the SETR, we construct a hypothetical single event market where some simplifying assumptions are valid. We expect that some of these assumptions will be realistic even in real markets, while others will not. These assumptions are crucial in reducing a complicated stochastic system to a significantly simpler system with a smaller number of stochastic variables and quantities. The absence of these simplifications would make the principal question too difficult to answer and may even render it unsolvable, thus taking away its utility. We therefore, at present restrict our analysis within the paradigm of these assumptions. These assumptions also call for more literature where the transition risk is calculated for markets with one or more of these assumptions relaxed.
Assumption 1. A non-negative carbon premium exists in the shares of firms with high carbon emissions. When other parameters are kept constant, this premium is greater for firms with greater emissions. This is a general assumption that we believe is universally valid, based on empirical evidence.
Assumption 2. The financial market is efficient in pricing the carbon premium.
By this we mean that investors in the market are aware of carbon transition risk and the value of the carbon premium with respect to the expected transition risk is correctly priced and universally agreed upon. Apart from assumptions about the carbon risk premium. We also make assumptions about the SETR.
Assumption 3. The entire loss of value of the share of a firm due to low-carbon transition, will be due to a single event that takes place in an instant.
Assumption 4. There exists a probability distribution for the arrival of the transition event such that the probability that the transition event occurs in the future is a surety (conditional to an initial time before which the transition has not occurred).
Mathematically, the transition event can be modelled by a stochastic risk process R(t) defined by, 1 , The transition event occurs 0 , The transition event does not occur (1) Let P [(t A , t B )] denote the probability of the transition event occurring in some interval (t A , t B ) i.e., the probability that ∃ t ∈ (t A , t B ) such that R(t) = 1.
Thus, given initial time t 0 such that low-carbon transition is yet to happen, we can model the arrival process by some probability density function Additionally from Assumption 4 we know that, Finally, inspired from the pricing of premia in the context of credit risk [Bluhm et al., 2016], we make our most crucial assumption.
Assumption 5. The expected surplus return from the carbon premium is equal to the expected loss of value due to the single event transition.
This is the assumption on the basis of which we will find the relationship between the carbon premium and the SETR. We shall call this the "(weak) no-arbitrage assumption" (as opposed to the strong no-arbitrage condition in Appendix 5.2).

