Sage Journals: Discover world-class research

Abstract

Network marketing’s growth is plagued by ethical concerns, emphasizing the importance of implementing training programs that prioritize integrity. This study aims to analyze how different forms of network marketing training play a role in shaping the marketing ethics of trainees with different identities. It examines how network marketing training shapes ethical proneness of the trainees. Differences in trainee identity (college students vs. incumbent marketers) and training form (college course vs. online training) are also explored. Structural equation model (SEM) is established amongst adaptability, class cooperation, motivation, self-efficacy, and ethical proneness. Through textual analysis, questionnaires, and sim-structural interview, this research conducts three studies to empirically analyze the relationship between factors of marketing training and ethical proneness. It explores the influencing mechanisms of network marketing training on marketing ethics. The relationship between network marketing training and ethical propensity is partially confirmed. The shaping mechanism of marketing ethics is different under different trainee identities and training forms. Positive learning motivation and self-efficacy do not constantly improve trainees’ marketing ethics. Informal online training plays a more significant role in shaping market ethics than college courses in network marketing. Lastly, college students are less likely to have their marketing ethics shaped by training than in-job marketers.

Keywords

marketing ethics network marketing training ethical proneness college education trainee identity

Introduction

Network marketing has emerged as an indispensable tool for reaching vast audiences and driving sales in today’s digital age. Its booming popularity, however, has been marred by ethical concerns. While many network marketers adhere to ethical practices, others unfortunately violate them to maximize profits. Ethical challenges, such as issues with privacy protection, security, and fair competition, are prevalent worldwide, including in China’s online marketing sector. For instance, Simba, a renowned network anchor in China, faced repercussions in 2020 when he sold a faulty bird’s nest on the TikTok platform and was subsequently punished by the government. Further highlighting these concerns, AAuto Quicker, a popular video We Media platform in China, reported instances of unfair competition behaviors in 2021. These included the deliberate fabrication and spread of false negative facts about others, malicious comments, and actions that disrupted the regular operations of other entities (Finance, 2021; Tech, 2021). Given the frequency of such events and the growing reliance on network marketing, ethical training in this domain becomes crucial. Whether for budding marketers in academic settings or seasoned professionals in the industry, network marketing training transcends being a mere advantageous skill—it’s a necessity. This training serves as a vital foundation to prevent ethical lapses and promote moral integrity in the field.

Ethical leadership profoundly impacts behaviors in this marketing ethical domain, with moral identity acting as a bulwark against unethical conduct (Al Halbusi et al., 2021; Al Halbusi, Ruiz-Palomino, & Williams, 2023). E-entrepreneurship emphasizes the importance of mentorship and rigorous ethical training (Fadi et al., 2023), while societal influences guide marketers’ ethical choices (Al Halbusi, Soto-Acosta, et al., 2023; Mohammed et al., 2023). In academia, faculty expertise and relevant curricula shape students’ ethic (Allam, 2020). Organizational ethics, especially in HR, correlate with job satisfaction and loyalty (Asad, 2020), stressing the need for ongoing ethical vigilance (Allam, 2017), and fair HR practices (Al-Kahtani et al., 2016; Allam, 2019). Globally, nations must balance economic goals with environmental responsibility (Shahid, Junfeng, et al., 2022). Corporate ethics span from leadership to transparency (Shahid, Akram, et al., 2022), with financial sectors prioritizing principles like transparency and risk management for stakeholder protection (Ali et al., 2019). Research on network marketing ethics is expanding, encompassing areas like big data marketing, tourism e-commerce, and marketing theory education (Mengchen, 2021). Ethical dilemmas in big data marketing and the influence of online comments have been analyzed (Demin, 2019).

Amidst the vast landscape of research delving into network marketing ethics, a lacuna remains: the intricate relationship between education and the development of ethical comportment in this sector. Ethical behaviors are fundamental to good business practices, but they don’t arise naturally; instead, they are carefully cultivated over time. The distinct roles of formal academic marketing education and informal network marketing training in establishing these ethical foundations are not clearly delineated, especially when comparing their efficacy. Furthermore, the nuanced mechanisms—how education, acting as a catalyst, influences a marketer’s ethical orientation through channels like motivation and self-efficacy—remain tantalizingly unexplored in depth.

Present research delves into the domain of network marketing training and its implications for molding marketing ethics. It seeks to investigate how different types of training, both formal and informal, impact participants’ understanding of ethics. By comparing the ethical perceptions of students with those of practicing marketers, the research aims to elucidate the underlying mechanisms. The approach is rooted in a blend of concepts from marketing, education, and ethical behavior, offering a comprehensive perspective on the complex interactions.

Drawing from the Social Cognitive Theory (SCT), student ethics model, and moral utility theory, this study examines how adaptability, classroom cooperation, self-efficacy, motivation, and ethical proneness intersect in the realm of network marketing ethics. Key concepts include Adaptability, Adjusting cognitively, emotionally, and behaviorally to uncertain situations, impacting both academic metrics like motivation and non-academic aspects such as self-esteem (Caya & Neto, 2018; Martin et al., 2013). Classroom Cooperation: The joint effort between students and teachers to reach mutual goals, prioritizing collaboration over competition. Coupled with technology, it fosters a learning environment that bolsters motivation and achievement (Castilla-Polo et al., 2022; Dwyer et al., 2004; Hanusch et al., 2015). Self-efficacy: One’s confidence in accomplishing tasks, which in a collaborative setting, grows through collective success and support, enhancing competence (Abcouwer, 2021). Strong self-efficacy can propel ethical actions and deter unethical behaviors, resulting in positive moral consequences (Bandura, 2002; Blewitt et al., 2018; Bussey et al., 2020; Hwang et al., 2021). Motivation: The internal or external drive guiding actions. Intrinsic motivation can be nurtured in the right settings, and ethically-focused network marketing training can produce conscientious marketers (Curren, 2014; Kraut, 2014; Melnyk et al., 2022; Shuhan & Ruihui, 2022). Ethical Proneness: A tendency toward moral action. Ethical proneness refers to an individual’s inclination toward ethical or unethical behavior. Researchers have developed various measurement scales to assess ethical proneness, including the Ethical Climate Index (ECI) for organizational ethical norms (Arnaud, 2010), the Moral Identity Scale (MIS) for self-perceived moral identity (Black & Reynolds, 2016), the Moral Judgment Test (MJT) for moral reasoning (Bartels, 2008). These scales help evaluate factors such as ethical decision-making, moral values, and attitudes toward misconduct. Researchers often adapt or create new scales based on their specific research goals.

