Developing a structural equation model for the life cycle of generic pharmaceutical products

Abstract

Objective

Product life cycle (PLC) refers to the time ranging from when a product is introduced into the market until it is taken off the shelves. Product life cycle management can guarantee product survival and prevent it from declining. The purpose of this study is to explore factors influencing the life cycle of generic pharmaceutical product and their effect sizes to shed light on better approaches for the life cycle management of such products.

Method

To this end, a standard questionnaire containing six dimensions and 47 items was used to collect data from 302 industrial pharmacists. The research hypotheses were tested using structural equation modeling to quantify the relationship between each dimension and its associated factors with the PLC.

Key finding

The results indicated that government interventions (Standard estimate (SE) = 0.63) and technology advancement (SE = 0.60) as environmental factors, quality (SE = 0.60) and price (SE = 0.62) satisfaction, patient adherence (SE = 0.68), physicians' awareness (SE = 0.64), market saturation (SE = 0.60); and demand rate (SE = 0.62) as demand factors, brand power (SE = 0.92); and availability of imported competitors (SE = 0.59) as competition factors, drug safety (SE = 0.63), product appearance (SE = 0.57); and ease of administration (SE = 0.61) as product-related factors, manufacturer reputation (SE = 0.63); and production continuity (SE = 0.64) as producer-related factors, and distributors number (SE = 0.54); and on-time delivery and availability of products (SE = 0.69) as distributor-related factors at 5% significance levels had the most effect on generic pharmaceutical PLC in Iran.

Conclusion

For the first time, this study offers a structural equation modeling framework for better managing the PLC of generic pharmaceutical products to help the company’s performance and survival.

Keywords

product life cycle pharmaceuticals structural equation modeling product lifecycle management factor analysis

Introduction

Product life cycle (PLC) refers to the time span ranging from the occasion a product is introduced into the market until when it is taken off the shelves. It consists of four stages—namely, introduction, growth, maturity, and decline. Based on this definition, each PLC stage entails a different amount of product sales and has a unique set of features. Critical to PLC management, therefore, is adopting a proper strategy sequence. In general, product lifecycle management (PLM) is a strategic process for handling a product as it moves through the typical stages of its life, from the introduction to market exit.¹ PLM requires to be dynamic given the fast pace changes in the market, technology, rules and regulations, and products/suggestions offered by the competitors. As a result, PLM strategy should be constantly evaluated in light of the new opportunities.^2,3

PLM became a mature concept in the 1990s and, ever since, has had a profound impact in various industries.⁴ PLM improves processes for developing firm products and enables authorities to utilize product-related information for better decision making in their business.⁵ In the pharmaceutical industry, PLM is mainly used to promote patient acceptance, have revenue growth, improve clinical benefits, extend product life, and enter the market earlier. For effective and useful PLM implementation, various features should be taken into consideration. These include early start, clear leadership in strategic planning, support for knowledge and skills, and readiness for changes in governmental and organizational rules.⁴ In recent years, the pharmaceutical industry has experienced efficiency decline in research and development, speedy shift in health care perspectives, and intense competition in generic products, all of which have yielded decreased growth and profit margins. Product development in the pharmaceutical sector has primarily focused on the management and results of clinical trials. Nonetheless, this industry is in need of more comprehensive approaches to introduce new products into the market and eliminate defective ones, a process that can lower operating costs and precipitate manufacturing of new products.⁶

For years, while PLM was used to be implemented only in automotive and electronics industries, it is nowadays obvious that pharmaceutical companies should also embrace PLM. In fact, research shows that firms that adopt a comprehensive PLM and design detailed plans to reach their goals are more likely to succeed in the economic domain (3). For example, the short life cycle in the pharmaceutical industry is caused by various factors such as fierce competition, lack of dynamism in the supply chain of the pharmaceutical sector, increasing oversight of health products, rising development costs, falling drug prices, and the need for the latest drug manufacturing technologies.^4,7 These problems can be minimized through proper management in the scientific, technical, supervisory, and marketing sectors, hence boosting the value and life cycle of pharmaceutical products.⁴ Overall, efficient PLM begins by identifying influential factors.⁸ Numerous factors can influence PLC in the course of time.⁹ In the current study, attempts were made to pinpoint factors affecting the life cycle of pharmaceutical products and explore their effect size in the hopes of achieving better PLM in pharmaceutical products.