CALCULATION OF THE SETR
Consider an investor who buys a stock at time t 0 . To buy a stock and to hold a position in a stock is equivalent, as an investor will only be interested in profit opportunities in the future. Without loss of generality, we can assume the pre-risk price of the share at time s = t 0 to be S(t 0 ) = 1. Since the premium is included in the returns, it is also included in the price of the stock S(s) for any general time s. We can subsequently define a real valued function φ(t) : [t 0 , ∞) → R that describes the potential loss of value of the price of each share at any time t at which the transition event occurs. Now, φ(t) is our estimate of the SETR for an event that occurs at time t i.e., R(t) = 1. Since there is only a single transition event, we know that t is unique i.e., if the transition event takes place at time t, it will not take place at any other time s = t. Therefore we can model the price of a stock generally as, We can see that this function is equal to S(s) for s < t (until the transition event occurs). Based on this, we expect that the existence of the risk premium in the single event market is motivated by the function φ(t) and the process R(t) (and therefore implicitly by the density function f (t)). Therefore, given f (t), we aim to find the relationship between the risk premium p of the shares of a firm and the SETR function φ.
If an investor expects to receive a premium p(s) from time s = t 0 through s = t, then the amount of gains made from the premium till time t is, Since t is determined by the distribution f (t), the expected earnings from the premium is given by, Here, the inner integral gives us the total amount that an investor expects to earn from risk premia above the risk free returns, for a given t, while the outer integral averages over all possible times for the transition event with respect to the arrival process f (t). Similarly, we can find the expected value of the SETR as, Therefore, from Assumption 5 we get, Hence, This equation gives us the relationship between the risk premium and the SETR. In order to be able to simplify this further, we shall assume p(s) = p to be a constant for all future times s > t 0 . Then, Therefore, We can split the left hand side into two terms as, We can see that the first integral on the left hand side is the expected time of arrival of the risk and the second integral is equal to 1. This is an interesting result because this tells us that the amount an investor can expect to earn from the risk premium does not depend on the form of the arrival process at all and only depends on the expected time of arrival. Therefore, Furthermore, we see that the expected value of the risk grows linearly with the amount of premium and the expected arrival time of the transition event. This, however is a consequence of assuming a constant expected annual risk premium. A better calculation could use a continuously growing risk premium as determined by Bolton and Kacperczyk (2021), but due to the dearth of available data on risk premiums over sufficiently long periods of time and models that make explicit projections of risk premiums for the future, we are constrained to assume a constant premium.
The expected value of the risk however, does not tell us much about the form of φ(t), but only gives an average. But intuitively, since φ represents the transition risk, it should be a constant in time, unless the firm makes changes to its operations or scale. This is because the risk is thought of to be the difference in share value between the present value and the value when the firm is forced to cut down its carbon emissions by a certain "maximum" amount. Therefore, unless the firm makes changes in the scale of production or the methods of production, the value of the SETR should not change over time. Therefore, we can also assume specifically that φ(t) = ϕ is a constant. Therefore, Now, substituting equation (15) in equation (14), we get, This relationship between the risk and premium is extremely important for several reasons. Firstly, because it helps us answer the question that we started out with, namely, to quantify the SETR in the "single event" market in terms of the carbon premium. But due to the assumptions of constancy of the premiums and the SETR, the expression is independent of the form of f . This is particularly useful for investors, as now quantifying the SETR does not depend on the exact arrival process, but only the expected time of arrival. It does not even depend on the variance of the time of arrival. The expression in equation 16 is also the intuitive expectation for the pricing of the SETR under the no-arbitrage conduction, because it produces the result that the value of the SETR is equal to the expected cumulative earnings from the carbon premium for the expected duration for which there will be a premium in the market. Although this calculation has been performed assuming the premium stays constant into the future, empirical analysis shows that carbon premiums have increased over the years [Bolton and Kacperczyk, 2021a]. This can be attributed to the following three scenarios: (1) More investors have become aware about carbon transition risk and the "average" premium in the market moves away from 0 towards the fair price. Thus, it is possible for the estimated premium to increase without the fair pricing of the premium actually increasing. In this case it is a valid assumption to consider a constant premium in the efficient market.
(2) The value of the fair price of the premium increases over time even in the efficient market. In which case a better calculation would involve assuming an increasing form of the premium.
(3) A combination of both of the above two factors. In this case as well, the function will probably have an increasing form.
In the Appendix 5.1 we consider a case where the carbon premium is projected to grow exponentially into the future. This is another realistic assumption as stock prices are conventionally modeled using Geometric Brownian Motions [Black and Scholes, 2019], which have exponential solutions. Thus if the premium is maintained at a constant fraction of the share value, it would also likely show an exponential growth (albeit with stochastic variation).

APPLICATIONS AND FUTURE DIRECTION
Throughout the previous sections, we have introduced the theoretical framework of estimate carbon transition risk via the SETR. We have also calculated the relationship between the risk premium and the SETR under the simple assumptions of a constant premium and a constant risk. For calculations with some relaxations on these assumptions, one may refer to Appendix 5.2 for a calculation where the SETR is not assumed to be a constant, and Appendix 5.1 for a calculation for a geometrically growing premium. In this section, we will discuss the utility of the SETR for market analysts and investors.
An obvious benefit of SETR is that there now exists a quantity that can be used as a proxy for the magnitude of the risk. While firm-level carbon premiums are proportional to the SETR, they do not give us information about how the risk scales with the premium. The Single Event framework helps quantify a measure of transition risk that also takes magnitude into account. A consequence of the existence of such a risk measure is that now investors not only know if one asset is riskier than another, they also have a measure of how much riskier an asset is as compared to liquidity. This allows a trader to also compare the risks of holding assets on absolute terms rather than relative terms. Furthermore, this opens avenues of non-arbitrary hedging strategies that allow for finding an optimum investment strategy. The advantage of having an actual risk measure is that investors can now calculate the return-risk trade-off better. One must recognize that the return-risk trade-off in the context of sustainable investing is particularly important due to the presence of a carbon premium and a transition risk which are by themselves measures of returns and risks. Similarly one can use the SETR to develop a dynamic divestment strategy from carbon intensive stocks that allows them to reduce their long term holdings of carbon intensive stocks without losing out on short term earnings from premiums.
The SETR formulation can also be extended to modelling real carbon transition risks associated with legislation such as firm-specific carbon allowances. A transition to an economy where each firm has a limit on the absolute amount of emissions they can produce will lead to several firms having to cut down on their emissions in a very small window, which can be approximated to a single event transition, where the entire transition, from current levels of emissions to the allowed levels happens in a very small interval of time. Thus in a paradigm where carbon allowances or carbon rationing is seen as the prevalent mode of carbon transition, the SETR framework provides a fine approximation of the risk. The premiums can be calculated in terms of the "abnormal excess returns" of the share of the firm attributed to the "excess emissions" i.e., the difference of current and allowed emissions. The SETR then calculated will give the value of transition risk of the share in the real market. In this case, the real market is also a single event market. Therefore, the SETR calculated under single event assumptions can be extended to real markets.
Additionally, the single event framework can be expanded to a k-event framework, where there are k possibly non-fatal shocks in the system. A generalization to k-events, with k being a random variable will allow the modeling of risk in real markets, rather than using the risk in a single event market as a proxy for the real risk.