Three studies are conducted to analysis the relationship of these key concepts. Study 1 explores the ethical dimensions within negative online reviews, using Taobao as a representative platform. Despite these negative reviews making up less than 1% of all feedback, they offer a unique insight into online marketing’s ethical challenges. Using the LDA model, the study extracts and then analyses themes to pinpoint these ethical issues. Study 2 use structure model to assesses the effects of internet marketing training on ethical proneness. Two comparative analyses were carried out. The first analysis revealed that network marketing training, whether in a college course or through online informer training, improved college students’ self-efficacy due to its adaptability and class cooperation. However, it did not enhance their ethical predisposition. The second analysis indicated that, while network marketing college courses could boost students’ self-efficacy through adaptability and class cooperation, they did not significantly impact their ethical predisposition. In Study 3, interviews with 31 participants revealed significant differences between practical and theoretical learning’s impact on ethics. Students are driven by academic motivations, while in-job marketers prioritize tangible benefits. Real-world marketers also face strict regulatory and competitive pressures, compelling ethical adherence, whereas students remain entrenched in theoretical understandings.

This research contributions to the disciplines of network marketing, business ethics, and education. The research seeks to provide deeper insights into the impact of training on the development of ethics, offer businesses a framework for designing their training modules, and equip policymakers with foundational knowledge to draft guidelines that enhance the ethical integrity of the network marketing environment.

There are four parts of this paper. Firstly, in the introduction, it illustrates the necessity of research by putting forward the practical problems and research gaps reflected by the study theme. The second part provides a theoretical background and conceptual model, explaining the relationship between various variables such as training traits, motivation, self-efficacy, and ethical proneness. The third part consists of three studies that test the relationship between variables between marketing ethics training model. The fourth part provides a discussion and conclusion of the research results, including its theoretical and practical implications. The final part mentions the limitations of the research.

Theoretical Background

Social Cognitive Theory

Social Cognitive Theory (SCT) is proposed by Bandura (2001). SCT emphasizes that learning occurs in a social context with dynamic interactions between the individual, environment, and behavior. Self-efficacy, a core concept of SCT, is one’s belief in their ability to achieve a specific task. The theory posits that self-efficacy can influence choices, effort levels, persistence, resilience, and emotional reactions. The idea that self-efficacy impacts ethical proneness and motivation can be rooted in SCT. SCT provides a robust framework to understand how individuals learn ethical behaviors and how their beliefs in their abilities (self-efficacy) influence their actions. Given the myriad of ethical challenges in marketing, integrating the principles of SCT into marketing ethical education can provide students with the tools they need to navigate their future careers ethically.

Student Ethics Model

Indartono (2020) proposed a structural model of student ethical behavior. This behavior model is used to test the internal and external factors on the idea that curriculum training affects the moral conduct of higher education students. The model tested the relationship amongst students’ ethics proneness, learning motivation, self-efficacy, resilience, knowledge articulation, team strain, and cooperative classroom environment. Moreover, this model depicts the impact of stability, knowledge articulation, team strain, and cooperative environment on learning motivation and self-efficacy; and affects the ethical proneness of trainees thereafter. Training characteristics’ influence on trainees’ ethical proneness has been confirmed.

Moral Utility Theory

According to moral utility theory (Hirsh et al., 2018), individuals’ moral decisions depend on maximizing subjective expected utility (SEU). Individuals will evaluate ethical and unethical behaviors. When the expected benefits of moral violations exceed the expected costs, unethical behaviors will occur. The theory provides a model to understand the factors that motivate people to act ethically and unethically. Factors that affect the moral decision-making of individuals include their internal decision-making mechanism, norms and cultural factors, benefits of behavior and environmental factors when making decisions. Depending on their ethical proneness, different trainee groups tend to make different marketing decisions. Network marketing training takes into account norms, cultural factors, and benefits of behavior.

Hypotheses and Conceptual Model

Learning will stimulate and develop these factors that motivate learners’ interest in learning from their perspective of themselves (Hennig, 2010). Self-formation of morality is a process embedded in trainees’ interaction with the world around them, and inseparable from their past, present and future lives. In this process, individuals actively choose and intentionally implement moral standards to live a meaningful life (Wiltshire et al., 2020).

Effect of Trainee Identity and Training Form

Social identity theory holds that individuals develop unique personal and social identities based on their group membership (Aguirre et al., 2020). An ethical judgement is developed by considering environmental factors and becoming aware of issues’ ethical implications (Krishnamurthy et al., 2022). For example, when identified as management, senior executives will be willing to establish shared values amongst employees and considerably focus on legal and ethical values to more consistently comply with the law and organizational ethics (Pearce, 2013). In the network marketing context, student and enterprise marketer identities have different characteristics, and their ethical proneness is affected by various environmental factors. Accordingly, research needs to be conducted in comparison.

Ethics training based on normative ethical theory has minimal effect on how people act ethically. Sensemaking approaches to ethics training have proven to be effective (Brandt et al., 2021). Online training would constantly provide related information on different topics and educational resources for research. Whilst in a face-to-face setting, such as enrolling in college courses, trainees would be able to discuss and reflect on specific issues. It helps shape morality by making trainees feel authentic and more likely to participate in moral decision-making (Pizzolato & Dierickx, 2021). A comparative study should be conducted to determine how different forms of training impact moral shaping. To analyze the different relationships, we conduct structural equation model (SEM) separately for the different trainee identities and training form groups.

Conceptual Model of Marketing Ethics Training

On the bases of the preceding hypotheses, the conceptual model of marketing ethics training proposed in this study is as follows (Figure 1).

Figure 1.

Conceptual model of marketing ethics training.

Overview of Studies

Three studies are conducted to test the relationships in the conceptual model of marketing ethics training. The goal of study 1 is to analyze the ethical deficiency of Internet marketing. Negative customer comments crawled from the Taobao platform were used to analyze the ethical factors. Study 2 tests the proposed hypotheses by SEM to verify the overall relationship amongst adaptability, class cooperation, motivation, self-efficacy, and ethical proneness. The difference between different groups is also tested. Study 3 used semi-structured in-depth interviews and ground theory to analysis the difference of ethical factors that exists in different groups (Figure 2).

Figure 2.

Overview of three studies.

Study 1: Marketing Ethics Reflected in Online Negative Reviews

This study analyses the dimensions related to marketing ethics reflected in negative online reviews. Online negative comments are bad reviews of consumers on products purchased online. Even though the negative comments constitute less than 1% of all online feedback, they shed light on the ethical issues prevalent in online marketing. Apart from the quality of goods and services, factors leading to poor evaluation are generally related to business ethics. Therefore, analyzing bad reviews helps us understand the constituent factors of marketing ethics. As the largest third-party online shopping platform in China, the ethical issues of network marketing reflected by the negative comments of Taobao are representative. For topic extraction, the Latent Dirichlet Allocation (LDA) model analysis technique is used to select the comment text of negative reviews. Lastly, the obtained theme distribution is analyzed and interpreted to examine the ethical dimensions.

