Research Article Study on the Influence of Government Intervention on the Occupational Health and Safety (OHS) Services of Small- and Medium-Sized Enterprises (SMEs) Jingjing Zhang , Qiang Mei , Suxia Liu , and Qiwei Wang School of Management, Jiangsu University, Zhenjiang 212013, China Correspondence should be addressed to Qiang Mei; [email protected] Received 16 May 2018; Accepted 10 October 2018; Published 25 October 2018 Academic Editor: Peter P. Egeghy Copyright © 2018 Jingjing Zhang et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. e OHS services of SMEs are still in their start-up stage in China. As such, there is an absence of mature market norms, which in turn makes it difficult to guarantee the quality of OHS services. e government, as the “night watchman” of the market, is supposed to not only involve itself in the regulation of OHS service quality, but also introduce and implement proper regulatory strategies. is paper employs a computational experiment approach to construct an experimental platform based on multiagent interactions. By simulating the OHS service transaction activities of SMEs, this paper takes the perspective of dynamic evolution. From this perspective, we probe into the optimal regulatory strategy covering the positive influence of government punishment, policy supports, and service quality ratings of the OHS services of SMEs. ese strategies should be built on the foundation of proper punishment standard and intensity, proper support standard and intensity, and quality rating information disclosure. 1. Introduction More and more Chinese companies actively adopt OHSAS18001 system in order to adjust to the international competition order. However, Chinese small- and medium- sized enterprises (SMEs) are confronted with various difficulties in building the occupational health and safety management systems (OHSAS) due to the limitations in scale, labor power, and economic power. At present, many countries’ governments are trying to shiſt the corporations’ occupational health and safety responsibilities to private agencies, so as to lessen the “legislative burden” and use the “market” to improve occupational health & safety (OHS) system [1]. Researches show that the marketized supply of OHS services helps the corporations prevent safety accidents, reduce cost, save time, facilitate the employees’ flexibility, and improve the overall quality of OHS plan [2, 3]. Successful OHS services employ the service provider’s unique expertise and resources to help the corporations actuate their occupation health and safety goals [4]. us, to help SMEs strengthen their international competitiveness, and also to turn around their historically poor safety records, the Chinese government encourages SMEs to seek professional OHS services from third-party service agencies. ese agencies can help facilitate SMEs’ construction, operation, and improvement of their OHSAS18001 OHS management systems. In other words, these third-party agencies can help SMEs provide a professional supply of OHS services. It is in this context that OHS services develop rapidly in China which has become an important means of improving SMEs’ OHSAS18001 management systems. However, China’s OHS services are still in the preliminary stages. More time is needed for the industry norms and self-discipline of the OHS service market to take form. In addition, combined with the apparent lagging of OHS service effect and the high degree of information asymmetry related to service processes, there is a severe lack of any reliable means for purchasers to distinguish different SMEs’ service quality. Due to the combination of these factors, guaranteeing the quality of OHS services is difficult. In addition, the risk SMEs face when purchasing low-quality OHS services is significant [5]. When low-quality OHS services are purchased by an SME, this not only makes it difficult to avoid safety risks, but also increases the SMEs’ safety costs and dampens Hindawi BioMed Research International Volume 2018, Article ID 5014859, 15 pages https://doi.org/10.1155/2018/5014859
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Research ArticleStudy on the Influence of Government Intervention on theOccupational Health and Safety (OHS) Services of Small- andMedium-Sized Enterprises (SMEs)
Jingjing Zhang , QiangMei , Suxia Liu , and Qiwei Wang
School of Management, Jiangsu University, Zhenjiang 212013, China
Correspondence should be addressed to Qiang Mei; [email protected]
Received 16 May 2018; Accepted 10 October 2018; Published 25 October 2018
Academic Editor: Peter P. Egeghy
Copyright © 2018 Jingjing Zhang et al.This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The OHS services of SMEs are still in their start-up stage in China. As such, there is an absence of mature market norms, whichin turn makes it difficult to guarantee the quality of OHS services. The government, as the “night watchman” of the market, issupposed to not only involve itself in the regulation of OHS service quality, but also introduce and implement proper regulatorystrategies. This paper employs a computational experiment approach to construct an experimental platform based on multiagentinteractions. By simulating the OHS service transaction activities of SMEs, this paper takes the perspective of dynamic evolution.From this perspective, we probe into the optimal regulatory strategy covering the positive influence of government punishment,policy supports, and service quality ratings of the OHS services of SMEs. These strategies should be built on the foundation ofproper punishment standard and intensity, proper support standard and intensity, and quality rating information disclosure.
1. Introduction
More and more Chinese companies actively adoptOHSAS18001 system in order to adjust to the internationalcompetition order. However, Chinese small- and medium-sized enterprises (SMEs) are confronted with variousdifficulties in building the occupational health and safetymanagement systems (OHSAS) due to the limitations inscale, labor power, and economic power. At present, manycountries’ governments are trying to shift the corporations’occupational health and safety responsibilities to privateagencies, so as to lessen the “legislative burden” and use the“market” to improve occupational health & safety (OHS)system [1]. Researches show that the marketized supplyof OHS services helps the corporations prevent safetyaccidents, reduce cost, save time, facilitate the employees’flexibility, and improve the overall quality of OHS plan [2, 3].Successful OHS services employ the service provider’s uniqueexpertise and resources to help the corporations actuatetheir occupation health and safety goals [4]. Thus, to helpSMEs strengthen their international competitiveness, andalso to turn around their historically poor safety records, the
Chinese government encourages SMEs to seek professionalOHS services from third-party service agencies. Theseagencies can help facilitate SMEs’ construction, operation,and improvement of their OHSAS18001 OHS managementsystems. In other words, these third-party agencies can helpSMEs provide a professional supply of OHS services. It isin this context that OHS services develop rapidly in Chinawhich has become an important means of improving SMEs’OHSAS18001 management systems.
However, China’s OHS services are still in the preliminarystages. More time is needed for the industry norms andself-discipline of the OHS service market to take form. Inaddition, combined with the apparent lagging of OHS serviceeffect and the high degree of information asymmetry relatedto service processes, there is a severe lack of any reliablemeans for purchasers to distinguish different SMEs’ servicequality. Due to the combination of these factors, guaranteeingthe quality of OHS services is difficult. In addition, the riskSMEs face when purchasing low-quality OHS services issignificant [5].When low-qualityOHS services are purchasedby an SME, this not only makes it difficult to avoid safetyrisks, but also increases the SMEs’ safety costs and dampens
HindawiBioMed Research InternationalVolume 2018, Article ID 5014859, 15 pageshttps://doi.org/10.1155/2018/5014859
2 BioMed Research International
their enthusiasm for purchasing OHS services [6, 7]. Thepractice of purchasing OHS services also departs from theoriginal intention of the Chinese government when theybegan encouraging the development of OHS services. Thus,there is an urgent need to reduce the risk of purchasingshoddy service, guarantee the service quality, and promotethe well-being of OHS services market. The objectives of thisstudy are how to address the above-mentioned issues.
In general, the quality control of products is uniformlyconducted by the principal, as agreed in the contract. How-ever, due to the complexity of safety-related accidents andthe lagging of service effect, it is very difficult for SMEs(as the principal) to distinguish rights and liabilities andcontrol service quality via commercial contracts, as wouldnormally be the case with ordinary products. In this case,third-party intervention is needed to assist management. Thequality of OHS services is a matter of concern as regardssocial public security. OHS service quality constitutes abranch of public administration, and the government has acompelling obligation in terms of public service management[8]. As such, it is logical for the government’s functionaldepartments to intervene in the management of OHS servicequality. In consideration of the government’s position in theOHS service market and the market response combined,which is different from the government’s direct supervisionof the corporations, it is an urgent theoretical and practicalproblem to explore what intervention measures the govern-ment should take to realize the virtuous development ofOHS service market to help SMEs build the OHSAS18001system efficiently. Therefore, this paper attempts to comparethe intervention measures taken by the government in thedevelopment of the OHS service market and discuss whichintervention measures can bring about effective functioningof the OHS service market system. Here, effective means thatSMEs are willing to purchase the high-quality OHS service.In other words, SMEs are willing and able to establish theOHSAS18001 system efficiently through market mechanisms.Accordingly, in order tomeasure the effectiveness of differentintervention, one needs to analyze whether or not enterpriseswill purchase services under a specific government interven-tion and the quality of the purchased services.