CONCLUSION
With the acceleration of climate change, as is the trend, governments will soon have to intervene in the process of firms transitioning to low-carbon methods of production. If firms do not actively transition themselves, they will be forced to see drops in revenue and profitability when governments impose restrictions on emissions. This brings a carbon-transition risk to an investor's portfolio in possibly large magnitudes. However, the magnitude of the risk is difficult to estimate or approximate from market data, leaving investors with rough indicators like absolute emissions of firms and ESG scores. In this article, we develop a novel measure, namely, the SETR, to have an approximate measure for the magnitude of the transition risk. The SETR is thought of as the magnitude of the transition risk in a corresponding ideal, single event market which would yield the same carbon premium as measured from data in real markets. We introduce the ideal assumptions made for the single market and define the relationship between the carbon premium and the SETR. We also explicitly find a simple form for the relationship under the assumption that both the premium and SETR are constants in time. The SETR thus calculated can be used as a proxy for the firm-specific magnitude of carbon transition risk. [Pedersen et al., 2021] Pedersen, L. H., Fitzgibbons, S., andPomorski, L. (2021). Responsible investing: The esg-efficient frontier. Journal of Financial Economics, 142(2):572-597.

CALCULATION OF SETR UNDER GEOMETRICALLY GROWING PREMIUMS
Consider a carbon premium that increases with time s according to the following differential equation, dp(s) ds = λp(s).
The solution of this equation gives us, p(s) = p 0 e λs , where p 0 is the value of the premium at time s = t 0 . This model of the premium is based on the assumption that stock prices themselves grow geometrically, and therefore if the premium is maintained at a fixed proportion of the share price, its growth will likely be geometric. Putting this relationship back in equation (10), and further assuming a constant SETR, we get,


Online Impulse Buying: Impact of Internet Celebrity Endorsement and Peer Pressure

During the epidemic of COVID-19, online purchases have been rising and e-commerce live broadcasts have become more popular. As impulse buying happens a lot, the factors influencing impulse buying in e-commerce live broadcast rooms are of interest. Basing on the expectation confirmation model (ECM), we proposed a new model that explores the roles played by internet celebrity endorsement and peer pressure in the process. This study extends related research in consumer behavior and provides implications for marketing practices.

INTRODUCTION
During the COVID-19 pandemic, numerous consumers have chosen online shopping without leaving their homes, and e-commerce live broadcasts have been widely favored by consumers to collect information of products. Therefore, how to further enhance sales through e-commerce live broadcasts by improving the content and design has become a key concern to managers. While previous studies have explored this issue from several perspectives, most of them focused on general online buying behavior, rather than online impulse buying [1,2]. With an impulsive characteristic, the latter apparently has a different pattern in the purchase decision-making process. For instance, a good visual design and web-consumer interactions can immerse consumers in a flow state and stimulate impulsive purchase Wu, Chiu [3]. Given live-broadcast's unique features in terms of visual effect, direct communication and instant engagement [4,5], it is necessary to focus on the impulse and explore influencing factors in such a context.
Basing on the expectation-confirmation model (ECM), this study develops a new research model to further explore the influence of consumer perception factors and live broadcast platform factors on impulse buying behavior. We highlight the impacts of celebrity endorsement and peer pressure on consumer purchase decision through their interactions during live broadcasts. This paper contributes to the theoretical development in the field. And marketing implications are discussed. The findings suggest e-commerce platforms use live broadcast as a marketing strategy and utilize social influence from online celebrities and peers.