Data Collection

Octopus Collector is an Internet data collector. It converts web page data into structured data and stores it in various forms. This study takes the Taobao platform as the data collection object. This study chooses to gather the bad evaluation data of six categories of products in Taobao commodity classification: sports, mobile phones, electrical appliances, home decoration, digital, and car products. This study selects the products with the largest sales volume in each category. The time interval is from May 1 to June 30, 2022. The crawled text is split by word segmentation tool Jieba and to remove irrelevant characters and deactivated words. After data pre-processing, we finally get 3,560 user comments data.

Data Analysis

The Latent Dirichlet Allocation (LDA) topic model is a Bayesian document topic generation model with three layers. The LDA model is often applied to the study of textual topic discovery. The calculation formula is as follows:

$p (w / d) = p (w / t) * p (t / d)$ (1)

where $w$ represents the word in the text, $d$ is the independent text and $t$ is the topic of the corresponding text, thereby forming a three-layer probabilistic model. Therefore, this study determines the topic and extracts keywords based on the LDA model for ethics-related text data. The number of topics is determined using the perplexity method. Topics of theme analysis are shown in Table 1.

Table 1.

Topics of Negative Comments.

Topic naming	Top 10 keywords of the topic
Topic 1: Protection of consumer privacy	Phone number, arrange, noise, huge, be cautious, address, selling my address, price, harassment call, never
Topic 2: Safe and reliable goods	I cannot believe the wrong delivery, receipt, just so-so, quality, tawdry, frame, function, holistic, in the mud
Topic 3: Fair competition	Unpack, not this one, compare, price, counterfeit, induction, diminish, customer service, tawdry, any
Topic 4: Credibility and integrity	Depict, discrepancy, waste of money, worth nothing, seller, layer, picture, indeed, never, a litter

Results of Study 1

The text analysis of negative customer comments on Taobao confirmed a lack of CSR and marketing ethics. Apart from many negative comments on the quality of goods or services, consumers’ attacks on Taobao sellers’ social responsibility and marketing ethics mainly focus on the following five aspects. Privacy concerns: Advertising or other harassment caused by personal information disclosure owing to the purchase of products has caused consumers to worry about personal information security. Safe and reliable products: Numerous negative comments focus on complaints on product quality and delivery. Fair competition: Some customers complained that sellers’ misleading statements and persuasiveness have caused them to buy products that did not meet their expectations. They originally wanted to purchase products from other manufacturers. Credibility and integrity: Attacks in this regard focus on the false description of the seller and the discrepancy between the picture and the real object. There are also some complaints on false promises. CSR: Consumers believe that the material or smell of the product may cause environmental pollution. They also have doubts on the brands or credits of products. Lastly, they questioned the company’s reputation.

The topics are summarized by manual discernment and combined with keywords. Thereafter, on the bases of the meaning of the Chinese words included in each topic, we summarize the dimensions each topic belongs to. Amongst them, topic 2 (Safe and reliable of goods) accounts for the highest proportion of 23.65%. The other four topics have a smaller percentage difference (Figure 3) in the following order: credibility and integrity (18.73%), protection of consumers’ privacy (16.70%), fair competition (13.12%), and CSR (11.27%; Figure 3).

Figure 3.

Percentage of each topic.

Study 2: Influence of Network Marketing Training on Ethical Proneness

Questionnaire Design

This study aimed to test the relationship between variables in the conceptual model and compare the differences between different groups of participants. Literature reviews were conducted to identify measurement items. Repeated experiments and expert consultation were conducted to ensure their validity. Ultimately, 18 measurement items were identified, and a marketing ethical proneness questionnaire was developed. A seven-point Likert-type scale ranging from 1 (strongly disagree) to 7 (strongly agreed) was used to measure the items (Table 2).

Table 2.

Measurement Item Structure and Source.

Variables	Measuring items	References
Adaptability	I can deal with emergencies in the learning process.	Li et al. (2008)
	I believe I can learn network marketing well.
	Difficulties in learning can help me master knowledge better.
Self-efficacy	I can master the knowledge and skills of network marketing.	Li et al. (2015)
	I can apply the knowledge and skills of network marketing to my actual work.
	I am willing to share network marketing knowledge and skills with people around me.
Learning motivation	I hope the network marketing course can improve my income.	Indartono (2020)
	I hope the network marketing course can improve my employability
	I want to prove my personal ability.
Classroom cooperation	Training courses provide opportunities for discussion with others.	Indartono (2020)
	Training courses enable me to find friends with the same interests.
	Training courses bring me more opportunities for personal development.
Ethical proneness	Information about customers should not be disclosed.	Shen (2016)
	Ensure that the products are not damaged or lost during logistics distribution.
	Product description must not be false.
	Fair competition.

In this research, we adopt the questionnaire-star platform, which is commonly used by Chinese scholars to conduct questionnaire surveys. A total of 504 questionnaires are distributed. However, questionnaires that are incomplete, short in answering time or invalid are excluded, leading to 328 valid questionnaires obtained. The questionnaire results indicate that 65.08% are valid. Then, the demographic characteristics of respondents (Table 3) showed that 66.46% of the respondents are incumbent. Additionally, 69.21% of the respondents are taking network marketing training from online training.

Table 3.

Demographic Characteristics of Respondents (N = 328).

Demographic characteristics		Number of respondents	%
Gender	Male	145	44.21
	Female	183	55.79
Age	18–25 years old	129	39.33
	26–30 years old	95	28.96
	31–40 years old	86	26.22
	41–45 years old	16	4.88
	46–50 years old	2	0.61
Trainee identity	College students	110	33.54
	Incumbent	218	66.46
Training form	College course	101	30.79
	Online training	227	69.21

Modeling

This study uses PLS-SEM to test the relationship between the variables that influence the trainees’ ethical proneness. PLS-SEM has the advantage of fully mining the information from the data and minimizing the error term. Furthermore, sample size, model identification, and distribution state of data are not high requirements, and collinearity between variables can be effectively handled. The PLS-SEM method also produces higher average variance extracted (AVE) and composite reliability (CR) values, indicating a more reliable and valid construct (Ganesh & Justin, 2021).

Reliability and Validity Analysis

SPSS22.0 was used for the reliability and validity analysis of variables in the model. The results (Table 7) showed that the scale’s total clone Bach α coefficient was .916, and the KMO value of the exact scale obtained by factor analysis was 0.932. Cronbach’s Alpha values of each potential variable were all greater than the critical value of .7, indicating that the measurement indexes of each potential variable were consistent and had good and stable notification reliability. KMO = 0.932 > 0.8, Bartlett spherical test significance probability value p = .000 < .05, indicating that the scale is suitable for factor analysis. Exploratory factor analysis was conducted on the questionnaire by the principal component analysis method. The total explanatory variation of the 5 factors reached 68.002%, indicating that the scale structure validity was good.

Table 4 showed that the standardized factor load of sample data is all greater than 0.6, indicating good reliability of the subject; CR value of combination reliability is all greater than 0.7, indicating internal consistency; AVE value is all greater than 0.4, indicating good convergence validity among latent variables.

Table 4.

Convergent Validity and Composite Reliability.