Iranian pharmaceutical industry

Compared to developing countries, the Iranian pharmaceutical market has grown significantly and is expanding rapidly.¹⁰ Iran had approximately 40 pharmaceutical plants in 1979, most of which were subsidiaries of international companies producing 30% of the Iranian pharmaceutical market under license. After the Islamic Revolution, international companies left the country, and medicine manufacturing continued in the form of generic production by domestic firms, which can improve access to medicines and the production of cost-effective medicines.¹¹ The domestic industry can produce 95% of medicines in Iran.¹² In recent years, Iran’s pharmaceutical industry has moved toward trademark systems, which has increased the intensity of competition in this industry and provided good opportunities for its future development. In fact, the domestic pharmaceutical industry has experienced two digit growth in recent years and succeeded in manufacturing biological products with advanced technology as an emerging knowledge.¹² Nevertheless, the industry suffers from weak management and investment in various sectors like research and development, marketing, equipment, and technology.¹³ It is hoped that the results of this study shed light on factors influencing the PLC of pharmaceutical products, hence offering a deeper understanding to managers.

Literature review and hypothesis development

The primary studies on drugs’ life cycle dates 1967, when Cox et al. explained the theoretical background of PLC in relation to pharmaceutical products and, upon examining the sales of 258 prescription pharmaceutical products over a 10-year period, presented six general patterns about the drugs used in that time.¹⁴ In studies conducted prior to 2000, researchers typically concentrated on product sales as the single factor affecting PLC. Examining differences in the PLC of old and new cardiovascular drugs, Bauer et al. (2000) concluded that more peak sales for early entrant compared to drugs entering the market later were recorded. In other words, products that enter the market earlier experience longer sales growth.¹⁵ Fischer et al. (2010) also demonstrated that quality and order of entry affect the level of peak sales and the time-to-peak sales of pharmaceutical brands in PLC curves. In Fisher’s study, researchers used a multidimensional scale to measure product quality capturing the number of approved indications, number of interactions, frequency of side effects, frequency of medication, protein absorption, plasma half-lifetime, and bioavailability. The findings showed that all these dimensions can influence the sales of pharmaceutical products. The study further shed light on some other factors influencing PLC of pharmaceutical products, such as the number of competitors, market entry strategy, marketing expenditures, and the drug price.¹⁶ Li et al. (2011) developed a PLC model while considering competition-related factors, with the results indicating that factors like competition and the costs influence PLC.¹⁷ Afshar Kazemi et al. (2011) utilized the system dynamics approach to simulate PLC, which was found to be a function of quality, price, product attractiveness, and customer satisfaction.¹⁸ Using the same approach, Safri et al. (2011) proposed that PLC is influenced by uncertainty in customer demand, product innovation, and organizational research and development.¹⁹ Guo et al. (2014) developed a bass-type model using aggregate market data. They eventually presented a mathematical formula encompassing factors like price and marketing (which influence customers’ behavior) to predict a particular product’s sales in the course of time as it enters the market.²⁰ Gmelin et al. (2014) believed that different PLC stages are influenced by various factors such as organizational mission, technology, organizational structure, type of organizational ownership, market situation, and the prevailing culture.²¹ Abdollahiasl et al. (2014) constructed a comprehensive model simulating the impact of multiple variables on Iran’s national drug policy indicators. They concluded that the sales of pharmaceutical products depends on various factors including availability of domestic products, availability of imported products, power of domestic brands, power of imported brands, domestic consumption, intensity of domestic competition, domestic demand, price of domestic products, price of imported products, market saturation, consumers’ information, and packing quality.²² In their study, Fardazar et al. (2019) displayed that policies pursued by the government and Food and Drug Administration of Iran play a critical role in the country’s pharmaceutical intervention. Two of the major policies are supporting domestic industry through prohibition of foreign drug imports or increase of customs tariffs and supporting the research and development departments in the pharmaceutical industry. These policies were more seriously followed as a result of intensification of sanctions against Iran, the national currency depreciation, and lack of cooperation of foreign countries in supplying pharmaceutical raw materials and drugs required by Iran.²³ Mousavi et al. (2022) developed a system dynamic model for generic pharmaceutical products. This study has shown that three major subsystems, including demand, provider, and competitor subsystems, have a crucial role in forming PLC in generic drugs.³

In the light of previous studies, the following conceptual model was developed to capture the life cycle of pharmaceutical products in six subcontracts (Figure 1). Each of the subcontracts encompass a number of factors, which will be described in detail in the Method section. Based on prior findings, the following hypotheses are proposed:

Figure 1.