Expectation confirmation model
Impulse buying refers to the sudden and immediate purchase made by consumers without the intention to purchase a specific category of goods or a specific purchase task [6]. Previous studies on external factors of online impulse buying mainly focused on the characteristics and contextual factors of e-commerce platforms. Website quality and attributes were found influential to consumer perception and impulsive purchase online [7]. Research on cognitive dimensions mainly referred to a series of psychological processes occurring when consumers receive external influence, which further affect the consumer's behavior [7,8].
Thanks to the development of social media and instant communication, consumer purchase decisions have been given an interactive online environment, opening to more stimuli that may trigger their impulse [9]. As a new way for e-commerce, live broadcast enlarges such impacts with more visual, direct and instant interactions among the host, the focal viewer and other viewers. To capture attentions, the products displayed on e-commerce live broadcast platforms usually have special functions or outstanding quality that consumers have not predicted, resulting in a lot of unplanned purchases.
Oliver [10] suggests that perceived usefulness has a positive impact on consumer satisfaction in environments such as mobile websites and e-commerce [3]. Previous studies have shown that consumers are more affected by the positive effects of situation factors in online platforms than the negative effects when they buy online [11,12]. In addition, another driving factor in the structure is confirmation, which was mainly defined in previous studies as the confirmation between expected and perceived performance of electronic stores when using information systems [3]. In this study, based on the environment of e-commerce live broadcast platforms, it is summarized as perceived confirmation, which mainly refers to whether the functions and design of e-commerce live broadcast platforms are higher than consumers' expectations on them. Regarding consumer satisfaction, it is considered the core content of marketing research in previous studies [1]. It acts as a decisive factor in online purchases in studies using the ECM model [3]. In this study, it is defined as the customer satisfaction of online consumption in e-commerce live broadcast. It has been found that confirmation and satisfaction were related in the fields of social media and electronic product consumption [13]. Other studies have explored factors such as website quality and pointed out that effective product promotion and search processes could increase consumer satisfaction with the website, thereby promoting consumers' unplanned purchases or impulse buying tendencies [2]. These showed that improving consumer satisfaction may have a positive effect on promoting consumers' impulse buying behavior.

Internet celebrity endorsement
Internet celebrity refers to online celebrities who have an influence through social media. Internet celebrities use social media and other platforms to carry out interactive activities, creating a parasocial relationship, which makes them higher credibility and influence than traditional celebrities [14]. Internet celebrity endorsement is influential because consumers believe that Internet celebrities are unlikely to abuse their influence or damage their reputation by publishing false endorsement information [14]. Previous studies have shown that when Internet celebrities have sufficient credibility, some consumers may believe that the products endorsed by the Internet celebrities are authentic and reliable, which can promote consumer impulse buying behavior to a certain extent [14,15]. Moreover, consumers are more likely to be affected by positive reviews in the underlying cognitive information processing process (the perception process of the ECM model), while negative reviews are less affected [15].
Meanwhile, when the audience shows a sufficiently high degree of awareness of the internet celebrity, the audience's attitude will also be more positive [4]. The authenticity and enthusiasm of the information contained in the internet celebrity positive endorsement has a significant impact on impulse buying [15]. Internet celebrity endorsement enhances the mediating role of satisfaction between perceived usefulness, perceived confirmation and impulse buying.

Peer pressure
Peer pressure indicates that consumers are influenced by others' opinions and behaviors related to the product in the e-commerce live broadcast room (such as placing an order, 'favoriting' or evaluating the product, etc.). The influence of peer pressure is twofold. First, active purchase behavior of other consumers means fierce competitions among buyers for limited quantity of goods, causing a perception of scarcity and strengthen the motivation to get it. Previous studies have shown that scarcity leads to consumers' online impulse buying behavior [16]. Second, the "popularity" of a product can give consumers a belief: if most people think that the product is good, then it must be good. Hence, scarcity and popularity may stimulates consumers' desire for the product, which in turn leads to impulse buying behavior. Wang, Du [17] found that popularity has a significant moderating effect on consumer satisfaction. Peer pressure, which includes the popularity factor, may work in a similar way. With the development of technology, e-commerce live broadcasts have engaged more and more consumers to interact on the platforms, enlarging the effect of peer pressure. When the peer pressure is high (i.e., when a consumer perceives the product is more scarce or popular), a consumer is more likely to feel satisfied and is more impulsive to buy it. Notes: Coefficients (β) and significance were indicated in the brackets; * p < 0.05; ** p < 0.01; *** p < 0.001.