Constructs	Items	Standardized loadings	AVE	CR
Adaptability	AD_1	0.667	0.497	0.747
	AD_2	0.741
	AD_3	0.705
Self-efficacy	SEF_1	0.685	0.490	0.742
	SEF_2	0.711
	SEF_3	0.703
Motivation	MOT_1	0.675	0.439	0.701
	MOT_2	0.635
	MOT_3	0.677
Class-cooperation	CO_1	0.706	0.508	0.756
	CO_2	0.707
	CO_3	0.725
Ethical proneness	EP_1	0.706	0.491	0.793
	EP_2	0.707
	EP_3	0.76
	EP_4	0.622

The discriminant validity of the questionnaire requires that the observed values should be distinguished when different topics are used to measure different latent variables. Table 5 shows that compared with the original model, all fitting indicators of other models are worse, and they meet the requirement of discriminative validity by chi-square test with significance level of .001, indicating that the model has good discriminative validity.

Table 5.

Discriminant Validity.

Index	Model	Combination	X²	df	X²/df	GFI	CFI	RMSEA	Compare	ΔX²	Δdf
1	Research		197.533	98	2.016	0.932	0.956	0.056
2	Four-factor 1	AD + CO, SEF, MOT, EP	211.325	100	2.113	0.928	0.951	0.058	2 vs. 1	13.792***	2
3	Four-factor 2	CO + SEF, MOT, EP, AD	467.013	101	4.624	0.878	0.837	0.105	3 vs. 1	269.48***	3
4	Four-factor 3	SEF + MOT, EP, AD, CO	217.717	100	2.177	0.925	0.948	0.06	4 vs. 1	20.184***	2
5	Four-factor 4	MOT + EP, AD, CO, SEF	325.562	100	3.256	0.881	0.9	0.083	5 vs. 1	128.029***	2
6	Three-factor 1	AD + CO + SEF, MOT, EP	468.161	102	4.59	0.877	0.837	0.105	6 vs. 1	270.628***	4
7	Three-factor 2	CO + SEF + MOT, EP, AD	222.337	102	2.18	0.924	0.947	0.06	7 vs. 1	24.804***	4
8	Three-factor 3	SEF + MOT + EP, AD, CO	395.028	101	3.911	0.853	0.869	0.094	8 vs. 1	197.495***	3
9	Two-factor 1	AD + CO + SEF + MOT, EP	255.761	102	2.192	0.923	0.945	0.06	9 vs. 1	58.228***	4
10	Two-factor 2	CO + SEF + MOT + EP, AD	399.427	103	3.878	0.853	0.868	0.094	10 vs. 1	201.894***	5
11	Single-factor 1	AD + CO + SEF + MOT + EP	406.534	104	3.909	0.85	0.866	0.094	11 vs. 1	209.001***	6

***

Correlation is significant at the 0.001 level.

Correlation Analysis and Multicollinearity Diagnosis

Pearson correlation analysis was conducted for 5 factors, and the results (Table 6) showed that correlation coefficients of all factors were above 0.4, showing a positive correlation. p = .00 < .01 in bilateral test, indicating obvious significance.

Table 6.

Correlation Matrix of Variables.

	Pearson Correlation Sig. (2-tailed)
Variables	AD	SEF	MOT	CO	EP	Cronbach’s alpha	KMO
AD	1	.763^**.000	.646^**.000	.694^**.000	.441^**.000	.745	0.687
SEF		1	.690^**.000	.728^**.000	.454^**.000	.734	0.682
MOT			1	.678^**.000	.534^**.000	.701	0.653
CO				1	.524^**.000	.760	0.689
EP					1	.790	0.774

Correlation is significant at the 0.01 level (2-tailed).

Table 7 showed that VIF of variables is less than 3 and the variance ratio of variables with condition index greater than 15 is less than 0.9, indicating that there is no multicollinearity between model variables.

Table 7.

Multicollinearity Diagnosis of Variables.

Dimension	Eigenvalue	ConditionIndex	(Constant)	Adaptability	Self_efficacy	Motivation	Class_cooperation
1	4.958	1.000	0.00	0.00	0.00	0.00	0.00
2	0.18	16.719	0.93	0.04	0.06	0.00	0.03
3	0.10	22.563	0.05	0.34	0.05	0.66	0.05
4	0.008	24.289	0.01	0.08	0.03	0.30	0.89
5	0.007	27.424	0.02	0.54	0.86	0.04	0.03
VIF				2.722	2.149	2.240	2.586

Model Fit

After the modification of the SEM model, the fit degree of the model has met the adaptation standard, and Table 8 shows that the fit degree of the research model is good.

Table 8.

Modified Model Fit.

Fitting index	Index value	Adaptation standard/threshold value
X²/df	2.016	1–3
RMSEA	0.056	0.05–0.08
GFI	0.932	>0.9
AGFI	0.905	>0.9
CFI	0.956	>0.9

According to the fitting index results of each model in Table 9, it can be seen that the model in this study has a good fit. The “measurement invariance” test results showed that p = .000 < .05 of Structural residuals and Measurement residuals models, indicating that the model was stable. Even though p values of Measurement weights model and Structural weights model are greater than .05, compared with Unconstrained model. Their values of ΔNFI, ΔRFI, ΔIFI, ΔTLI, ΔCFI, ΔGFI, and ΔAGFI are all less than 0.05, which still indicates that the model has good stability (Table 10). The “measurement invariance” test is passed and multi-group analysis can be performed.

Table 9.

Multi-Group Analysis Adaptation.

Model	CMIN	DF	CMIN/DF	NFI	RFI	IFI	TLI	CFI	GFI	AGFI	RMSEA	AIC	ECVI
Unconstrained	948.933	490	1.937	0.872	0.843	0.934	0.917	0.933	0.897	0.856	0.031	1,328.933	1.364
Measurement weights	976.837	534	1.829	0.868	0.852	0.936	0.927	0.935	0.894	0.865	0.029	1,268.837	1.303
Structural weights	1,018.922	554	1.839	0.862	0.851	0.932	0.926	0.932	0.89	0.864	0.029	1,270.922	1.305
Structural residuals	1,127.489	578	1.951	0.848	0.842	0.92	0.916	0.919	0.878	0.856	0.031	1,331.489	1.367
Measurement residuals	1,188.315	642	1.851	0.84	0.85	0.919	0.925	0.92	0.872	0.865	0.03	1,264.315	1.298

Table 10.

Invariance Test List.

Model	ΔCMIN	ΔDF	p	ΔNFI	ΔRFI	ΔIFI	ΔTLI	ΔCFI	ΔGFI	ΔAGFI
Measurement weights	27.904	44	0.972	−0.004	0.009	0.002	0.01	0.002	−0.003	0.009
Structural weights	69.989	64	0.283	−0.01	0.008	−0.002	0.009	−0.001	−0.007	0.008
Structural residuals	178.556	88	0.000	−0.024	−0.001	−0.014	−0.001	−0.014	−0.019	0
Measurement residuals	239.382	152	0.000	−0.032	0.007	−0.015	0.008	−0.013	−0.025	0.009

Empirical Results

Multi-group structural equation model with identity as moderator to detect whether or not the identity variable has a moderating effect in the model, 328 valid samples was divided into students (110 pieces of data) and non-students (218 pieces of data) by identity for multi-group analysis. The results are shown in Table 11.