The OHS service market in the real world is a large andcomplex system that requires the cooperation of government,service organization, and SMEs. Due to the complexity ofthe system environment, the incompleteness of informationamong transaction agents, the behavioral bias and framingeffect of experiential drive, and the nonlinear associationsamong elements, the evolution of the system presents aninstability and polymorphic equilibrium. This means thatboth traditional empirical study method and game simu-lation are not applicable to the research on the effects ofgovernment intervention. In order to study the impact ofgovernment interventions from a dynamic and boundedrational perspective, this paper employed multiagent-basedcomputational experiment simulation method. The purposeof this method is to construct a controlled and reproduciblegovernment intervention model for OHS service marketbased on heterogeneous subjects and simulate different inter-ventions so that it is possible to observe the market status.
The contribution of this study is the tentative applicationof the multiagent-based computational experiment simula-tion method on the OHS service market studies. Throughthe simulated evolution based on real situations, it observesthe market response when the government takes differentmeasures, which further proves and compareswhichmeasurecan genuinely promote the sound development of OHSservice market, so as to help SMEs make their OHS planefficiently. This study not only fills the research gap from atheoretical perspective but also informs policy-making andpractice.
2. Literature Review
Through reviewing the literature, the researchers found thatprevious studies on OHS services and government regulationmainly focused on the supervision of OHSAS18001 certi-fication system, which discussed the relationship betweenthe government and the enterprises. Different from thedirect supervision of the government on the enterprises,the government's participation in the OHS service marketintervention is concerned with the triangular relationshipbetween the government, enterprises, and OHS service orga-nizations. Researches that are directly relevant to this studyare few and far between. However, it should be noted thatenterprise security resulting from enterprise OHSAS18001certification is an important part of public security. Besides,OHS service market could also be considered as an importantpart of public service market. Therefore, the research onthe OHSAS18001 certification system supervision and publicservice market supervision may serve to inspire the currentstudy.
2.1. Studies on OHSAS18001 Certification System Supervision.The OHSAS18001 certification is an international standardjointly launched by 13 organizations, including the BritishStandard Institute (BSI) and the Det Norske Veritas (DNV).The purpose of this certification is to urge the enterprise thathas received the certificates to ensure the maximum healthsecurity of its employees and adequately protect the propertysafety of itself.
A comparative study by Santos [9] focused on the partici-pation of Portuguese small- and medium-sized companies inOHSAS18001 certification. The result shows that companiesengaged in certification are better at preventing security risksthan those that do not. Some researchers have studied howto motivate companies to participate in OHSAS certification.For example, Olsen [10] believes that the government plays animportant role in guiding small businesses to participate inOHSAS certification. Kvorning [11] believes that small enter-prises have limited resources and therefore are incapable ofactively participating in OHSAS certification. However, gov-ernment regulation, trade union intervention, and networkassistance can encourage the initiative of the small enter-prises. Several researchers have examined how to improve thelevel of OHSAS18001 certification. Hasle P [12] argues thatthe policy instrument which jointly combines government,business owners, and trade unions can effectively improve
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the certification level. Legg et al. [13] think that, in orderto improve the certification level, it is necessary to considerthe specific characteristics of SMEs and strengthen the gov-ernment's inspection of the compliance of enterprises duringthe certification process. Okun [14] believes that governmentcould participate in safety certification with institutes liketrade organizations and trade union organizations and usethe existing communication channels to address high-riskworkplace safety and health issues which can improve acompany's certification level. A few researchers have studiedhow to ensure the effectiveness of OHSAS certification. Somebelieve that the joint participation of government regulatorsand national security assessment departments, laws andregulations, policies, resources, information, organization,planning, and implementation are all key factors to guaranteethe effectiveness of OHSAS certification [15–17].When intro-ducing government regulation, it is important to considerthe diversity characteristics of SMEs [18]. Besides, the risk-oriented approach of government regulation focuses onregulatory standards development and enforcement activitieson the highest priority risks through various risk-basedindicators and policy tools, which makes regulation moreproportional and effective [19]. Some researchers studied thefailure of OHSAS certification system and found that if thepolicymakers are too optimistic about the operation of thecompulsory certification, the regulation has its limitations,and the lack of institutional factors leads to operationalfailure [20–23]. In face of certification system failure, thegovernment needs to strengthen the certification enterpriseintervention. Not only should the regulation of the certifica-tion enterprise be strengthened, but the technical, financial,and material support to the certification enterprise shouldalso be provided [24–27].
Previous studies have shown that the well-functioning ofthe OHSAS18001 certification system will effectively improvethe safety level of SMEs, and the operation of the systemneeds to be strengthened by government regulation. How-ever, whether it is to encourage enterprises to participate incertification or to prevent the certification system from failingthrough regulation, previous studies only focus on the rela-tionship between the government and the enterprises, and theenterprises are regulated entities of the government. In thestudies that further explore government regulation methods,the empirical methods or game methods are used to measurethe effects of regulation under a single method. There is nocomparison between different supervision methods, and noin-depth study on how to control the intensity and frequencyof regulation strategies can be found.
2.2. Studies on Government Regulation of Public ServiceMarket. Government purchase of public service is a newmode of providing services for citizens by the government,which solves the problem of the government being theonly public services provider. The government’s function hasbeen changed greatly and it becomes more service-orienteddue to the new mode [28]. However, it should be notedthat the government purchase of public service does notmean that the government will transfer its responsibility
to social organizations. Still it is supposed to shoulder theresponsibility of fostering a competitive public service supplymarket, fully support public services, strengthen contractoversight, strengthen the capacity to manage throughout theprocurement process, and improve the quality of services[29, 30], since the outsourcing of public service contractsfaces various risks, such as moral hazard and rent-seekingthat act against fair competition and equity risk [31]. Besides,the market is full of high uncertainties and challengesthat may cause the deviation of government public serviceoutsourcing.Therefore, the risk-based supervision of the gov-ernment is needed to monitor enterprise performance andassess contractor performance [32, 33]. In the government-directed social supervision model, the government relies onadministrative law enforcement to formulate market rulesand standards, guides market credit and price mechanismsin the form of services and penalties, and makes publicinterests a starting point to disclose market information andinduce rational production and consumption [32, 34, 35].With regard to the supply of rural public goods in China, thegrassroots government needs to better reflect its functions inestablishing scientifically sound market approval and with-draw criteria, monitoring competition and price systems, andcurbing corruption [36]. In the market-oriented operation ofpublic sanitation services, the government fosters, regulates,develops, and supervises the market through price, financialsubsidies, and taxation, in order to improve the quality ofpublic sanitation services. In addition, the government alsoacts as a decision-maker ofmarket rules, a procurement agentfor public sanitation services, and a supervisor of marketoperation [37].
These studies often refer to the administrative and lawenforcement means that is necessary for the governmentin different fields from various perspectives by means ofcase study, empirical research, and other methods. Thegovernment perform its market supervision duties throughprice guidance, information disclosure, financial subsidyincentives, and penalties for breach of laws and regulations.However, there is no in-depth study on the advantages anddisadvantages of these regulatory methods and how to drawthe lines for the supervision. Different from the traditionalpublic service market, in the OHS service market, the gov-ernment is not a direct service purchaser.The two sides of themarket are SMEs and service organizations, respectively, andthe purpose of government supervision is not only to protectthe market, but also to encourage and help more SMEs toestablish OHS18001 system certification efficiently. Therefore,OHS service market regulation faces more challenges thanthe general public service market regulation.
