Research background
In today's competitive business-to-business landscape, suppliers are increasingly implementing Key Account Management (KAM) programs to effectively manage their strategic customers These key accounts, which significantly contribute to sales and profits, are prioritized over traditional minor accounts due to their vital role in driving business success (Pardo, 1997).
Key Account Management (KAM) has been received so many interests as most of sales turnover generated by key customers (Shapiro and Moriarty, 1982; Cespedes,
In the high-tech industry, key accounts are crucial for both short-term and long-term success, warranting increased attention from suppliers Investing resources in these strategic customers fosters close, valuable relationships and enhances trust This approach is encapsulated in a strategic framework known as the Key Account Management Program.
Research indicates that key account management programs significantly enhance trust, which in turn fosters long-term commitment between partners Studies by Kumar (1996) and Geyskens et al (1998) demonstrate that establishing key account teams cultivates a high level of trust, resulting in mutually beneficial, enduring relationships.
The high technology industry is characterized by rapid changes and swift technological advancements, leading to the quick obsolescence of products As a result, the value of these products diminishes rapidly over time Success in this dynamic sector demands a strong commitment from businesses to adapt to the continuously evolving environment.
Vietnam's economy is rapidly evolving, presenting significant challenges for businesses regarding commitment in a fast-changing landscape Many Vietnamese companies tend to prioritize short-term gains, often breaking commitments to achieve immediate objectives This issue is particularly prevalent in the high-tech sector, where instability and rapid transformation are common To foster long-term, sustainable growth and keep pace with industry changes, businesses must focus on building trust and commitment through strategic initiatives, such as key account management programs.
In Vietnam's transitional economy, there is a notable scarcity of research on the impact of key account management (KAM) programs on trust and commitment, particularly within the high-tech industry This raises an important question: how do these constructs influence trust and, subsequently, commitment in this sector? Understanding the strength of these relationships is crucial for developing effective long-term cooperation strategies between business partners Therefore, empirical research is essential to explore the effects of KAM programs on trust and commitment, providing valuable insights and practical implications for the industry.
Problem statement
Numerous studies have examined the impact of Key Account Management (KAM) on trust and commitment, particularly in the fast-moving consumer goods sector Research by Willem et al (2004) highlights the significance of KAM programs in fostering these essential business relationships Additionally, insights from Faten Baddar Al-Husan and Ross Brennan contribute to understanding the dynamics of KAM in enhancing trust and commitment among stakeholders.
A study conducted in 2009 highlighted the significance of strategic account management in emerging economies within the Arab world, emphasizing the positive impacts of key account management programs This research advocates for increased investment in such programs to foster trust and long-term customer commitment However, in transitional economies like Vietnam, there appears to be a lack of research interest in key account management, particularly within the rapidly evolving high-tech industry.
This study explores the impact of key account management programs on trust and commitment from the perspective of Vietnamese dealers in high-tech industries, including telecommunications, electrical appliances, and information technology.
Research questions
This research aims at answering the following questions:
- W at’s t e elatio s ip betwee ke acco t ma a eme t p o ams’ facto s on trust and consequently on commitment?
- What factors should be deserved for investment in order to build long-term win-win business cooperation?
Research objectives
This research aims to analyze the impact of key account management programs on commitment through the lens of trust These programs encompass elements such as responsiveness, information sharing, logistics, and customized promotions Consequently, the study focuses on exploring the relationships between these components and their influence on fostering commitment.
1 The relationship between responsiveness and trust
2 The relationship between information and trust
3 The relationship between logistics and trust
4 The relationship between tailor-made promotions and trust; and
This research will investigate such relationships from Vietnamese dealer perspective Consequently, findings will show some facts, and then practical implications are proposed.
Research Methodology and Scope
This study aims to survey eight key accounts in the high-tech industry across all provinces of Vietnam, where selected dealers operate offices and branches The research will target approximately 210 respondents to gather comprehensive insights.
The research consists of two stages: a pilot study and a main study Initially, a qualitative approach was employed to assess the suitability of the measurement scales for the constructs Necessary adjustments were then made based on the findings.
In the second stage of the research, a quantitative approach was utilized, involving interviews with dealers to gather data for analysis This phase aimed to assess the reliability of the measurement scales through Cronbach's alpha coefficient and Exploratory Factor Analysis (EFA) Additionally, Multiple Linear Regression (MLR) analysis was conducted to evaluate the research model and hypotheses, with data analysis performed using SPSS software version 16.
Structure of the research
The structure of this research consists of five chapters:
This chapter presents research background of the study, as well as, research problems, research objectives, research methodology and scope
In this chapter, literature review has been summarized This chapter would present a research model of the research
This chapter outlines the research methodology, data collection, research design, and research process, building on the objectives and scope established in Chapter 1, as well as the literature review and empirical model discussed in Chapter 2.