Conceptual model and developed hypotheses of the study.

Hypothesis 1: There is a significant relationship between product-related factors and PLC.

Hypothesis 2: There is a significant relationship between manufacturer-related factors and PLC.

Hypothesis 3: There is a significant relationship between competition-related factors and PLC.

Hypothesis 4: There is a significant relationship between demand-related factors and PLC.

Hypothesis 5: There is a significant relationship between distributor-related factors and PLC.

Hypothesis 6: There is a significant relationship between environmental factors and PLC.

Structural equation modeling

In the present study, the data were collected through a questionnaire and proposed hypothesis analyzed via structural equation modeling (SEM), which has recently gained popularity in behavioral and social sciences for multivariate data processing.²⁴ SEM is a multivariate statistical framework adopted for modeling complex associations between observed and latent variables,²⁵ and it has many advantages over other statistical methods such as regression.²⁶ It is an overarching framework encompassing regression, path analysis, factor analysis, and latent growth curve modeling. SEM is used to estimate the system of linear equations with the ultimate goal of testing the model hypotheses. The first step in running SEM is conceptualizing a hypothetical model or a path diagram based on the existing knowledge and theories. In the path diagram, the rectangles commonly represent observed variables (x, y), which are directly measured, while circles/ovals indicate latent constructs (a, b), which are defined based on the measured variables. One-way arrows demonstrate causal paths, whereby one variable directly influences another. Conversely, two-way arrows display correlations between the variables. Figure 2 illustrates the structural model developed for this study. Accordingly, γ and λ are the factor loadings, whereas ε and δ indicate the error rates.²⁵ In SEM, factor loadings show the strength of the relationship between two variables.²⁷

Figure 2.

The general structure of structural equation modeling.

Method

Data collection

To glean the required data, a standardized questionnaire was distributed online among pharmacists. To design the questionnaire, existing studies were initially scrutinized, a process culminating in the development of a set of factors/indictors potentially affecting the life cycle of pharmaceutical products. In order to localize the indicators within each factor, they were subsequently submitted to 10 domain experts in the pharmaceutical industry, who also served as academicians. Data saturation and unanimous agreement among the experts (with regard to the variables impacting the life cycle of pharmaceutical products) were used as the criteria for selecting the participants in this phase of the study. Based on the feedback received from the experts, some of the indicators were removed or replaced. Upon identifying the factors affecting PLC (the main structure), questionnaire items were developed and submitted to 8 pharmaceutical experts, who were invited to pinpoint possible ambiguities and judge whether the questionnaire was comprehensive. Some of the items were revised in light of the ideas gleaned from these experts. The finalized questionnaire entailed six subcontracts (i.e., manufacturer, distributer, product, demand, competition, and environment) and 47 items (Appendix 1). It was then administered among pharmacists who were active in the pharmaceutical. The questionnaire was sent to 500 pharmacists in total and was properly completed by 302 participants.

Reliability and validity assessment

Assessing questionnaire reliability: Cronbach’s alpha is usually used to gauge questionnaire reliability, with values greater than 0.7 deemed acceptable.²⁸ In a similar vein, upon feeding the data into SPSS 26, we carried out Cronbach’s alpha to measure the reliability of the responses. Table 1 contains information about the reliability indices of the questionnaire subconstructs. Given that all the observed values are greater than 0.7, the reliability of all the subconstructs is attested.

Table 1.

Calculated Cronbach’s alpha for each subconstruct of the product life cycle.

Subcontract	Indicators	Cronbach’s alpha
Competition factors	8	0.74
Product factors	10	0.81
Producer factors	6	0.74
Distributor factors	6	0.71
Consumption factors	9	0.80
Environmental factors	8	0.81
PLC	47	0.92

Assessing content validity: Content validity refers to how well the developed items can represent the entire domain of the variable being measured. This subjective judgment is accomplished through detailed analysis of previous studies for identifying the influential variables and having their suitability confirmed by domain experts.²⁸ As mentioned earlier, content validity of the developed questionnaire was measured through consulting domain experts and academicians.

Assessing construct validity: Convergent and divergent types of validity are tested through running confirmatory factor analysis and identifying the factor loadings of items within each subconstruct.²⁸ Table 2 displays the results of confirmatory factor analysis. Two of the major model fit indices are Kaiser–Meyer–Olkin (KMO) and factor loading, whose values should be greater than 0.6 and 0.3, respectively, (27). As indicated in Table 2, the results of Bartlett’s test shows that the obtained KMO value was 0.858 (p < 0.05), hence adequacy of the sampling. Moreover, all factor loadings were above 0.3, indicating the model fitness.