In summary, whether it is the OHSAS18001 certificationsystem or public services, all researchers emphasize theimportance of government regulation. However, few studieshave analyzed the different intervention strategies that mayguarantee the effective functioning of OHS services marketfrom the perspective of government regulation. Differentfrom direct supervision of the enterprise by the government,the priority at present is how to link the role of the govern-ment in the OHS service market with its market reaction andexplore what kind of intervention strategy the government
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should adopt so as to achieve the sound development of themarket, which are still theoretical and practical problems thatneed to be solved. Many current studies on the governmentsupervision of OHSAS18001 certification system focus onthe relationship between the government and enterprises byempirical or game analysis.The research on the supervision ofpublic service market discusses the influence of governmentregulation from the perspectives of government punishment,government support, andmarket information disclosure.Theenvironment considered is simple and static, which neitherreproduces the dynamic and complex system situation norcompares the market operation effect under different inter-vention strategies.
In real life, as seen from the micro perspective, OHSservice activities constitute a transaction subsystem thatconsists of two types of activities. Those two activities arethe purchase of OHS services by SMEs and the provisionof OHS services by third-party professional service agencies.As seen from the macro perspective, however, OHS serviceactivities become a complex macro system that consistsof numerous micro subsystems. Therefore, the traditionalresearch methods are difficult to achieve the purpose ofcomparing intervention strategies and measuring the effectof market intervention. Themultiagent-based computationalexperimental method adopted in this study is a methodusing computer technology to construct experiment object,experiment environment, and experiment platform, simulatethe dynamic law of material movement in the real world, andcarry out experimental research on scientific problems [38].
It is a research method in the natural sciences studies,mostly applied in the studies of uncertainty and networkevolution. Now this method is gradually extending into thesocial sciences studies and is applied well in these fields,making it a useful tool in studying system complexity. Forexample, Wang and Singh [39] employed this method toexpand the behavioral economics theory and yielded thefinancial mathematical model; Hafezi et al. [40] simulatedthe operation of financial market to predict the share priceby constructing a four layer multiagent framework; Behdaniet al. [41] used the multiagent-based modeling in the envi-ronmental management studies to depict the complexityof the government’s management of environmental issues;Santos et al. [42] constructed multilayer multiagent modelsto study the change in behavior of different subjects in theelectricity market; Meng et al. [43] developed multiagent-based modeling framework to simulate the operation ofsupply chains, comparing the competitive powers betweensupply chains; and Shafie-khah and Catalao [44] simulatedthe operation model of electricity market to compare theoperation performances in different situations by depict-ing the behaviors of the service provider, supervisor, andrelative participants based on complicated and changefulreal situations. These studies all have made contribution tothe application of this method in social sciences studies.Therefore, on the basis of existing research, this paperconstructs a dynamic model of government participation inOHS service market supervision and selects three commonintervention methods, i.e., punishment, government support,andmarket informationdisclosure, in order to test themarket
reaction under the three means. Not only can the advantagesand disadvantages of various interventions be compared,but the strengths and limitations of various measures canalso be better understood, not to mention the reference thisresearch could provide for the government about the specificinterventions. It is hoped that this study could promote thesound development of the OHS market and bring about theimprovement in the quality of OHS enterprises.
3. Methods
3.1. Experimental Framework. This paper abstracts the trans-action process of the OHS services of SMEs from real-ity for simulation purposes. The related agents involvedtherein include SMEs (Factory agent), third-party pro-fessional OHS service agencies (Agency agent), and thegovernment (Government agent). The interactive relation-ships between these agents are manifested as follows: (1)Agency agent provides OHS services, and Factory agentmakes the decision whether or not to purchase OHSservices. (2) When low-quality OHS services are pur-chased, Factory agent faces safety-related accident risks. (3)Government agent intervenes with regard to OHS services,and different intervening behaviors influence the OHS ser-vices of SMEs in different manners. See the specific interac-tion process in Figure 1.
In order to make the model closer to the real situation,the analysis is founded on the following five assumptionsin the system development: (1) High-quality OHS servicescan effectively improve the OHSAS18001 safety system ofSMEs and reduce the occurrence of safety-related accidents.(2) Due to requirements of relevant laws and safety sys-tem certification, there is no intentional reduction of OHSservice quality after purchasing service of SMEs. (3) Dueto capability limitations and information asymmetry, it isdifficult for SMEs to independently distinguish the quality ofthe OHS services they have purchased. (4) In reality, whenSMEs (due to limitations in terms of scale, manpower, andtechnology) choose to self-construct and implement theirown OHSAS18001 safety systems, they have to pay a highersafety production cost.Therefore, it is assumed that their self-construction cost is higher than the cost would be to purchaseOHS services. (5) The prices of OHS services provided bydifferent OHS service agencies on the market are reasonablysimilar. Therefore, it is difficult for enterprises to judge thereal quality of OHS services based on the price of thoseservices alone.
3.2. Activity Simulation andMultiagentModel Building for theOHS Service Market
3.2.1. Behavioral Decision-Making Design of Factory AgentPurchasing OHS Services. The system creates m Facto-ry agents, each of which has to make the decision whether ornot to purchase OHS services.The Factory agents can chooseto independently complete their OHS-related work. That is,they can adopt the no-service-purchasing strategy j1, or theycan seek professional OHS services from professional OHS
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Factory_agent
Whether to purchaseOHS services?
Agency_agent
Provision of high-qualityOHS services?
Improvement of OHSmanagement system
and elevation of safetylevel
Failure on the part ofFactory_agent to
elevate safety level
Occurrence ofsafety-related accidents
Self-construction ofOHS management system
Government_agentIntervention
N
N
Y
Y
Figure 1: Interaction process of OHS service transactions.
service agencies, which is effectively adopting the service-purchasing strategy j2. In the former case, the Factory agentshave to pay a certain safety cost for putting in place theOHSAS18001 safety system. In the latter case, they havesaved the safety cost, but, due to the asymmetry of marketinformation, they face the risk of failing to truly bring theOHSAS18001 safety system into play, due to the possibility ofpurchasing low-quality services. The revenue functions of aFactory agent under the two strategies are Ui(jj) (j=1,2) as,respectively, given below:
𝑈𝑖 (𝑗1) = 𝐹𝐹actory−𝑎𝑔𝑒𝑛𝑡 (𝑠𝑡) − 𝐶𝐹actory−𝑎𝑔𝑒𝑛𝑡 (𝑠𝑡) (1)
𝑈𝑖 (𝑗2) = 𝐹𝐹actory−𝑎𝑔𝑒𝑛𝑡 (𝑠𝑡) − 𝑃 (𝑠𝑡) − 𝛽𝐷 (2)
The study adopts the research of safety economy [45,46], function 𝐹𝐹𝑎𝑐𝑡𝑜𝑟𝑦-𝑎𝑔𝑒𝑛𝑡(S) represents safety income ofenterprises, and function 𝐶𝐹𝑎𝑐𝑡𝑜𝑟𝑦-𝑎𝑔𝑒𝑛𝑡 (S) represents safetycost of enterprises. Safety income includes safety reductionL(S) and safety increase I(S), 𝐹𝐹actory−𝑎𝑔𝑒𝑛𝑡(𝑠𝑡) = 𝐿(𝑠) + 𝐼(𝑠).Safety reduction L(S) represents when safety level s increases,damage and loss will reduce; function is (3). Safety increaseI(S) represents when safety level s increases, the service lifeof equipment extends and productivity increases; function is
(4). Safety cost 𝐶𝐹𝑎𝑐𝑡𝑜𝑟𝑦-𝑎𝑔𝑒𝑛𝑡 (S) represents when safety levelS increases, safety cost will be higher; function is (5). L,l, I, i,𝐿0, CF, 𝐶0𝐹 are constant. If the purchased OHS services havefailed to genuinely bring the OHSAS18001 safety system intoplay, the Factory agent will face the risk of accidents. To bespecific, when 𝛽=1, this means that an accident has occurred.When 𝛽=0, no accident has occurred. Also, P(S) representsthe purchasing price of OHS services, and D represents theaccident losses suffered by the Factory agent.