RESEARCH METHOD
This study adopted the method of online survey to test the proposed model. To ensure the validity of the questionnaire, the measurement items are adapted from previous studies [3,[18][19][20] and further modified to fit the context of this study. As the original measurement scales are in English, we used the method of translation and back-translation to ensure their linguistic equivalence. We administrated the questionnaire to participants in China via multiple social and shopping platforms in different time slots, and initially obtained 400 complete responses. To control the impact of outliers on the results, 21 questionnaires were excluded. Thus, a number of 379 responses were used for hypothesis test.

RESULTS AND DISCUSSION
This study applied a partial least square structural equation modelling (PLS-SEM) with Smart PLS 3 to test the model. As shown in Figure 1, the results indicated that only one hypothesis (i.e., H5c) was rejected, and all others were supported. Peer pressure can enhance the relationships between perceived usefulness/confirmation and consumer satisfaction, but cannot affect the consumer satisfaction-consumption impulse relationship. In other words, the more people were involved, the higher they feel satisfied, but it cannot improve consumers' impulse purchase in a specific marketing activity. It could be plausibly explained by people's requirement of uniqueness.
Consumers would be more rational when they realized that a product becomes popular. Thus, we suggest marketers provide more options for consumers to choose in a sales promotion. To improve sales, marketers are suggested to invite internet celebrities in the marketing activities. More research may be conducted to clarify the effect in the future.

CONCLUSION
This study contributes to the theory and practice about consumer behavior in several ways. Theoretically, we extended the expectation-confirmation model by introducing internet celebrity endorsement and peer pressure as moderators, which are found can strengthen the mediating effect of satisfaction on online impulse buying. As these two factors are important elements of the contemporary e-commerce environment, especially given a fast emerging of live broadcast for online selling in the recent years, our findings provide valuable implication for e-business and online marketing.


The Effect of Cigarillo Packaging Characteristics on Young Adult Perceptions and Intentions: An Experimental Study

Research demonstrates that characteristics of cigarette packaging influence consumer product perceptions, yet the current literature on the impact of cigar packaging is limited. This study aims to examine how different cigarillo packaging features influence young adult cigar smokers’ perceptions. In 2016, we recruited past-year cigar users aged 18–34 from Amazon Mechanical Turk (N = 1260). We utilized a 2 × 2 × 3 × 2 between-subjects factorial design, randomly assigning participants to view one of 24 images of a cigarillo package that varied by brand (Black & Mild vs. Swisher Sweets), brand name (full vs. abbreviated), color (brown vs. green vs. purple), and price promotion (present vs. absent). Participants rated the product on several perceptions and purchase intentions, and they reported on cigar use and demographics. Overall, color and brand name influenced perceptions, but effects varied by brand. For Swisher Sweets, only price promotions influenced perceptions (e.g., taste, use for marijuana); for Black & Mild, all packaging features influenced perceptions (e.g., harshness, tobacco quality), and price promotions increased purchase intentions. Our findings also raise questions that product features may interact with one another, with certain features, such as color, overpowering other attributes. More research is needed to understand the impact of other packaging features, such as warning labels, on product perceptions across a variety of brands.