Table 2.

Confirmatory factor analysis results for each subconstruct of the product life cycle.

		Components
Subcontract	Indicators	Product	Environment	Demand	Producer	Distributor	Competition
Product factors	Q11	0.53
	Q12	0.57
	Q13	0.65
	Q14	0.63
	Q15	0.46
	Q16	0.40
	Q17	0.52
	Q19	0.52
	Q21	0.59
	Q23	0.61
	% cumulative of Variance	23.20%
Environmental factors	Q46		0.47
	Q47		0.56
	Q48		0.60
	Q49		0.61
	Q50		0.46
	Q51		0.42
	Q52		0.47
	Q53		0.57
	% cumulative of Variance		30.93%
Demand factors	Q10			0.49
	Q38			0.55
	Q39			0.43
	Q40			0.40
	Q41			0.46
	Q42			0.49
	Q43			0.60
	Q44			0.63
	Q45			0.42
	% cumulative of Variance			34.74%
Producer factors	Q27				0.46
	Q28				0.57
	Q29				0.66
	Q30				0.56
	Q31				0.53
	Q32				0.55
	% cumulative of Variance				37.79%
Distributor factors	Q34					0.61
	Q35					0.67
	Q36					0.58
	Q37					0.53
	Q25					0.41
	Q33					0.41
	% cumulative of Variance					40.78%
Competition factors	Q1						0.45
	Q2						0.50
	Q4						0.50
	Q5						0.53
	Q6						0.46
	Q7						0.45
	Q8						0.50
	Q9						0.55
	% cumulative of Variance						43.57%

Kaiser–Meyer–Olkin Measure of Sampling Adequacy 0.86.

Approx. Chi-Square 6341.57.

Df 1378.

Sig 0.000.

Table 2 only includes factor loadings greater than 0.3. As observed, the factor loadings for all relevant subconstructs are greater than 0.4, which confirms convergent validity. On the other hand, such values are smaller than 0.4 in the irrelevant subconstructs, verifying divergent validity. It is thus argued that the developed questionnaire enjoys acceptable convergent and divergent validity. Additionally, questionnaire items accounted for 43.571% of the variance. Williams et al. argue that, in most of the humanities studies, questionnaire factors explain around 50% of the variance. Even in some studies, the accounted variance is reported to be smaller than 50%.²⁹ Furthermore, in a meta-analysis, Peterson contended that a questionnaire is not valid enough if less than 30–40% of cumulative variance is accounted for by the factors emerging from a questionnaire.³⁰ In the present research, over 40% of the cumulative variance is explained by the questionnaire factors, which appears to be acceptable in humanities.

Analysis

The structural equation model of PLC was then performed by SPSS Amos 26.

Results

Descriptive statistics

Table 3 contains Demographic data for the respondents.

Table 3.

Demographic data for the respondents (sample size 302).

Sex
Man	123	40.7%
Woman	197	59.3%
Age (years)
Less than 30	64	21.2
30–40	5978	19.5
40–50	781	25.8
More than 50	101	33.4
Education
Pharm.D.	293	97%
Ph.D.	9	3%
Job title
Authorized Person of Pharmaceutical in industry	103	34.1%
Authorized Person of Pharmaceutical in pharmacy (urban and hospital pharmacists)	299	65.9%
Work experience (years)
5–10	93	30.8%
10–15	34	11.3%
15–20	51	16.9%
20–25	53	17.5%
More than 25	71	23.5%
Sum	302	100%

SEM results

SPSS Amos was exploited to run SEM to test the hypotheses, with the results illustrated in Figure 3.

Figure 3.

Relationships between different product life cycle subconstructs with PLC in the structural equation model (The numbers on the arrows indicate the standardized regression weight, and the numbers in parentheses indicate the p-value).

Model fit indices are as follows: GFI = 0.840, RMSEA = 0.042, and CFI = 0.880. The recorded RMSEA is lower than 0.05, indicating the model fitness. The values obtained for GFI and CFI are smaller than the recommended cut-off score (i.e., 0.9); nonetheless, since RMSEA is bigger than 0.08, values higher than 0.8 are acceptable for GFI and CFI.³¹ The emerged model indicates that environmental factors had the biggest impact on PLC (recording a standardized regression weight of 0.92), while distributor-related factors exercised the smallest influence (with a standardized regression weight of 0.54). Based on Figure 3, all the research hypotheses are approved, demonstrating that PLC is significantly associated with competition, demand, manufacturer, distributor, product, and environment. Each of the subconstruct consists of some indicators whose relationships and effect sizes are displayed in Table 4.