𝐿 (𝑠) = 𝐿 ⋅ 𝑒(𝑙/𝑠) + 𝐿0(𝐿 > 0, 𝑙 > 0, 𝐿0 > 0)
(3)
𝐼 (𝑠) = 𝐼 ⋅ 𝑒(−𝑖/𝑠) (𝐼 > 0, 𝑖 > 0) (4)
𝐶𝐹𝑎𝑐𝑡𝑜𝑟𝑦−𝑎𝑔𝑒𝑛𝑡 (𝑠) = 𝐶𝐹 ⋅ 𝑒[𝑐𝐹/(1−𝑠)] + 𝐶0𝐹(𝐶𝐹 > 0, 𝐶0𝐹 > 0)
(5)
This paper adopts the EWA learning algorithm [47] todepict enterprise behaviors. The algorithm assumes that eachstrategy has a numerical attractiveness index, and introduces
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certain rules to determine the probability of selecting eachstrategy. See the specific updating formula below:
𝑁𝐹 (𝑡) = 𝜌𝑁𝐹 (𝑡 − 1) + 1 (6)
𝐴𝑗𝑗𝐹𝑖 (𝑡)
=𝑁𝐹 (𝑡 − 1) 𝜑𝐴
𝑗𝑗𝐹𝑖(𝑡 − 1) + [𝜕 + (1 − 𝜕) 𝐼𝐹 (𝑗𝑗)]𝑈𝑖 (𝑗𝑗)
𝑁𝐹 (𝑡)
(7)
wherein 𝑁𝐹(𝑡) represents the empirical weight; 𝜌 rep-resents the discount factor of past experience; 𝐴𝑗𝑗𝐹𝑖(𝑡) rep-resents the attractiveness index of the strategy 𝑗𝑗 (j=1,2) tothe Factory agent; that is, the higher the value, the higherthe probability of adopting this strategy; 𝜑 represents thediscount factor of past attractiveness index; 𝑈𝑖(𝑗𝑗) repre-sents the expected revenue of the Factory agent, wherethe Factory agent will update the corresponding revenueaccording to its specific status; 𝜕 represents the discountfactor of future strategy payments or opportunity costs; thatis, the higher the value of 𝜕, the higher the importanceattached to or the expectations held for the strategy by theFactory agent. Finally, 𝐼𝐹(𝑗𝑗) is an indicative function, where𝐼𝐹(𝑗𝑠) = 1means that the strategy is adopted; thus
𝐴𝑗𝑗𝐹𝑖 (𝑡) =𝑁𝐹 (𝑡 − 1) 𝜑𝐴
𝑗𝑗𝐹𝑖(𝑡 − 1) + 𝑈𝑖 (𝑗𝑗)
𝑁𝐹 (𝑡)(8)
When 𝐼𝐹(𝑗𝑗) = 0, itmeans that the strategy is not adopted;thus
𝐴𝑗𝑗𝐹𝑖 (𝑡) =𝑁𝐹 (𝑡 − 1) 𝜑𝐴
𝑗𝑗𝐹𝑖(𝑡 − 1) + 𝜕𝑈𝑖 (𝑗𝑗)
𝑁𝐹 (𝑡)(9)
In the EWA learning algorithm, the attractiveness indexwill determine the probability of each strategy being selected.In other words, the higher the attractiveness index, the higherthe probability of the strategy concerned being selected. Thispaper uses the logit response function [48] to express theprobability of strategy 𝑗𝑗 being selected by the Factory agent,wherein 𝜆 is used to measure the sensitivity of the attrac-tiveness index in decision-making. When j=1, it means thatenterprise i has a probability of𝑃rob𝑗1i (𝑡+1) of choosing not topurchase service strategy 𝑗1 in phase t+1. When s=2, it meansthat enterprise i has a probability of 𝑃rob𝑗2i (𝑡 + 1) of choosingto purchase service strategy 𝑗2 in phase t+1.
𝑃rob𝑗𝑗i (𝑡 + 1) =exp (𝜆𝐴𝑗𝑗𝐹𝑖 (𝑡))
∑2𝑗=1 exp (𝜆𝐴𝑗𝑗𝐹𝑖(𝑡))
(10)
3.2.2. Occurrence of Factory Agent Accidents. When low-quality OHS services are purchased, the OHSAS18001 safetysystemof the Factory agent has failed to be genuinely broughtinto play. In such cases, the Factory agent faces a certainoccurrence probability of accidents. Given that the safety levelSt (St<1) of the Factory agent is inversely proportional to
Table 1: Safety state table of Factory agent.
State Safe UnsafeProbability St 1-StCumulative probability St 1
the occurrence probability of accidents, the model (for thepurpose of better embodying the randomness of accidents)adopts a roulette mode to simulate the occurrence of acci-dents, as detailed below:
A Step 1: Determine the safety state of the Factory agentaccording to its safety level St, as indicated in Table 1.
B Step 2: Generate the randomnumber R=random(0,1).C Step 3: CompareRwith St .𝑅 ≤ 𝑆𝑡, there is no accident.
When 𝑆𝑡 ≤ 𝑅 < 1, an accident occurs.