Introduction
While cigarette use in the United States (U.S.) has significantly decreased over the past decade, sales and consumption of cigars have steadily increased [1][2][3], as has the variety of cigar brands, sizes, tip styles, and flavors available on the market [3,4]. Flavored cigars in particular have experienced a dramatic growth in sales since 2008 [3,5]-in part due to the federal ban on flavored cigarettes [6] and the rapid proliferation of flavor varieties [4], which drove most of the growth in overall cigar sales [7]. Of the various types of cigars, cigarillos are the most popular [8,9]; in fact, industry reports show cigarillos currently make up more than half of the cigar market [10].
Various data sources point to high rates of cigar use among young adults [9,[11][12][13][14]; most notably, the National Survey on Drug Use and Health has consistently shown that the prevalence of past month cigar use is highest among young adults compared to all other age groups [15]. Moreover, cigars are the most common product initiated during young adulthood [15]. Not surprisingly, the majority of young adult cigar smokers report using cigarillos as their primary cigar product [16]. There are many factors that drive cigar popularity among young adults, including flavors that appeal to young people [7,9,17,18], a lack of awareness and misperceptions about the risks of cigar use [19,20], and more affordable pricing that results from differing tax rates and less stringent marketing regulations [21,22]. Another potential yet understudied aspect of cigar popularity is the visual appeal of cigar packaging.
Past studies have highlighted the importance of packaging as a tobacco marketing strategy that can influence brand imagery and consumer perceptions [23][24][25][26][27][28]. Tobacco products, including cigars, have diverse packaging that can explicitly and implicitly communicate different attributes surrounding the product's flavor, taste, and harshness. Visual presentation, such as the use of color on packaging, can impact consumers' perceptions about the product itself, including its taste and flavor [29,30]. Given the diversity and abundance of visual components that companies can potentially manipulate on a single package, it is important to recognize and examine how such components can independently influence perceptions, particularly among vulnerable populations, such as young adults. However, much of the current evidence comes from the literature on cigarette packaging, and cigar-specific research is scant.
Similar to research on cigarettes, findings from studies on perceptions of cigar packaging have identified that packaging descriptors and claims (e.g., "natural") [31,32], branding [32], price promotions [32,33], and colors [31,32,34] are appealing to consumers. Moreover, many appealing cigar packaging characteristics are unique to cigars and not apparent in cigarettes. These include resealable pouches [32,35], small pack sizes [31,32,36], shiny foil packaging [36], and diverse flavors promoted via flavor descriptors and pack imagery [31,32,34]. Nascent research has found that some of these attributes, including flavor descriptors, influence perceptions of harm and addictiveness [34] as well as intentions to use the product [34].
While these studies draw attention to attributes of cigar packaging that appeal to consumers and may influence product perceptions and intentions to use, most relied on qualitative data or experimental studies using fictitious brands or the complete absence of branding and thus have low external validity. Furthermore, there is a need for examination of how perceptions of cigar packaging differ by brand, given the diversity of cigar packaging and varying user profiles across different cigar brands [37]. As such, this study aims to examine how different features of cigarillo packaging influence young adults' cigarillo perceptions. Specifically, we examine the influence of four packaging characteristics: brand, abbreviation of brand name, color, and presence of price promotions.

Participants
In 2016, we recruited young adult participants from Amazon Mechanical Turk (MTurk), which is a platform that is well-suited for experimental studies and is commonly used to recruit participants for social and behavioral science research [38][39][40]. To be eligible for the study, participants had to be 18-34 years old and had to have used a cigar (i.e., used any type of cigar or cigarillo, smoked a blunt) in the past 12 months. Of the 1279 eligible participants, 19 were dropped due to having duplicate survey entries, completing less than 50% of the survey, having more than one missing value for the experimental questions or failing two or more attention checks. The final sample included 1260 participants.

Procedures
We conducted an online experiment with a 2 × 2 × 3 × 2 between-subjects factorial design, in which we randomly assigned participants to view one of 24 images of a resealable, 2-count foil cigarillo package that varied by brand (Black & Mild vs. Swisher Sweets), brand name (full vs. abbreviated name (i.e., B&M, Swisher)), color (brown vs. green vs. purple), and price promotion (present vs. absent). We digitally created the images to manipulate each factor, making sure none of the images explicitly presented flavor names ( Figure 1). After viewing the image, participants rated the product on several perceptions. Participants also responded to survey measures assessing cigar use and demographics. The Rutgers Biomedical and Health Sciences Institutional Review Board approved all research procedures for this study.