Chapter 4: Data Analysis and Results
This chapter outlines the characteristics of research samples and measures the concepts under investigation Descriptive statistics were employed to examine the features of explanatory variables and to analyze the relationships between these variables.
This chapter presents findings of the study, practical implications, contribution of the research Some limitations of the research will be mentioned and directions are recommended for future research.
Introduction
This chapter presents a literature review on key account management, trust, and commitment, examining the relationships among these constructs through the lens of previous studies and relevant theories The objective is to clearly define each construct, propose a research model, and formulate hypotheses for testing from the perspective of high-tech dealers in Vietnam.
In today's business landscape, Key Account Management (KAM) has gained significant importance, prompting a variety of scholarly literature to explore its different aspects While empirical studies have been conducted, there is a strong call for further research to investigate KAM in diverse contexts The relationship between trust and commitment has been a focal point in marketing literature, with Geykens et al (1998) highlighting that trust serves as a crucial mediator between the factors leading to commitment and the commitment itself, as noted by Morgan and Hunt (1994).
This research aims to explore the relationships between Key Account Management (KAM) factors, trust, and commitment within business dyads, particularly in the high-tech industry The subsequent sections will review the characteristics of the high-tech sector and their implications, followed by an analysis of key account management programs, trust, and commitment Based on this review, a series of hypotheses will be formulated, leading to conclusions that will inform future testing.
High-technology attributes and their effects
This research centers on the high-tech sector, necessitating the identification of specific attributes that define and differentiate high-tech products from other categories Based on existing literature in high-technology marketing, five distinct attributes of high-tech products have been identified.
Several attributes identified in existing studies are interrelated, despite appearing in different texts These five key attributes encompass the primary dimensions discussed in the literature It is essential to clarify these attributes and their effects within the high-tech industry before delving into the literature review of the research model constructs By understanding these attributes and their implications, clearer hypotheses can be formulated The following section presents these five high-tech attributes and their associated effects.
High-tech products experience shorter life cycles compared to traditional products, leading to rapid sales growth followed by a swift decline (Rosenau, 1988) This phenomenon reflects ongoing technological advancements, resulting in consistently brief life cycles for specific product lines.
The high-tech industry faces a greater risk of discontinuous change in product technology, which can lead to a complete transformation in consumption and customer profiles Such changes can render existing business competencies obsolete, as current knowledge may not suffice to develop new market products Weiss and Heide (1993) highlighted the unpredictability associated with high-tech attributes, emphasizing that discontinuous innovation in this sector can result in rapid obsolescence of products.
The success of a high-tech product launch heavily relies on robust supporting infrastructure, which must adapt to the rapid evolution of technology Research by MacInnis and Heslop (1990) and Moriatry and Kosnik (1989) highlights the importance of a well-established service network as a crucial element of the marketing mix for high-tech products For instance, a mobile phone cannot be effectively marketed without a reliable mobile network in place Essential components of this infrastructure include complementary products that enhance the functionality of the high-tech device, as well as vital supplies, spare parts, and skilled service personnel for installation and repairs Without compatible and readily available complementary products, a high-tech product risks market failure and may struggle to gain acceptance among consumers.
The rapid evolution of high-tech products to meet changing consumer demands highlights the lack of well-established industry standards As products strive to fulfill similar needs while adhering to varying standards, the process of establishing a unified standard can be lengthy and unpredictable A product may currently dominate the market, but the introduction of a revolutionary design by a newcomer can disrupt this status quo and shift market leadership According to Utterback (1994), this dynamic illustrates the challenges faced in standardizing high-tech innovations.
A dominant design secures market loyalty and sets the standard that competitors and innovators must follow to achieve significant market share.
Take the example of Apple with the re-invention of Iphone, it has defeated to some extent the long dominance of Nokia in the mobile phone market
High-tech products often present significant uncertainty regarding their functionality, as highlighted by Moriarty and Kosnik (1989), who refer to this as "market-related certainty." End-users frequently struggle to align their needs with the benefits offered by these products Additionally, switching from one product to another can incur substantial costs A survey by O2 (2012) revealed that while smartphones were originally designed for making calls, their primary functions have shifted to internet browsing, social media, music, and gaming This evolution, along with the difficulty in tracking market changes, contributes to the heightened uncertainty surrounding the functionality of high-tech products.
2.2.2 Effects of high-tech attributes:
Shorter product life cycles significantly impact consumer segments at each stage, necessitating firms to adapt their marketing strategies to align with evolving customer profiles on the innovation curve As technology advances, consumers seek new criteria for product evaluation, prompting dealers and retailers to frequently adjust their orientations This shift leads to declining prices, affecting marketing costs and profit margins within the supply chain Consequently, stronger cooperation and trust between suppliers and retailers become essential to optimize business performance for both parties.