Table 4.

Indicators effects related to the subconstruct of PLC based on structural equation modeling results.

Indicators	Standard estimate	S.E.	P
Competition
q1	0.59	—	—
q2	0.53	0.15	***
q6	0.47	0.12	***
q4	0.53	0.14	***
q5	0.48	0.11	***
q7	0.19	0.20	0.06
q9	0.92	0.13	***
q8	0.13	0.15	0.03
Demand
q10	0.60	—	—
q39	0.62	0.14	***
q40	0.64	0.15	***
q41	0.58	0.14	***
q42	0.68	0.16	***
q43	0.60	0.12	***
q44	0.62	0.14	***
q45	0.44	0.17	***
q38	0.24	0.16	***
Product
q11	0.46	—	—
q12	0.63	0.22	***
q13	0.57	0.20	***
q14	0.35	0.29	***
q15	0.59	0.23	***
q17	0.61	0.24	***
q19	0.56	0.17	***
q21	0.52	0.28	***
q23	0.47	0.18	***
q16	0.43	0.20	***
Distributor
q25	0.55	—	—
q33	0.44	0.15	***
q34	0.59	0.18	***
q35	0.69	0.18	***
q36	0.36	0.13	***
q37	0.53	0.13	***
Producer
q28	0.45	—	—
q29	0.47	0.19	***
q30	0.63	0.21	***
q31	0.55	0.22	***
q32	0.64	0.25	***
q27	0.58	0.20	***
Environment
q49	0.58	—	—
q51	0.63	0.12	***
q50	0.61	0.12	***
q53	0.48	0.12	***
q48	0.50	0.09	***
q47	0.67	0.12	***
q46	0.64	0.15	***
q52	0.63	0.15	***

Discussion

This study sought to explore the impact of factors subsumed under six dimensions on the life cycle of pharmaceutical products. The results showed that environmental factors, manufacturer-related factors, demand-related factors, competition-related factors, product-related factors, and distributor-related factors significantly influenced the life cycle of pharmaceutical products in Iran in that order.

There is a wide array of factors affecting the life cycle of pharmaceutical products. Based on the results obtained in this study, it seems that environmental factors and political/economic conditions of the society exercise the highest impact on the life cycle of pharmaceutical products.

A pharmaceutical supply chain comprises three main components: manufacturer, distributor, and consumer. The manufacturer receives the raw materials and converts them to the final product, which is eventually submitted to the distributor. The produced drug is then distributed among people via pharmacies, which are fed by the distributor.³² Thus, the first issue that is of great importance to produce a final product is supplying the raw materials. Due to the sanctions, the pharmaceutical market of Iran faced huge problems with regard to importing drugs and active pharmaceutical ingredients (APIs). Moreover, as a result of declining international revenues, Iran’s national currency (rial) considerably lost its value in comparison with international currencies. These factors contributed to measurable rise in prices and shortage of drugs in the pharmaceutical market of Iran. Following international laws, drugs are always exempt from sanctions; nevertheless, many international firms did not accept orders from Iran because of limitations in money transaction, inappropriate insurance, and uncertainty in sanction-related exemptions of particular products. Also, it is very difficult for domestic pharmaceutical companies to have access to line of credit to import drugs or APIs.³³ Therefore, sanctions have badly influenced Iran’s pharmaceutical industry. The findings of the current study also unveiled that problems related to the supply of raw materials and financial transactions play a significant role in the life cycle of pharmaceutical products.

On the other hand, Iran’s government has taken some measures in recent years to support the domestic production of drugs, for example, creating tariff and non-tariff barriers or even banning the import of certain drugs. These policies, however, have raised some concerns. For example, patients have a limited number of options in selecting appropriate drugs for their treatment. Furthermore, it is not clear whether manufacturing the drugs inside the country is economically viable. This governmental support has also led to illogical use of drugs in some cases. In addition, studies have indicated that the major drawback of Iran’s pharmaceutical industry is lack of necessary investment in research and development, stemming from the low profit margin in this industry. The government has taken some steps in this regard to encourage pharmaceutical firms to improve their research and development departments.²³ It therefore appears that policies adopted by the government and Food and Drug Administration have a profound effect on Iran’s pharmaceutical industry and the rise of drug quality. As indicated in the present research, the life cycle of pharmaceutical products (from introduction to decline) is influence by these policies.