3.2.3. Behavioral Decision-Making Design of Agency AgentProviding OHS Services. Agency agents’ aim is to provideprofessional OHS services to Factory agents and to thus helpthe latter realize and improve their OHSAS18001 safety sys-tems and elevate their overall safety levels. However, the pro-vision of OHS services by Agency agents to Factory agents isa market behavior. Agency agents (as market agents assum-ing sole responsibility for their profits or losses) take the max-imization of profits as their primary objective. At the begin-ning of the experiment, the system creates n Agency agents,and the total profit made by each Agency agent in each phaseis determined by the profit from a single business transactionand the volume of business transactions. Theoretically, thesize of the total profit is inversely proportional to the costof a single business transaction and is directly proportionalto both the profit from a single business transaction and thevolume of business transactions. To be specific, the higherthe quality of services provided by Agency agents, the higherthe business cost and, correspondingly, the lower the profitfrom a single business transaction. Thus, Agency agents havethe motivation to provide low-quality services. However, thesize of the total profit is influenced not only by the profitfrom a single business transaction, but also by the volumeof business transactions. Due to the asymmetry of marketinformation, it is difficult for Agency agents to judge how tomake the optimal decisions that can attract more businessand maximize their profits. As a result, Agency agents willconstantly adjust their behavioral decision-making in eachcycle, based on market changes and previous experience. Inthe samemanner, the EWA learning algorithm is employed todepict the behavioral decision-making of Agency agents. Tobe specific, the decisions made by Agency agents with regardto the provision of OHS services include three types, namely,(1) improving the quality of OHS services, (2) keepingthe quality of OHS services constant, and (3) reducing thequality of OHS services. The expected revenue functionscorresponding to the three types of decisions are 𝜋𝑖(𝑘𝑗)(j=1,2,3), as, respectively, given below:
𝜋𝑖 (𝑘1) = 𝑇 (𝑠+) ⋅ 𝑄 (𝑠+) − 𝜃 ⋅ 𝐼𝑉 (11)
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𝜋𝑖 (𝑘2) = 𝑇 (𝑠) ⋅ 𝑄 (𝑠) − 𝜃 ⋅ 𝐼𝑉 (12)
𝜋𝑖 (𝑘3) = 𝑇 (𝑠−) ⋅ 𝑄 (𝑠−) − 𝜃 ⋅ 𝐼𝑉 (13)
wherein T(𝑆+), T(S), and T(𝑆-), respectively, representthe volumes of current market businesses acquired in thethree cases described above and Q(𝑆+), Q(S), and Q(𝑆-),respectively, represent the profits from a single businesstransaction made in the three cases described above. When𝜃=1, this means that there is government intervention. When𝜃=0, there is no government intervention. In addition, IVrepresents the government intervention value. Assuming that𝑠 represents the upper limit of service quality for governmentintervention, when 𝑠 < 𝑠, Agency agents are punished by theintervention value IV. When 𝑠 > 𝑠, however, Agency agentsare supported by the intervention value IV. Assuming thatthe OHS services provided by Agency agents in each case canelevate the safety level of Factory agents to s, the profit froma single business transaction can be expressed as
𝑄 (𝑠) = 𝑃 (𝑠) − 𝐶Agency−agent (𝑠) (14)
Here, P(s) represents the price of services provided byAgency agents and 𝐶𝐴𝑔𝑒𝑛𝑐𝑦-𝑎𝑔𝑒𝑛𝑡(s) represents the businesscost paid by Agency agents [34] (normally, the lower thequality of services provided, the lower the business cost tobe paid); that is, 𝐶𝐴𝑔𝑒𝑛𝑐𝑦−𝑎𝑔𝑒𝑛𝑡(𝑠) = 𝐶𝐴 ⋅ 𝑒[𝑐𝐴/(1−𝑠)] + 𝐶0𝐴.Theupdated formula of the learning algorithm [35] adopted isgiven below:
𝑁𝐴 (𝑡) = 𝜌𝑁𝐴 (𝑡 − 1) + 1 (15)
𝐴𝑘𝑗𝐴𝑖 (𝑡)
=𝑁𝐴 (𝑡 − 1) 𝜙𝐴
𝑘𝑗𝐴𝑖(𝑡 − 1) + [𝜕 + (1 − 𝜕) 𝐼𝐴 (𝑘𝑗)] 𝜋𝑖 (𝑘𝑗)
𝑁𝐴 (𝑡)
(16)
wherein 𝑁A(𝑡) represents the empirical weight; 𝐴𝑘𝑗A𝑖 (𝑡)represents the attractiveness index of strategy 𝑘𝑗 (j=1, 2, 3) tothe Agency agent; 𝜋𝑖(𝑘𝑗) represents the expected revenue ofthe Agency agent; and 𝐼A(𝑘𝑗) is an indicative function, where𝐼A(𝑘𝑗) = 1means that the strategy is adopted; thus
𝐴𝑘𝑗𝐴𝑖 (𝑡) =𝑁𝐴 (𝑡 − 1) 𝜙𝐴
𝑘𝑗𝐴𝑖(𝑡 − 1) + 𝜋𝑖 (𝑘𝑗)
𝑁𝐴 (𝑡)(17)
When 𝐼𝐴(𝑘𝑗) = 0, this means that the strategy is notadopted; thus
𝐴𝑘𝑗𝐴𝑖 (𝑡) =𝑁𝐴 (𝑡 − 1) 𝜙𝐴
𝑘𝑗𝐴𝑖(𝑡 − 1) + 𝜕𝜋𝑖 (𝑘𝑗)
𝑁𝐴 (𝑡)(18)
Similarly, the logit response function is adopted to expressthe probability of the Agency agent selecting strategy Kj,wherein 𝜆 is used to measure the sensitivity of the attrac-tiveness index in decision-making. When j=1, this means thatservice agency i has a probability of 𝑃rob𝑘1i (𝑡 + 1) of choosingstrategy K1 in phase t+1. That is, the quality of OHS services
is improved. When j=2, this means that service agency i has aprobability of 𝑃rob𝑘2i (𝑡 + 1) of choosing strategy K2 in phaset+1. That is, the quality of OHS services remains constant.When j=3, this means that service agency i has a probabilityof 𝑃rob𝑘3i (𝑡 + 1) of choosing strategy K3 in phase t+1. That is,the quality of OHS services is reduced.
𝑃rob𝑘𝑗i (𝑡 + 1) =exp (𝜆𝐴𝑘𝑗𝐴𝑖 (𝑡))
∑3𝑗=1 exp (𝜆𝐴𝑘𝑗𝐴𝑖(𝑡))
(19)
3.2.4. “Death” and “Birth” of Agency Agent. In the real world,a service agency may withdraw from the market for verycomplicated reasons. This model, for simulation purposes,has provided simplification to a very high degree. In ourmodel, when the Agency agent meets one of the followingconditions, this means that the Agency agent is forced towithdraw from the market.
A When the actual revenue of the Agency agent is lessthan 0 for T consecutive phases (that is, when 𝜋𝑖 ≤ 0), theAgency agent withdraws from the market.
B According to the model, each Agency agent has afixed asset of GT(t). In addition, the total asset KT(t) isthe sum of the fixed asset combined with the profit ofeach phase, that is, 𝐾𝑇(𝑡) = 𝐺𝑇(𝑡) + ∑𝜋. In cycle t,when the debt of the Agency agent (i.e., penalty amountof punishment ∑ 𝐼𝑉𝑝𝑢𝑛𝑖𝑠ℎ𝑚𝑒𝑛𝑡) exceeds the Agency agent’scurrent total assetKT(t) by a given proportion k (that is, when∑𝐼𝑉𝑝𝑢𝑛𝑖𝑠ℎ𝑚𝑒𝑛𝑡/𝐾𝑇(𝑡) > 𝑘), the Agency agent goes bankruptfor reasons of insolvency.
Corresponding to the death of a certain number ofAgency agents in any given cycle, new Agency agents willenter the market. In our model, we assume that the entry ofnew Agency agents depends on the average profit level of theentire industry. In each cycle, there are several randomly gen-erated service agencieswhich have the requiredwillingness toenter the market. Also, the higher the average profit level ofthe entire industry, the higher the probability of these serviceagencies entering the market.
3.2.5. Behavioral Decision-MakingDesign of Government AgentIntervention. As the “night watchman” of the market, thegovernment must adopt the role of maintaining market orderand preventing market failure. In the OHS service marketof SMEs, the purchase of OHS services is a spontaneousbehavior on the part of enterprises. The government hasno right to interfere with the decisions of enterprises aboutwhether or not those enterprises purchase OHS services,or which service agency they choose. However, unlike thecase of an ordinary consumer market, in the OHS servicemarket of SMEs, the effect of OHS services presents a severelagging effect, as well as information asymmetry. Combinedwith themultiscale complexity of safety-related accidents, it isvery difficult for SMEs to distinguish rights and liabilities viacommercial contracts, as they could in the case of ordinaryproducts. This combination of factors makes it even moredifficult for SMEs to either distinguish service quality orinvestigate and affix the responsibility for the poor effect
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of OHS services. This creates an opportunity for serviceagencies to provide low-quality services. However, providinglow-quality services not only causes unnecessary losses toenterprises, but also jeopardizes fair competition in theOHS service market. On that account, one governmentalregulatory agency (Government agent) is set in the system,and government intervention behaviors are introduced toexplore the influence of government intervention on thequality of OHS services.
Based on related studies and real-life situations, thispaper significantly simplifies and summarizes governmentintervention behaviors as three types, i.e., (1) governmentpunishment, (2) policy supports, and (3) quality rating.The behavior of government punishment is manifested asfollows: The Government agent inspects the service qual-ity of Agency agents in the system and sets the upperlimit (or standard Sp) of service quality which must bemet to avoid government punishment. That is, when theGovernment agent finds that the quality of OHS servicesprovided by some Agency agents is lower than the standardSp, those Agency agents are punished.The behavior of policysupports ismanifested as follows:TheGovernment agent setsthe lower limit Sr of service quality for policy supports andoffers policy supports to Agency agents in the system whoseservice quality is higher than Sr. The behavior of qualityrating is manifested as follows: The Government agent reg-ularly inspects the service quality of all Agency agents anddetermines quality ratings on theAgency agents on this basis.Factory agents can observe the results of the quality ratingsand thus more clearly understand the service quality ofAgency agents. Through the setting up of related parametersituations, we conducted experiments, respectively, to probethe influences of the different types of intervention behavioron OHS services.