Measures
We assessed the extent to which participants agreed or disagreed with the following statements: "I think this product might taste good"; "I think this product might smell nice"; "This product looks like it is high-quality tobacco"; "This product looks like it is used to smoke marijuana"; "This product looks like it is fresh"; "This product looks like it is harsh-tasting"; "This product looks like it is harmful to your health." All responses were on five-point scales ranging from "Strongly disagree" to "Strongly agree." For analyses, we dichotomized all outcome measures such that 1 = "Strongly agree" and "Agree" and 0 = "Strongly disagree," "Disagree," and "Neither agree nor disagree." We reverse coded responses related to perceptions of the product being "harsh-tasting" and "harmful to your health," such that the outcomes were "not harsh-tasting" and "not harmful to your health." Then, respondents were asked about their intention to purchase the product using a modified version of the Juster scale [41]; we asked respondents, "What is the likelihood that you will buy this product in the future?" Responses were on a five-point scale ranging from "Certain or practically certain" to "No chance or almost no chance." For analyses, we dichotomized all outcome measures such that 1 = "Certain or practically certain" and "Probable" and 0 = "Some possibility," "Very slight possibility," and "No chance or almost no chance." To assess cigar use, participants were asked, "Do you currently use a [CIGAR TYPE] every day, some days, rarely, or not at all?" for traditional cigars, cigarillos for tobacco, cigarillos for marijuana/blunts and filtered cigars. Respondents were categorized as a user of each product if they reported using the product every day or some days. Given that participants were being exposed to images of Black & Mild and Swisher Sweets packaging, we created a dichotomous Black & Mild and Swisher Sweet brand preference variable (i.e., usually uses/does not usually use), since participants may view their own brand differently, and as such, this could influence their responses. Brand preference was assessed for each cigar type among past-year users of each product with the following question: "What brand of [CIGAR TYPE] do you use most often?" Black & Mild and Swisher Sweets were response choices for both cigarillos and filtered cigars, since both brands sell both cigar types-as such, we used response to the cigarillos and filtered cigar item.
The following demographics were assessed: gender (male, female), age, race (white, Black or African-American, other), ethnicity (Hispanic/Latino, non-Hispanic/Latino), sexual orientation (sexual minority, heterosexual), student status (part/full time student, not a student), highest level of education completed (12th grade or less, some college or greater), employment (full-time, part-time, not currently working), and income ($30,000 or less, greater than $30,000).

Data Analyses
First, we conducted bivariate analyses to confirm that respondent characteristics did not differ by experimental condition. We found that experimental conditions differed by age, gender, and employment; therefore, all multivariable models included these variables as covariates. Next, we used descriptive statistics to characterize the sample in terms of demographics and cigar use behavior. Then, we conducted chi-square tests to estimate the proportion of respondents in each experimental condition who reported agreement with each of the study outcomes. Next, separate multivariable logistic regression models were used to examine the association between brand, brand name, color, and price promotion, and each study outcome among the overall sample and stratified by brand, controlling for age, gender, and employment. Stratified models also controlled for whether the user's preferred brand was the same as the brand in the given condition (Swisher Sweets or Black & Mild). Lastly, interaction terms were added to all the stratified models to examine the combined effects of the different experimental conditions, above and beyond their main effects and covariates. All statistical tests used a critical alpha of 0.05 and were conducted in Stata/MP 16.1 [42]. Table 1 presents all participant demographics and tobacco use behaviors. The mean age of participants was 26.5 years, and a little over half (57%) were employed full-time. More than half of the sample was male (63%) and white (75%). α Not mutually exclusive groups, * Defined as every day or some days among past 12-month users.

Pack Color
Overall, pack color affected perceptions of the product tasting good, smelling nice, and not being harsh (Table 2). However, analyses stratified by brand showed that there was no main effect of package color for Swisher Sweets (Table 3). Interestingly for Swisher Sweets, the color condition interacted with the brand name condition on the perception that the product was fresh; use of the abbreviated brand name (i.e., Swisher) reduced perceptions of freshness only when the pack was brown; green and purple packs were unaffected by brand name abbreviation (p < 0.05).
On the other hand, pack color increased several favorable perceptions of Black & Mild products (Table 4). Specifically, the purple pack was more likely to be perceived as smelling nice (p < 0.05), the green pack was more likely to be perceived as being used for marijuana (p < 0.05), and both the green and purple packs were more likely to be perceived as tasting good, being fresh, and not being harsh, compared to the brown pack.

Price Promotion
Although price promotion did not affect perceptions or intentions in the overall sample (Table 2), it affected different perceptions when stratified by brand. For Swisher Sweets, the presence of a price promotion reduced perceptions that the product was used for marijuana (p < 0.05) ( Table 3). For Black & Mild, a price promotion reduced perceptions that the product tasted good and was high quality (both ps < 0.05), but it increased intentions to purchase (OR = 1.90, p < 0.01) ( Table 4).

Brand Name
Use of the abbreviated brand name reduced favorable perceptions of the product compared to use of the full brand name, and it was associated with lower perceptions of the product tasting good, smelling nice, and not being harsh (all ps < 0.01) ( Table 2). When examining these findings by brand, there were some differences for Black & Mild but not Swisher Sweets. When the brand was Black & Mild, use of the abbreviated brand name (i.e., B&M) reduced perceptions of the product smelling nice (p < 0.01), being high quality tobacco (p < 0.05), and not being harsh (p < 0.001) ( Table 4). There was a negative effect of the abbreviated brand name on intention to purchase, but it was only borderline significant (p = 0.05).