Table 11.

Parameter Estimates for the Structured Models of Different Identity Groups.

Paths	College students		Incumbent
Paths	Estimate	p	Estimate	p
Adaptability → self-efficacy	0.752	***	0.648	***
Class-coop → motivation	0.508	***	0.936	***
Class-coop → self-efficacy	0.313	***	0.266	***
Motivation → ethical proneness	0.380	0.029	0.700	***
Self-efficacy → ethical proneness	0.322	0.012	0.365	***

***

Path effect is significant at the 0.001 level.

The estimated result shows that all hypotheses are confirmed in the incumbent group. H1 of the influence of adaptability on self-efficacy is supported. H2 and H3 of the influence of class cooperation on motivation and self-efficacy are supported. H4 and H5 of the influence of motivation and self-efficacy on ethical proneness are supported. Whilst in a group of college students, the hypothesis of motivation and self-efficacy would influence ethical proneness are not certified. Even though the network marketing training can enhance college students’ self-efficacy due to the training’s adaptability and class cooperation, it does not improve their ethical proneness. Meanwhile, for the on-the-job marketer group, all the parameters work to improve their ethical predisposition (Figures 4 and 5).

Figure 4.

Estimated results of the model (college students).

Figure 5.

Estimated results of the model (incumbent marketers).

Another multi-group SEM with training form as moderator was estimated to test the influence of training form. A total of 328 valid samples were divided into college courses (101 pieces of data) and online training (227 pieces of data) for multi-group analysis to detect whether or not the training form has a moderating effect on the model. The results are shown in Table 12.

Table 12.

Parameter Estimates for the Structured Models of Different Training form Groups.

Paths	College course		Online training
Paths	Estimate	p	Estimate	p
Adaptability → self-efficacy	0.381	***	0.655	***
Class-coop → motivation	0.781	***	1.003	***
Class-coop → self-efficacy	0.592	***	0.248	***
Motivation → ethical proneness	0.841	***	0.652	***
Self-efficacy → ethical proneness	0.264	0.194	0.284	***

***

Path effect is significant at the 0.001 level.

The estimated result shows that all hypotheses are confirmed in the online training group. H1 of the influence of adaptability on self-efficacy is supported. H2 and H3 of the influence of class cooperation on motivation and self-efficacy are supported. H4 and H5 of the influence of motivation and self-efficacy on ethical proneness are supported. Whilst in the college course group, the hypothesis that self-efficacy would influence ethical proneness is not certified. The informal online training can enhance trainees’ ethical predisposition due to the training’s adaptability and class collaboration, by improving their self-efficacy and motivation. On the other hand, while the college course can boost students’ self-efficacy through its adaptability and class cooperation, it does not enhance their ethical predisposition (Figures 6 and 7).

Figure 6.

Estimated results of the model (college course).

Figure 7.

Estimated results of the model (online training).

Study 3: Comparison of Different Groups

As found in Study 2, the training in internet marketing has a different influence on the shape of ethical proneness in different groups. College students’ ethical proneness did not improve as a result of training on motivation and self-efficacy. Another multi-group SEM test shows that internet marketing training improved motivation and thus improved ethical proneness in the college course group. However, the improvement in self-efficacy did not improve ethical proneness. Students in college courses are less likely to develop market ethics than in-service marketers who receive informal online marketing training. Based on studies 1 and 2, we conducted study 3 to examine the mechanisms behind internet marketing training that impact ethical proneness differently. The statistical results of study 2 partly supported the relevant assumptions proposed by the study, and some assumptions were not supported. Structured interviews with trainees can provide a deeper understanding of the specific reasons for this difference.

Data Collection

This study relied on semi-structured in-depth interviews to collect the data. In-depth interviews can better focus on the interviewee’s feelings so that both sides can have an open and detailed discussion. The interviewers were the first and second authors themselves. They deeply understand teaching and online marketing training and research on marketing ethics issues. Therefore, they can interview trainees from a more comprehensive perspective.

This study follows grounded theory’s theoretical sampling procedure. It sets the following screens alongside the research object to ensure typicality: (a) being a college student in school or an on-job marketer. (b) Learn about marketing ethics in internet marketing through course learning or other training methods. There are two sources of interviewees, one is the WeChat group of online marketing practitioners. We recruit on-the-job interviewees who have received network marketing training and are conducting online marketing through WeChat marketing group. Another source is college students who have studied network marketing through college courses. The interviewees of college students who have studied network marketing course are also recruited through WeChat groups. 31 interviewees were recruited. The demographic information of the interviewees is shown in Table 13. Each interview takes 20 to 30 min.

Table 13.

Demographic Characteristics of Interviewees (N = 31).

Demographic characteristics		Number of respondents	%
Gender	Male	19	61.29
	Female	12	38.71
Age	18–25 years old	16	51.61
	26–30 years old	7	22.58
	31–40 years old	8	25.81
Trainee identity	College students	13	41.94
	Incumbents	18	58.06
Training form	College course	13	41.94
	Online training	18	58.06
Employment industry of incumbents	Agricultural products	6	19.35
	General merchandise	4	12.90
	Real estate	2	6.45
	Online pharmacy	2	6.45
	Other	4	12.90

Analysis Based on the Ground Theory

In this study, the ground theory is used for data analysis, and it has a relatively perfect coding paradigm, which can gradually refine concepts and categories from original data, and finally realize theoretical construction (Zhang Xixi & Bingcheng, 2022). This study is conducted for the in-depth mining and analysis of the logical relationship between variables about trainees’ ethic shaping.

The coding of the contents of semi-structured in-depth interviews is displayed as follows. Tables 14 and 15 show the content codes of semi-structured in-depth interviews with the interviewees.

Table 14.

Coding from Interview Excerpts to Theoretical Coding (College Students).

Interview excerpts	Initial coding	Focused coding	Theoretical coding
The teacher will guide us to discuss cases and how to deal with emergencies. For example, how to remedy the loss of customers caused by enterprise products.	Simulated uncertainty	Adaptability	Training characteristics
I discussed with my classmates how to deal with market changes and competition between groups.	Cooperation and competition among peers	Classroom cooperation	Training characteristics
I plan to improve my communication skills at work in the future. Learning marketing enhanced my ability to do this.	Improvement of personal marketing ability	Learning motivation	Training effect
I am more confident about working in an enterprise in the future. Believe I can get the ideal position.	Increase social confidence and skills.	Self-efficacy	Training effect
I hate receiving harassing calls. Sometimes I get more harassing calls after I buy something on Taobao.	Empathy with customers	Security of privacy	Ethical proneness
I used to lose things I bought online. This causes a lot of trouble and sometimes economic losses.	Safe delivery and complete goods	Safe and reliable goods
Use proper means to win the team in marketing case simulation. We disdain to do so.	Competitive and candid	Fair competition
False propaganda or malicious competition will hurt the image of the enterprise.	Honesty, reliability, and preciseness	Credibility and integrity

Table 15.