3.3. Experimental Program. In this paper, Netlogo is used tosimulate the action that Government-agent investigates theservice quality of Agency-agent in the system based on themodel constructed above.Three interventions including gov-ernment punishment, policy support, and quality assessmentare introduced to study the effect on OHS services of small-and medium-sized enterprises in Experiment I, ExperimentII, and Experiment III, respectively.
In Experiment I, three sets of comparative experimentsare further set up. To be specific, Experiment 1.1 is designedwith two situations, namely, the situation before introducinggovernment punishment and the situation after introducinggovernment punishment. Then, the two situations are com-pared in terms of the average evolution of the overall qualityof OHS services, as well as the change in the number of SMEspurchasing OHS services. Experiment 1.2 further verifies theinfluence of different punishment standards on OHS servicesafter introducing government punishment. By setting thepunishment standard Sp, the evolutions of the quality of OHSservices under the two situations are compared. Experimental1.3 verifies the influence of different punishment intensitieson OHS services after introducing government punishment.By setting the punishment intensity 𝛽, the evolutions of
the quality of OHS services under the two situations arecompared.
In Experiment II, three sets of comparative experimentsare further set up too. Experiment 2.1 is designed with twosituations, that is, the situation before introducing policysupports and the situation after introducing policy supports.Then, the two situations are compared in terms of the averageevolution of the overall quality of OHS services and thechange in the number of SMEs purchasing OHS services.Experiment 2.2 further verifies the influence of differentsupport standards on OHS services after introducing policysupports. By setting the policy support standard Sr, theevolutions of the quality of OHS services under the twosituations are compared. Experimental 2.3 verifies the influ-ence of different support intensities on OHS services afterintroducing policy supports. By setting the support intensitycoefficient 𝛼, the evolutions of the quality of OHS servicesunder the two situations are compared.
To observe the influence of the OHS services qualityrating strategy introduced by the government, Experiment IIIis designed with two situations, namely, the situation beforeintroducing quality rating and the situation after introducingquality rating. To be specific, in the situation after introduc-ing quality rating, we assume that the Government-agentregularly inspects the service quality of Agency agents andconducts quality ratings of all Agency agents. Factory agentscan observe the results of these quality ratings and thus canobtain the evolution of service quality of Agency agents byexperiments.
4. Experiments and Results
4.1. Experiment I: Influence on the OHS Services of Small-and Medium-Sized Enterprises after Introducing GovernmentPunishment. The first step is to create the experimental sam-ples, that is, 1000 Factory agents, 20 Agency agents, and oneGovernment agent.TheAgency agents have an initial servicelevel of S0 (wherein 𝑆0 ∈ (0, 1)). Government agent inspectsthe service quality of Agency agents, and Sp is the upperlimit of service quality adopted by the Government agent forcarrying out punishment, or, in other words, the punishmentstandard. When it is found that the quality of OHS servicesprovided by some Agency agents is lower than the standardSp, the low-service quality Agency agents are punished. Also,𝛽 represents the punishment intensity coefficient. That is,the higher the coefficient, the higher the penalty amount.Setting Income Agency agent as the current revenue of theAgency agent, the punishment value IV punishment can beexpressed as 𝐼𝑉𝑝𝑢𝑛𝑖𝑠ℎ𝑚𝑒𝑛𝑡 = 𝛽 ⋅ 𝐼𝑛𝑐𝑜𝑚𝑒𝐴𝑔𝑒𝑛𝑐𝑦.
4.2. Experiment 1.1: Evolution of OHS Service Quality in Boththe Situation with Government Punishment and the Situa-tion without Government Punishment, and the Proportion ofEnterprises Purchasing OHS Services. In the situation beforeintroducing government punishment, Government agent-related activities are excluded. In the situation after intro-ducing government punishment, Government agent-relatedactivities are added. The punishment intensity is set at the
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0.00%10.00%20.00%30.00%40.00%50.00%60.00%70.00%80.00%90.00%
100.00%
0 100 200 300 400 500 600 700 800 900 Time
Proportion of Factory_agents that purchase OHS service
Figure 2: Situation before introducing government punishment.
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Average quality of OHS services
0.00%10.00%20.00%30.00%40.00%50.00%60.00%70.00%80.00%90.00%
100.00%
0 100 200 300 400 500 600 700 800 900 Time
Proportion of Factory_agents that purchase OHS service
Figure 3: Situation after introducing government punishment.
same time at 𝛽=0.8, and the upper limit of the quality of OHSservices for government punishment, or the punishmentstandard, is set at Sp=0.6. See the experimental results inFigures 2 and 3.
The results of our experiment indicate that, before intro-ducing government punishment, the average quality of OHS
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Time
Average quality of OHS services
Sp=0.3Sp=0.6Sp=0.8
Figure 4: Evolution ofOHS service quality in the situation of settingthe punishment standard at 0.3, 0.6, and 0.8, respectively.
services stabilizes at approximately 0.2, which is a relativelylow level. In this case, the number of SMEs purchasing OHSservices accounts for approximately 10% of the total number(a relatively small percentage). However, after introducinggovernment punishment, the average quality of OHS servicesstabilizes at approximately 0.6-0.7, which is a higher levelthan before. In this case, the number of SMEs purchasingOHS services continuously rises and ultimately stabilizes atapproximately 65% (an obviously and significantly increasedpercentage). Clearly, after introducing government punish-ment measures, the quality of OHS services has seen anapparent improvement and has also driven the rise of thenumber of SMEs purchasing OHS services. In other words,the improvement of the quality of OHS services can drive thegrowth of market demand for OHS services and promote thebenign development of OHS services.
4.3. Experiment 1.2: Evolution of OHS Service Quality undera Certain Punishment Intensity but with Different Punish-ment Standards. On the basis of adding Government agent-related activities, the punishment intensity is set constantlyat 𝛽=0.8. The upper limits of the quality of OHS services forgovernment punishment (or the punishment standard) areset at Sp=0.3, 0.6, and 0.8, respectively. See the experimentalresults in Figure 4.
The results of this experiment indicate that when thepunishment standard is set at 0.3, the average quality ofOHS services stabilizes at approximately 0.3-0.4. When thepunishment standard is set at 0.6, the average quality ofOHS services stabilizes at approximately 0.6-0.7. When thepunishment standard is set at 0.8, the average quality of OHSservices slowly rises up to approximately 0.5-0.6. Clearly,it is preferable to have the punishment standard set at aproper level, i.e., neither too low nor too high. When thepunishment standard Sp set by the Government agent istoo high, it becomes difficult for most Agency agents tosignificantly improve their service quality within a shortperiod of time. In other words, when the government makes
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Avg.S
Time
Average quality of OHS services
=0.1=0.8=1.5
Figure 5: Evolution of OHS service quality in the situation of settingthe punishment intensity coefficients at 0.1, 0.8, and 1.5.
decisions regarding a punishment strategy, the punishmentstandard established should conform to the present situationpertaining to the strength of most service agencies. This willtruly and effectively stimulate service agencies to improve thequality of their OHS services and to thus provide better OHSservices for SMEs.
4.4. Experiment 1.3: Evolution of OHS Service Quality undera Certain Punishment Standard but with Different Punish-ment Intensities. On the basis of adding Government agent-related activities, the government punishment standard is setconstantly at Sp=0.6. The punishment intensity coefficientsare set at 𝛽=0.1, 0.8, 1.5, respectively. See the experimentalresults in Figure 5.