Discussion
The findings of this study illustrate that packaging plays an important role in shaping young adult cigar users' perceptions of cigar products. It is worth noting that packaging elements such as color can particularly appeal to younger adults who may be experimenting with tobacco use. Our findings align with previous studies that have shown the effect of packaging on various product perceptions [31][32][33][34][35]. Favorable perceptions, such as the belief that the product tastes good, is not harsh, and is not harmful, have been linked with various behavioral outcomes, such as intentions to purchase.
Our findings showed color to be an influential packaging feature. For Black & Mild, we found that colorful packaging (i.e., purple and green) was associated with more favorable perceptions of the product compared to brown packaging. Furthermore, our interaction models showed that for Swisher Sweets packaging, color overpowered the impact of another element of the packaging: brand logo. Our findings build on the existing literature pertaining to the effects of color in the context of other tobacco products, including cigarillos [31]. Consumers assess a product based on the packaging in a process called sensation transference [30]. For example, people associate certain colors with certain flavors and flavor attributes. Our findings, which show that colorful packaging impacts perceptions of taste, smell, freshness, and harshness even without explicit flavor descriptors, suggest that color conveys similar meanings in the context of cigarillo packaging. As such, this raises a critical question as to whether products that undergo changes to the color of the packaging should be considered new tobacco products under the U.S. Food and Drug Administration (FDA) Center for Tobacco Products' deeming rule, rather than equivalent to the original product, due to the differential impact they have on perceptions [31]. Currently, changes to the design of the package are not considered for FDA substantial equivalence reviews.
Price promotion was the only packaging element that had direct associations with purchase intentions. Specifically, the presence of a price promotion increased intentions to purchase Black & Mild products, despite being associated with perceptions of worse taste and lower quality of the tobacco. Similarly, previous studies have found that price promotions on the packaging drove interest in the product [33].
Use of the abbreviated brand name (B&M) reduced favorable perceptions of the product and, importantly, it increased perceptions of harshness, compared to use of the full brand name (Black & Mild). This points to the potential positive influence of the word "Mild" in the brand name, which is undoubtedly important to Altria, the owners of Black & Mild. With the passing of the 2009 Family Smoking Prevention and Tobacco Control Act (TCA), the FDA was granted the authority to regulate tobacco products. The TCA banned the use of misleading descriptors that communicated reduced harm claims, including "light" and "mild." Initially, this only included cigarettes, roll-your-own tobacco, and smokeless tobacco, but it was extended to include all tobacco products, including cigars, in 2016 under the deeming rule. Shortly after the deeming rule, Altria filed a lawsuit against the FDA, alleging that removing "mild" from their Black & Mild cigar products "terminates this iconic brand name on the bare supposition that the word mild impermissibly communicates to consumers that Black & Mild products are safer than other cigars and pipe tobacco" [43]. Shortly after, the lawsuit was dropped and the chairman, president and CEO of Altria Group stated during the company's second-quarter earnings call that FDA indicated that they did not, at that time, plan to enforce the ban on "mild" in regard to Black & Mild [44]. Our findings show that the allowance of "mild" in the Black & Mild name, despite being a banned term for communicating misperceptions of harm, does indeed influence product perceptions related to harshness.
This study has limitations. First, the images used in this study were manipulated in terms of the various packaging elements. However, in an effort to closely mimic what consumers see in the marketplace, we used existing brands, improving external validity compared to past studies. This study also uses a convenience sample, which may not be generalizable to all young adults who use cigars in the U.S. However, research shows that experimental findings obtained via Amazon Mechanical Turk are comparable to findings from national probability samples [38].

Conclusions
Our study demonstrates that cigarillo packaging characteristics influence young adult consumers' product perceptions, including their perceptions of taste, smell, harshness, and the quality of tobacco. Our findings also raise questions that product features may interact with one another, with certain features, such as color, overpowering other attributes. In addition to the main effects of color on various favorable product perceptions, our finding that the abbreviated brand logo only influenced consumer perceptions when the packaging was brown, demonstrates that color plays an important role in cigarillo packaging not only in what it directly communicates to consumers but in how it influences the impact of other product features. While this study did not include a warning label condition, future studies should examine how color may affect the impact of warning labels on consumer perceptions. Given that the cigarillo marketplace is diverse and constantly in flux, there is a need for continued research on the impact of cigarillo packaging features on consumer perceptions, beyond the brands and attributes explored in this paper.