Coding from Interview Excerpt to Theoretical Coding (On-the-Job Marketers).

Interview excerpts	Initial coding	Focused coding	Theoretical coding
The daily work of marketers is to deal with various uncertainties.	Agility and resilience	Adaptability	Training characteristics
I will exchange network marketing skills with my peers. Marketing people must communicate and exchange more.	External communication is the instinct of marketers	Classroom cooperation	Training characteristics
Internet marketing has given me more opportunities to develop customers and improve performance.	Better income and higher position	Learning motivation	Training effect
I can better make friends with customers, which is essential for marketing.	Mastering marketing skills	Self-efficacy	Training effect
Inadvertent disclosure of customer information will be taken advantage of by competitors. I have learned a profound lesson.	Protecting customers’ privacy is protecting your own market	Security of privacy	Ethical proneness
If there is a problem with the quantity or quality of the product delivered, the customer will not buy it again.	Maintain customer loyalty	Safe and reliable goods
The customer knows about other brands. Bad-mouthing a competitor will turn customers off.	Competitive and candid	Fair competition
The company has standard introduction materials. I can’t mislead customers for the sake of performance.	Honesty, reliability, and preciseness	Credibility and integrity

Findings

Our interviews with 31 respondents revealed that different groups are affected by various factors when forming their morals. However, the main effect is that there is a big difference between practical learning and knowledge learning.

Firstly, interest is a critical factor affecting the shaping of marketing ethics. In school, students’ interests are virtual, while on-the-job marketers’ interests are actual. The students at school care about the course scores and the evaluation of teachers and classmates. The on-the-job marketers care about their real economic benefits. The training characteristics such as class cooperation and adaptability influence the trainees in totally different ways. As depicted by one on-the-job marketer:

In training, we mainly discussed online marketing skills, but we all know the consequences of consumer dissatisfaction. For example, a Taobao shopkeeper complained that because a customer did not receive the delivered goods, he constantly gave him nasty comments, which made his shop credit drop 80 places in the website ranking. As a result, the sales volume has been dramatically reduced.

Secondly, Laws and social norms play a significant role in the formation of marketing ethics. Even though students learn how to comply with the law and maintain a positive corporate image in school, these are concepts based on non-realistic marketing knowledge. The ethical behavior of on-the-job marketers is governed by laws, industry rules, personal character, and integrity. The management from the law and industry is more strictly, which has a pronounced deterrent effect on marketers. This is very different from student’s dissatisfaction with the school shopping experience. As depicted by one on-the-job marketer:

TikTok bloggers can attract many fans and earn a lot of money. But if they are found to have immoral behavior, they will be fined a lot of money, or even their business will go bankrupt.

Thirdly, the pressure of competitors compels enterprises to abide by the code of ethics. The regulation comes not only from the government and consumers but also from competitors. The market competition is becoming more and more fierce. Every enterprise is susceptible to market trends and pays close attention to competitors’ strategies. In addition, the development of information technology makes the information collection among competitors more extensive and accurate and enables them to supervise each other. As depicted by one on-the-job marketer:

Our company has a standard product description template and a pre-planned response mode for questions beyond the template. The competition in our industry is fierce, and we all know much about each other. Therefore, it is impossible to do anything against marketing ethics. If the industry’s reputation is terrible, there will be no market.

Discussion and Conclusion

This study aims to explore the impact of network marketing training on trainees’ moral inclination, find a better way to shape their market morality through network marketing training, and reduce the moral hazard of e-marketing. In study 1, the text analysis of negative comments on Taobao confirmed the dimension of ethical deficiency in online marketing. In study 2, the experimental analysis of the survey data partially supports the marketing ethics training conceptual model. College students’ marketing moral shaping through marketing training is inferior to that of on-the-job marketers. Study 3 tries to discover the reasons behind the differences.

Discussion

Given the sharing of personal and financial information over the internet, online marketing prioritizes privacy protection and reliability. Study 1 highlights that computer science brings unique technical characteristics to network marketing ethics. E-commerce platforms like Taobao offer an immediate, public avenue for feedback, contrasting with traditional marketing’s reliance on direct, often private feedback channels. This immediacy means that even a small fraction of negative online reviews can profoundly impact a brand due to the expansive digital reach. E-commerce introduces unique ethical issues, such as the challenge of non-delivery and heightened potential for deceptive marketing, given that online buyers can’t physically inspect items pre-purchase. Trust, encompassing both product authenticity and data security, is essential in this digital realm. Any ethical missteps can rapidly erode this trust, further compounded by the impersonal nature of online transactions.

Study 2 reveals that real-world implications heavily influence marketers in establishing ethical practices. While network marketing training notably impacts on-the-job marketers, its effect on college students is minimal, highlighting the importance of practical application in ethical education. College students, primarily driven by theory, don’t resonate with these trainings as much as in-service marketers, who value hands-on experience due to the tangible repercussions they face professionally. Study 3 further underscores that mere education isn’t enough to shape ethics; it’s also dictated by individual interests and real-world regulations. While previous research has indicated that rational choice leads marketers away from unethical behavior, the present findings stress that experiencing consequences in actual market settings enhances online marketers’ ethical tendencies. This research provides an evolved understanding of marketing ethics, especially in the online domain, building upon earlier studies that noted shifts in marketing ethics due to changes in communication technology.

Conclusion

The present research empirically tested the relationship of factors in the model of marketing ethics training. In the group of incumbent, the Internet marketing training’s adaptability and corporation positively influence trainees’ motivation and self-efficacy, thereby positively influencing their ethical proneness. Whilst in the college students group, the paths from motivation to ethical proneness and from self-efficacy to ethical proneness are not confirmed. College courses have less influence on marketing ethics than online training courses. In the college course of network marketing training, students’ self-efficacy does not improve their ethical proneness.

Theoretical Implication

Positive learning motivation and self-efficacy do not constantly improve the marketing ethics of trainees. This study emphasizes for the first time the market ethics shaping of college students and on-the-job marketers through network marketing training. When the moral identity of marketers is improved, they can reduce sales orientation and increase customer orientation, thereby improving sales performance (Itani et al., 2022). However, the present research found that the shaping mechanism of marketing ethics is different under different trainee identities and training forms.

Firstly, the influence of network marketing training on ethical proneness is only effective in incumbent marketer groups. Amongst trainees who received network marketing skills, incumbent marketers had stronger marketing ethics than college students. Effects of motivation and self-efficacy vary in different groups. Both motivation and self-efficacy have no significant influence on ethical proneness in the college student group.