The results of this experiment indicate that when thepunishment intensity is set at 0.1, the average quality ofOHS services stabilizes at approximately 0.2. When thepunishment intensity is set at 0.8, the average quality ofOHS services stabilizes at approximately 0.6-0.7. When thepunishment intensity is set at 1.5, the average quality ofOHS services also stabilizes at approximately 0.6-0.7. Clearly,when the punishment intensity is set at 0.8, the level ofOHS services quality is improved. In contrast, when thepunishment intensity is set at the lower level (𝛽=0.1), thequality of OHS services continuously trends downward, andgovernment regulation fails to exert a positive influence.When the level of punishment intensity is much higher(𝛽=1.5), the quality of OHS services is similar to that set at0.8; however, a plenty of service agencies are found to die forexcessively high pressure, with the experiment in progress. Itis thus obvious that, when adopting a punishment strategy,the punishment intensity should be set at a proper level, so asto effectively promote the improvement of the quality of OHSservices.
4.5. Experiment II: Influence on the OHS Services of Small-and Medium-Sized Enterprises after Introducing Policy Sup-ports. The first step is to create the experimental samples,
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0.00%10.00%20.00%30.00%40.00%50.00%60.00%70.00%80.00%90.00%
100.00%
0 100 200 300 400 500 600 700 800 900 Time
Proportion of Factory_agents that purchase OHS service
Figure 6: Situation before introducing policy supports.
that is, 1000 Factory agents, 20 Agency agents, and oneGovernment agent. The Agency agents have an initial ser-vice level of S0 (wherein 𝑆0 ∈ (0, 1)). We assume thatGovernment agent offers supports to Agency agents whoare active in the market and whose service quality is aboveSr, wherein Sr represents the lower limit of service qualityadopted by the Government agent for carrying out support.The support value IVsupport is expressed as 𝐼𝑉sup𝑝𝑜𝑟𝑡 = 𝛼 ⋅ (𝑠𝑡 −𝑠𝑟) ⋅ 𝐼𝑛𝑐𝑜𝑚𝑒/𝑚, wherein 𝛼 represents the support intensitycoefficient, “Income” represents the total revenue of all serviceagencies, and “Income/m” represents the average revenue.That is, the higher the quality St of the OHS services providedby service agencies, the higher the value of policy supportsthose service agencies will receive.
4.6. Experiment 2.1: Evolution ofOHS ServiceQuality in Both aSituation with Policy Supports and a Situation without PolicySupports, and the Proportion of Enterprises Purchasing OHSServices. In the situation before introducing policy supports,Government agent-related activities are excluded. In the sit-uation after introducing policy supports, Government agent-related activities are added, and the support intensity coeffi-cient is set at 𝛼=1.5. The lower limit of the quality of OHSservices for policy supports from the government (or thesupport standard) is set at Sr=0.3. See the experimental resultsin Figures 6 and 7.
The results of this experiment indicate that, before intro-ducing policy supports, the average quality of OHS servicesstabilizes at approximately 0.2 (a relatively low level), inwhichcase the number of SMEs purchasing OHS services accountsfor approximately 10% of the total number (a relatively smallpercentage). However, after introducing policy supports, the
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100.00%
0 100 200 300 400 500 600 700 800 900 Time
Proportion of Factory_agents that purchase OHS service
Figure 7: Situation after introducing policy supports.
average quality of OHS services stabilizes at approximately0.8-0.9 (a relatively high level on the whole), in which case thenumber of SMEs purchasing OHS services is relatively largeand stabilizes at approximately 89%.Clearly, after introducingpolicy supportmeasures, the quality of OHS services has seenan apparent and significant improvement. This once againproves that the improvement of the quality of OHS servicescan effectively drive the growth of the market demand forOHS services and promote the positive development of OHSservices.
4.7. Experiment 2.2: Evolution of OHS Service Quality undera Certain Support Intensity but with Different Support Stan-dards. On the basis of adding Government agent-relatedactivities, the support intensity coefficient is set constantly at𝛼=1.5. The policy support standards of the government areset at Sr=0.3, 0.6, and 0.8, respectively. See the experimentalresults in Figure 8.
The results of this experiment indicate that when thesupport standard is set at 0.3, the average quality of OHSservices rises up to approximately 0.8-0.9. When the supportstandard is set at 0.6, the average quality of OHS servicesstabilizes at approximately 0.6. When the support standard isset at 0.8, the average quality ofOHS services is approximately0.2-0.3. Clearly, when the support standard is set at too high alevel (Sr=0.8), the higher standard fails to exert any influenceon the quality of OHS services. This is because, when thestandard becomes too high, it becomes difficult for mostservice agencies to live up to the criteria. In this case, thesupport value IVsupport fails to exert the desired positive effecton service agencies. In other words, the support policies
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Average quality of OHS services
Sr=0.3Sr=0.6Sr=0.8
Figure 8: Evolution of OHS service quality in the situation of settingthe support standards at 0.3, 0.6, and 0.8, respectively.
become ineffective. These results are also consistent withobservations of real-life situations, which suggests that anexcessively high support standard does not exert any positiveeffect on the improvement of the quality of OHS services.When the support standard is set at a relatively low level(Sr=0.3, 0.6), the quality of OHS services provided by serviceagencies (influenced by the function of the support valueIVsupport) becomes directly proportional to the support value.In other words, the lower the support standard is, the strongerthe stimulationwill be to improve the quality ofOHS services.
4.8. Experiment 2.3: Evolution of OHS Service Quality undera Certain Support Standard but with Different Support Coef-ficients. On the basis of adding Government agent-relatedactivities, the support standard is set constantly at Sr=0.6.The support intensity coefficients are set at 𝛼=0.5, 1.5, 3,respectively. See the experimental results in Figure 9.
The results of this experiment indicate that when thesupport intensity is set at 0.5, the average quality of OHSservices stabilizes at approximately 0.3-0.4.When the supportintensity is increased to 1.5, the average quality of OHSservices increases as well and stabilizes at approximately 0.6.When the support intensity is increased to 3, the average qual-ity of OHS services is continuously increased and stabilizesat approximately 0.8. As clearly shown by these experimentalresults, when the support intensity becomes too low, thequality of OHS services fails to achieve the expected effect,in which case policy supports become ineffective. Only whenthe support intensity has reached a certain level can policysupports be expected to exert their influence. In addition,the higher the support intensity, the stronger the stimulationeffect on the improvement of the quality of OHS services.This is because excessively low support intensity results in anexcessively low value of policy supports received by serviceagencies. The profit thereby generated is insufficient to offsetthe high cost of improving service quality. To put it in anotherway, the higher the support intensity, the higher the value ofpolicy supports received by service agencies for improving
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Time
Average quality of OHS services
=0.5=1.5=3
Figure 9: Evolution of OHS service quality in the situation of settingthe support intensity at 0.5, 1.5, and 3.
the quality of their OHS services. When the support intensityreaches a certain level, the motivation of service agenciesto improve the quality of their OHS services is significantlyenhanced, thus promoting the all-round improvement of thequality of OHS services. These results are consistent withobservations of real-life situations.
4.9. Experiment III: Influence on the OHS Services of Small-and Medium-Sized Enterprises after Introducing Quality Rat-ings. The first step is to create the experimental samples,that is, 1000 Factory agents, 20 Agency agents, and oneGovernment agent.TheAgency agents have an initial servicelevel of S0 (wherein 𝑆0 ∈ (0, 1)). To observe the influ-ence of the OHS services quality rating strategy introducedby the government, this experiment is designed with twosituations, namely, the situation before introducing qualityrating and the situation after introducing quality rating. To bespecific, in the situation after introducing quality rating, weassume that Government agent regularly inspects the servicequality of Agency agents and conducts quality ratings of allAgency agents. Those whose service quality falls within theintervals of [0, 0.4], [0.4, 0.7], or [0.7, 1] are, respectively,rated at grade C, grade B, or grade A. Factory agents canobserve the results of these quality ratings and thus moreclearly understand the service quality of Agency agents. Seethe experimental results in Figures 10 and 11.