The results of this study also revealed that the duration of market presence and advertisements are two other main factors influencing the life cycle of pharmaceutical products. Prior research has shown that advertisements raise people and physicians’ pharmaceutical awareness, and a firm’s sales partly depends on its reputation.^18,34 Advertisements of a particular drug increase the likelihood that physicians prescribe it for their patients. Coupled with factors like availability (through appropriate distribution by distribution companies) and quality, advertisements play a critical role in product sales. All these factors boost physicians’ loyalty, leading to further prescription of target products.³⁵ The study findings also indicated that brand reputation and power have a profound impact on the life cycle of pharmaceutical products. Similarly, previous studies have shown that brand reputation and power are directly associated with consumers’ image of the product brand, which is in turn affected by consumption experience.^36,37 This latter variable is directly related to product quality³⁷; that is, quality enhances a product’ attractiveness and increases the number of its consumers. Quality not only extends the PLC, but also shortens the time-to-peak sales and the level of peak sales.¹⁶ In alignment with these findings, the results of this study demonstrated that all quality-related indices were significantly related to PLC. The study also showed that appearance quality significantly affects the life cycle of pharmaceutical products. Likewise, prior research has revealed that appearance quality and packaging of a product significantly contribute to its attractiveness and distinction for customers. Appearance quality, including beautiful color and form, makes consumers feel that the product is more effective than its rivals. This phenomenon is more evident in over-the-counter (OTC) drugs. Repeat purchase will take place if appropriate effectiveness follows appearance quality.³⁸ Thus, in addition to product quality, the manufacturer’s advertising, the distributor’s measures to stimulate customers, and awareness raising among consumers influence PLC in all stages from introduction to decline.³⁹

Finally, the findings of the current study indicated that competition was another important factor influencing PLC. The same result has been repeatedly reported in previous studies.^15,18,22 Based on prior research, competition influences sales in different ways: competitive pressure goes up as the number of competing products increases. If a new supplier enters the market too late, it will have to use relatively more marketing resources to maintain greater market power and share.¹⁵ Considering Iran’s generic pharmaceutical market, increasing number of manufacturers has raised competitive pressure, which is critical from two aspects: first, increased competition among manufacturers boosts research and development activities, which results in higher drug quality and more demand for domestic products.²² Besides, this competitive pressure may reduce manufacturers’ sales, hence shortening PLC, especially for new products.¹⁵ It seems that, in the competitive market of Iran, the number of manufacturers and importers, the degree of access to them, and importers’ price and brand strength are among the factors affecting PLC. The impact of these factors on PLC was also confirmed in the present study. In contrast, based on the respondents’ ideas, the price of domestic products did not measurably influence PLC. This may be attributed to the fact that drug pricing in Iran is accomplished through government’s price controls usually based on the reference pricing method. Thus, the prices are more or less the same for all manufacturers²³; hence, they cannot be an influential factor in the life cycle of pharmaceutical products.

Conclusion

Overall, since PLC has a dynamic nature and possesses specific features in each stage, it appears that long-term decisions cannot be easily made. Hence, specific and dynamic decisions should be made for each of the four stages (i.e., introduction, growth, maturity, and decline) depending on its particular characteristics.⁴⁰ Six categories of factors influencing the life cycle of generic pharmaceutical products were identified in the current study. These factors should be properly taken into account in each stage of the PLC to suitably manage the products in the light of the available conditions.

Footnotes

Authors’ contributions

AM conceptualized the study, designed, performed the data analysis, statistical interpretation, and drafted the paper. MM supervised the project and the critical revision of the article. MMon contributed to the acquisition of data and data analysis.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Mehdi Mohammadzadeh

Dimensions and related factors influencing the life cycle of pharmaceutical products.

Author Biographies

Atefeh Mousavi, Ph.D of Pharmacoeconomy & Administrative Pharmacy, Pharmaceutical and Health Economics and Management Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran (a.mousavi@sbmu.ac.ir)

Marjan Monadizadeh, Pharm. D, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran (marjanmonadi14@gmail.com)

Mehdi Mohammadzadeh, Assistant professor at Department of Pharmacoeconomy & Administrative Pharmacy, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Pharmaceutical and Health Economics and Management Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

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