Secondly, training form has different effects. College courses in network marketing and online training improve trainees’ self-efficacy, whilst only in the study of online training does self-efficacy improve the trainees’ ethical proneness. College courses in network marketing have no significant influence on shaping moral behavior.

Practical Implication

The findings have several practical implications for the realm of online marketing and education:

Feedback Management in E-commerce: Given that e-commerce platforms like Taobao allow for instant, widespread feedback, businesses need robust mechanisms to address grievances promptly. An isolated negative review can have a magnified impact due to the platform’s reach. Therefore, businesses should prioritize excellent customer service and efficient feedback resolution systems.

Enhanced Trust Protocols: E-commerce platforms must ensure top-tier security and data protection measures. Consumers entrust these platforms with personal and financial data, and any breach or misuse can quickly erode this trust. Companies should invest in securing their systems and regularly communicate these measures to users, emphasizing product authenticity and transactional security.

Tailored Training Modules: The disparity between college students and in-service marketers in the absorption of network marketing training suggests that one-size-fits-all training approaches are ineffective. Training modules should be tailored according to the target audience, emphasizing practical applications for professionals and perhaps blending theory with practice for students.

Real-world Ethical Scenarios: Given that on-the-job marketers are more influenced by real-world repercussions, training for them should incorporate real-world scenarios. By simulating situations where they might face ethical dilemmas and real-world consequences, these professionals can be better equipped to handle such challenges in their roles.

Revised Curriculum for Students: Educational institutions might want to reconsider how they teach online marketing ethics to students. Incorporating more hands-on, practical experiences or case studies might bridge the gap between theory and practice, enhancing the effectiveness of the teaching.

Holistic Ethical Formation: Beyond just formal training, the shaping of ethical norms in online marketing involves a blend of individual interests, real-world implications, and regulations. Businesses and educational institutions alike should recognize this and work toward creating environments that nurture ethical considerations from multiple angles.

Limitations and Future Directions

Firstly, current research has primarily focused on the impact of training programs on ethical behavior and decision making. However, it is crucial to consider other environmental factors that shape ethical behavior in the online marketplace, such as personal characteristics, company culture, organizational policies, and leadership.

Secondly, exploring diverse online training methodologies that shape ethical proneness would provide valuable insights for the design and improvement of effective and engaging online training programs.

Thirdly, current study has limited exploration of training methods. Further studies should expand upon this by examining additional factors that can influence ethical behavior and decision making, such as codes of conduct and ethical decision-making models.

Fourthly, the reasons behind the variations in ethical formation among individuals and organizations warrant further investigation. By understanding the underlying factors, such as personal values, cultural background, previous experiences, and organizational culture, organizations and individuals can make informed decisions and promote ethical behavior.

Footnotes

We thank Professor Luo Yu-Yan for her assistance in selecting topic and writing paper,we also thank students and teachers who participated in the survey for their support and cooperation.

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) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: 1. This research was funded by National Office for Philosophy and Social Science,20BGL157. 2. Philosophy and Social Science Research Foundation,Chengdu University of Technology (YJ2023-PY004)

Ethical Approval

This study is not applicable to animal and human studies,and no ethical statement is required.

ORCID iD

Yani Dong

References

Abcouwer

Emőke

Rcz

(2021). Innovating management and leadership in contemporary times: Motivation and evaluation in learning. Proceedings of the European Conference on Management, Leadership & Governance. Academic Conferences Limited.

Al-Kahtani

N. S.

Khan

N. A.

Allam

(2016). Unfair HRM practices in the telecom sector in Saudi Arabia: An empirical investigation of selected public and private sector companies. International Journal of Applied Business and Economic Research, 14(10), 6377–6396.

Al Halbusi

Ruiz-Palomino

Morales-Sanchez

Fattah

F. A. M. A.

(2021). Managerial ethical leadership, ethical climate and employee ethical behavior: Does moral attentiveness matter? Ethics & Behavior, 31(8), 604–627.

Al Halbusi

Ruiz-Palomino

Williams

K. A.

(2023). Ethical leadership, subordinates’ moral identity and self-control: Two- and three-way interaction effect on subordinates’ ethical behavior. Journal of Business Research, 165, 114044.

Al Halbusi

Soto-Acosta

Popa

(2023). Analysing e-entrepreneurial intention from the theory of planned behaviour: The role of social media use and perceived social support. International Entrepreneurship and Management Journal, 19, 1611–1642.

Ali

Zhang

Abbas

Draz

M. U.

Ahmad

(2019). Symmetric and asymmetric GARCH estimations and portfolio optimization: Evidence from G7 stock markets. Sage Open, 9(2).

Allam

(2017). Employee disengagement: A fatal consequence to organization and its ameliorative measures. International Review of Management and Marketing, 7(2), 49–52.

Allam

(2019). Exploring ambient discriminatory HRM practices: An insight from Kingdom Telecom Company. The Journal of Social Sciences Research, 5(3), 646–654.

Allam

(2020). Demystifying the aspect of quality in higher education: Insights from Saudi Arabia. Sage Open, 10(1), 1–8.

10.

Arnaud

(2010). Conceptualizing and measuring ethical work climate development and validation of the ethical climate index. Business & Society, 49(2), 345–358.

11.

Asad

(2020). Human resource practices and employee turnover intentions: Do organizational commitment mediates and social support moderates the relationship? International Journal of Psychosocial Rehabilitation, 24(7), 5685–5695.

12.

Bandura

(2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52(1), 1–26.

13.

Bandura

(2002). Selective moral disengagement in the exercise of moral agency Journal of Moral Education, 31(2), 101–119.

14.

Bartels

D. M.

(2008). Principled moral sentiment and the flexibility of moral judgment and decision making. Cognition, 108(2), 381–417.

15.

Black

J. E.

Reynolds

W. M.

(2016). Development, reliability, and validity of the Moral Identity Questionnaire. Personality and Individual Differences, 97, 120–129.

16.

Blewitt

J. C.

Blewitt

J. M.

Ryan

(2018). Business forums pave the way to ethical decision making: The mediating role of self-efficacy and awareness of a value-based educational institution. Journal of Business Ethics, 149(1), 235–244.

17.

Brandt

Despins

Wakefield

Fleming

Deroche

Popejoy

(2021). Health care ethics ECHO: Improving ethical response self-efficacy through sensemaking. International Journal of Ethics Education, 6(1), 125–139.

18.

Bussey

Luo

Fitzpatrick

Allison

(2020). Defending victims of cyberbullying: The role of self-efficacy and moral disengagement. Journal of School Psychology, 78(C), 1–12.

19.

Aguirre

G. C.

II Hyman

M. R.

Jones

L. J.

(2020). Dedicated marketing ethics course: Design and test. Marketing Education Review, 30(3), 177–194.

20.