The results of our experiment indicate that, before intro-ducing quality ratings, the average quality of OHS servicesstabilizes at approximately 0.2 (a relatively low level). In thiscase, the number of SMEs purchasing OHS services accountsfor approximately 10% of the total number (a relatively smallpercentage). However, after introducing quality ratings, theaverage quality of OHS services stabilizes at approximately0.8 (a relatively high level on the whole). In this case, thenumber of SMEs purchasing OHS services is obviously andsignificantly increased and stabilizes at approximately 80%.Clearly, when Factory agents can observe the service qualityof Agency agents, they will actively seek cooperation with
0.00.10.20.30.40.50.60.70.80.91.0
0 100 200 300 400 500 600 700 800 900
Avg.S
Time
Average quality of OHS services
0.00%10.00%20.00%30.00%40.00%50.00%60.00%70.00%80.00%90.00%
100.00%
1 101 201 301 401 501 601 701 801 901 Time
Proportion of Factory_agents that purchase OHS service
Figure 10: Situation before introducing quality rating.
0.00.10.20.30.40.50.60.70.80.91.0
0 100 200 300 400 500 600 700 800 900
Avg.S
Time
Average quality of OHS services
0.00%10.00%20.00%30.00%40.00%50.00%60.00%70.00%80.00%90.00%
100.00%
0 100 200 300 400 500 600 700 800 900 Time
Proportion of Factory_agents that purchase OHS service
Figure 11: Situation after introducing quality rating.
those who provide high-quality services. This in turn clearlyshows that SMEs have a demand for high-quality OHS ser-vices. Introducing the quality rating strategy can effectivelystimulate more SMEs to purchase high-quality OHS servicesand further promote the high-quality development of OHSservices. This finding further proves that there is a mutually
BioMed Research International 13
stimulating and interactive benign relationship between thequality of OHS services and the market demand for OHSservices and that a positive developmental trend is herebyformed.
5. Discussions and Conclusions
This paper focuses on the intervention measures taken bythe government in the development of the OHS market anddemonstrates which intervention measures can achieve thereal effective operating of the OHS service market. The effec-tive operating of OHS service market means that enterprisesare willing to buy services, and the services they bought areof high quality. The results of evolutionary experiments showthat different interventions lead to different implementationeffects and have different implementation conditions. Eachintervention has certain constraints, advantages, and disad-vantages. This means that the government should chooseinterventionmeasures according to actual circumstances. Forexample,
(1) Considering the effect of punishment strategy, thegovernment should set appropriate punishment standard andintensity. It is difficult to stimulate the service agencies toimprove service quality under excessive or too low punish-ment standard, or too low punishment intensity. Similarly,when the punishment intensity becomes too high, it is helpfulto enhance the quality of service, but a large number of serviceagencies will die, which will be nonconducive to marketdevelopment.
(2) Appropriate policy support standard and intensityof government are beneficial. Excessive support standardled to the fact that most service agencies are difficult tomeet the requirements. In such cases, policy supports cannottruly exert any significant positive influence. Meanwhile, itis also not desirable to set the support intensity at too lowa level, as the profit generated in this case is insufficient tooffset the high cost of improving service quality. In otherwords, the expected effect of stimulating the improvement ofservice quality fails to be achieved.Therefore, according to thecurrent level of market service quality, government shouldreduce support standards to improve support, so that serviceagencies are effectively supported by policy support, whichpromotes the service agencies to enhance the quality of OHSservice.
(3) When the government introduces quality ratingsof OHS services and discloses the results of such ratings,enterprises can actively seek cooperation with those whoprovide high-quality services by observing their service qual-ity rating. This measure could also stimulate the significantimprovement of the quality of OHS services and drive servicemarket development.
Besides that, by comparison of the different governmentalinterventions, all the government interventions can effec-tively improve the quality of service under certain circum-stances, simultaneously, increase the number of enterpriseswhich buy OHS services, and ensure the good developmentof the market. However, each intervention has its advantagesand disadvantages. From the perspective of the operation of
the OHS service market, policy support strategy is the mosteffective way. It can motivate to a great extent the serviceagencies to improve their service quality and ensure theproper development of the market as long as the governmentreduces the support standard and increase the supportintensity. In comparison to policy support strategy, the pun-ishment strategy calls for extra attention to the standard andthe intensity. The strategy would be ineffective if the standardand the intensity are set too low; however, when the standardand the intensity are set too high, the service agencies wouldhave difficulty in greatly improving their services in a shortperiod and would have to withdraw from the market at last,which goes against the prospect of sound operation of theOHS service market. From the prospective of the practicalityof policy implementation, policy support strategy demandsa great amount of fund from the government, which israther difficult in reality. In contrast, punishment strategy andquality ratings strategy only incur costs in quality verification,and this is rather more feasible. By comparison, it can beseen that policy support strategy is the most effective wayto promote OHS service development, however, which is atthe cost of high regulatory expenses. Punishment strategyis similarly effective, but it will bring too much pressure tothe OHS service agencies. Quality ratings strategy is not onlyeffective to the OHS service agencies, but also economic tothe government.
Our study, however, is still fallible to limitations. First ofall, we have set a general situation to achieve the simulation.On the basis of reality, we assume that the cost of SMEs’ self-built OHS system is higher than that of purchasing OHS ser-vices; SMEs are unable to tell the quality ofOHS services fromthe price; and quality OHS services are bound to improve thecorporates’ OHS quality. Therefore, the results of this studyare valid in general situations, and the results may deviatein special situations when the postulated conditions change.Secondly, our study only takes governmental interventionstrategies into consideration and does not introduce otherinterested agents into the playing field, which, though itgoes beyond this study, is very important for future studies.Thirdly, in regard to the setting of quality rating strategy, thisessay only verifies whether the strategy yields quality ratingeffects. In the future, the author will delve into the way ofinformation disclosure of different rating systems.
Despite all these limitations, this research develops arigid experimental framework dedicated to analyzing theinfluence of different intervention strategies on SMEs’ OHSservice quality. It also provides an evolution experiment, withthree specific strategies applicable to various situations, toexamine the influence of different governmental interventionstrategies on SMEs’ OHS service quality. Every strategyexperiment consists of one or several control experiments.The results of the experiments can tell the positive influencethat government interventions exert on OHS service qualityand how to control the standard and the intensity of variousstrategies to achieve the optimum results.
In consideration of the experiment results, this paperargues that, in order to ensure the operation of OHS ser-vice market so as to help SMEs build the OHSAS18001system efficiently, the government should take intervention
14 BioMed Research International
strategies to control OHS service quality and the strategiesmay be reasonable and moderate, as the Chinese SMEs’OHS management is lagging far behind the OHSAS18001standards.Meanwhile, the intervention should fit theChinesenational conditions and the current situation of the servicemarket, for instance, in the initial stage of the market, ifthe government has a good finance, is willing to invest,and adopts incentive methods that will activate the energyof the market. When the market researches a certain scale,the government can adopt the strategies of punishment andquality rate information exposure, strengthen the servicequality supervision of service organizations, encourage ser-vice organizations to improve service quality, standardizebehavior of the market, and guide good operation of themarket. Therefore, considering environment of the marketand the supervision cost, the government can choose themost appropriate intervention, prevent the failure of OHSservice market, stimulate development of the market, andfinally maintain the OHS level of SMEs.
Data Availability
The data used to support the findings of this study areavailable from the corresponding author upon request.
Conflicts of Interest
The authors declare that there are no conflicts of interestregarding the publication of this paper.
Acknowledgments
This work was supported by the National Natural ScienceFoundation of China under Grants 71373104 and 71403108.The authors would also like to appreciate Dr. Guoqing Zhang,University of Windsor, Canada, for providing the valuablesuggestions of this paper.
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