Castilla-Polo

Liceran-Gutierrez

Ruiz-Rodriguez

M. D.

(2022). The adoption of corporate social responsibility active learning methodology with management accounting students. International Journal of Management Education, 20(1), 100613.

21.

Caya

Neto

J. J.

(2018). A bibliometric review about adaptivity. Procedia Computer Science, 130, 1114–1119.

22.

Curren

(2014). Motivational aspects of moral learning and progress. Journal of Moral Education, 43(4), 484–499.

23.

Demin

S. P. a. W.

(2019). Tourism e-commerce enterprises’ marketing ethical behavior and tourist response: The mediating role of online experience. Tourism Guide, 3(05), 47–65.

24.

Dwyer

K. K.

Bingham

S. G.

Carlson

R. E.

Prisbell

Cruz

A. M.

Fus

D. A.

(2004). Communication and connectedness in the classroom: Development of the connected classroom climate inventory. Communication Research Reports, 21(3), 264–272.

25.

Fadi

Hussam

A. H.

Masoud

A.-B. R.

(2023). Cognitive style and fostering of technological adaptation drive e-entrepreneurial of new mature business. International Journal of Innovation Studies, 7(3), 230–243.

26.

Finance. (2021). Douyin Live carried out special governance of vulgar and bad values, and more than 200 anchors were punished. https://baijiahao.baidu.com/s?id=1716567448927541011&wfr=spider&for=pc

27.

Ganesh

Justin

(2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting & Social Change, 173, 121092.

28.

Hanusch

Mellado

Boshoff

Humanes

M. L.

León

S. d.

Pereira

Ramírez

M. M.

Roses

Subervi

Wyss

Yez

(2015). Journalism students’ motivations and expectations of their work in comparative perspective. Journalism & Mass Communication Educator, 70(2), 141–160.

29.

Hennig

B. B.

(2010). Language learning and the self: Exploring Hong Kong students’ motivation for learning German as a third Language from a Foucauldian ethical perspective. University of Hong Kong.

30.

Hirsh

J. B.

J. G.

Galinsky

A. D.

(2018). Moral utility theory: Understanding the motivation to behave (un)ethically. Research in Organizational Behavior, 38, 43–59.

31.

Hwang

Wang

X. Y.

Pasamehmetoglu

(2021). Customer online reviews and hospitality employees’ helping behavior: Moderating roles of self-efficacy and moral identity. International Journal of Contemporary Hospitality Management, 33(5), 1461–1481.

32.

Indartono

(2020). Data on higher education student ethics model. Data in Brief, 28, 104904.

33.

Itani

O. S.

Chonko

Agnihotri

(2022). Salesperson moral identity and value co-creation. European Journal of Marketing, 56(2), 500–531.

34.

Kraut

L. W. a. C. W.

(2014). The Blackwell guide to Aristotle’s Nicomachean ethics. Peking University Press.

35.

Krishnamurthy

Swain

Ramanathan

(2022). Ethical marketing strategies: The unique Nash equilibrium. The Journal of Business & Industrial Marketing, 37(6), 1373–1388.

36.

Zhang

(2008). Revision of adolescent mental resilience scale (HKRA). Psychological and behavioral research, 6(2), 6.

37.

Wang

(2015). Development and measurement of adult online learning self-efficacy scale. Journal of Distance Education, 33(6), 7.

38.

Martin

A. J.

Nejad

Colmar

S. L.

D., Liem

G. A. D.

(2013). Adaptability: How students’ responses to uncertainty and novelty predict their academic and non-academic outcomes. Journal of Educational Psychology, 105(3), 728.

39.

Melnyk

Carrillat

F. A.

Melnyk

(2022). The influence of social norms on consumer behavior: A meta-analysis. Journal of Marketing, 86(3), 98–120.

40.

Mengchen

L. Y. a. Y.

(2021). The ethical construction of Big Data Marketing in the era of Internet New Media. Business Culture, 9, 20–21.

41.

Mohammed

Hussam

A. H.

Fadi

(2023). May the force of text data analysis be with you: Unleashing the power of generative AI for social psychology research. Computers in Human Behavior: Artificial Humans, 1(2), 100006.

42.

Pearce

J. A.

II . (2013). Using social identity theory to predict managers’ emphases on ethical and legal values in judging business issues. Journal of Business Ethics, 112(3), 497–514.

43.

Pizzolato

Dierickx

(2021). Stakeholders’ perspectives on research integrity training practices: A qualitative study. BMC Medical Ethics, 22(1), 67.

44.

Shahid

Akram

N. M.

Junfeng

R. R.

Fizzah

Ishfaq

A. M.

(2022). “How” and “When” CEO duality matter? Case of a developing economy. Sage Open, 12(3).

45.

Shahid

Junfeng

Tauseef

H. S.

Ahmad

S. A.

(2022). Revolution of nuclear energy efficiency, economic complexity, air transportation and industrial improvement on environmental footprint cost: A novel dynamic simulation approach. Nuclear Engineering and Technology, 54(10), 3682–3694.

46.

Shen

(2016). Consumer perception of marketing ethics behavior of online retailers in distance education magazine – Scale development and dimension measurement. Contemporary Finance and Economics, 3, 10.

47.

Shuhan

Ruihui

(2022). The effects of contextual factors, self-efficacy and motivation on learners’ adaptability to blended learning in college English: A structural equation modeling approach. Frontiers in Psychology, 13, 847342.

48.

Tech

(2021). Simba affiliates were given an administrative penalty of 900,000 yuan for fair trade violations. http://finance.sina.com.cn/tech/2021-01-05/doc-iiznezxt0622925.shtml

49.

Wiltshire

A. B.

Bortolotto

M. C.

Morris

(2020). Motivation as ethical self-formation in learning te reo Māori as a second language. Journal of Multilingual and Multicultural Development, 44(3), 185–196.

50.

Zhang Xixi

H. X.

Bingcheng

(2022). A multivariate approach to the downward transfer effect of leadership resilience. Nankai Management Review, 1–25.

How Education Shapes Ethics in Network Marketing: The Role of Identity and Training

Abstract

Keywords

Introduction

Theoretical Background

Social Cognitive Theory

Student Ethics Model

Moral Utility Theory

Hypotheses and Conceptual Model

Effect of Trainee Identity and Training Form

Conceptual Model of Marketing Ethics Training

Overview of Studies

Study 1: Marketing Ethics Reflected in Online Negative Reviews

Data Collection

Data Analysis

Results of Study 1

Study 2: Influence of Network Marketing Training on Ethical Proneness

Questionnaire Design

Modeling

Reliability and Validity Analysis

Correlation Analysis and Multicollinearity Diagnosis

Model Fit

Empirical Results

Study 3: Comparison of Different Groups

Data Collection

Analysis Based on the Ground Theory

Findings

Discussion and Conclusion

Discussion

Conclusion

Theoretical Implication

Practical Implication

Limitations and Future Directions

Footnotes

Declaration of Conflicting Interests

Funding

Ethical Approval

ORCID iD

References