Effects due to the greater risk of discontinuous change in product technology
High-tech products often experience rapid and unpredictable changes, leading to fluctuations in consumer behavior, user demographics, complementary products, and demand curves (Robertson, 1971) To remain competitive, dealers must enhance their expertise in managing supplier relationships; failing to do so risks obsolescence in a fast-evolving market.
Furthermore, suppliers will risk losing ma ket’s co fide ce If close coope atio a d trust are not existed, all above bad effects will cause troubles for both sides
Effects due to the non-existence of industry standards Moriatry and Kosnik
In 1989, it was highlighted that suppliers and distributors face challenges in persuading customers when industrial standards are lacking, leading customers to invest more time and effort in their search processes To address this, firms must allocate additional resources towards customer education Furthermore, the purchasing behavior of high-tech products involves a complex process of information seeking and analysis at each stage, necessitating that dealers acquire and enhance their knowledge to effectively serve their customers Establishing a high level of trust and cooperation is essential for achieving success in this competitive landscape.
The development of supporting infrastructure is crucial for the successful adoption of high-tech products Dealers who maintain close relationships with customers are better positioned to understand the state of infrastructure in the market Additionally, effective installation and servicing are vital components of high-tech product offerings Suppliers that invest in well-trained service teams and foster strong collaborations with dealers significantly enhance the marketing success of their high-tech products.
In the high-tech industry, customers are increasingly concerned about product functionality, installation, and maintenance due to rapid technological advancements and obsolescence The high switching costs associated with these products further heighten this uncertainty To build customer confidence, marketing strategies should prioritize relationship-building over mere product sales, as emphasized by McKenna (1991) This approach is most effective when suppliers and dealers collaborate as a united organization, fostering mutual trust within the customer-supplier relationship.
Trust
Trust was defined as honesty or credibility (Geyskens et al., 1998) In the channel literature, trust was defined:
The firm believes that another company will engage in actions leading to positive outcomes for them while avoiding unexpected actions that could result in negative consequences.
Butler (1991) identified ten dimensions of trust, which include integrity, consistency, promise-fulfillment, receptivity, loyalty, fairness, competence, discretion, openness, and availability This research focuses specifically on the ethical treatment of suppliers, emphasizing trust in terms of integrity and promise-fulfillment from Butler’s framework.
Trust manifests in two primary forms: cognitive and affect-based Cognitive trust is grounded in reliable role performance, shared cultural or ethnic backgrounds, and professional qualifications In contrast, affect-based trust is influenced by the frequency of interactions and the demonstration of citizenship behavior (McAllister, 1995, as cited in Robert, 2002).
Key Account Management (KAM) programs
To adapt to changes in the business environment, B2B suppliers have implemented Key Account Management (KAM) programs These initiatives focus on understanding and responding to the specific behaviors and demands of their most significant customers, who hold strategic importance for the suppliers' businesses.
Key Account Management (KAM) literature highlights two primary aspects: the formation of small working groups dedicated to serving important customer accounts, known as selling teams, and the management of KAM programs that enhance buyer/seller relationships Research on selling teams primarily investigates the traits that differentiate successful teams from their less effective counterparts In contrast, the management aspect emphasizes the improvement of dyadic business relationships and the overall effectiveness of KAM initiatives.
KAM programs, partially based on the model by Willem et al (2004), include both personal and impersonal factors that influence dyadic relationships While personal factors involve responsiveness, the aspect of value similarity is deemed less significant in the context of high-tech KAM programs in Vietnam and will be explored in future research On the other hand, impersonal factors encompass three key elements: information, logistics, and customized promotions.
Responsiveness measures how effectively suppliers address dealers' issues within an acceptable timeframe It reflects the willingness and patience of suppliers in handling dealers' complaints.
Response time is recognized as a key competitive advantage, emphasizing the need to deliver the right products at the appropriate quality and price within minimal lead times (Stalk, 1988) Achieving this responsiveness requires specific market flexibility, which is defined by the Oxford Dictionary as the "ability to adapt." In management literature, flexibility is seen as a response to both internal and external uncertainties (Gerwin, 1993).
Literature suggests that suppliers who can respond quickly to changes in customer demands will significantly improve customer satisfaction (Robert et al.,
In the fast-paced high-tech industry, enhancing trust through improved relationship quality and loyalty is crucial for increasing customer satisfaction In this dynamic environment, responsiveness becomes the key factor that outshines other aspects, significantly impacting overall success.
H1: Responsiveness has a positive effect on Trust
Effective information exchange between suppliers and buyers is crucial, encompassing various aspects such as inventory levels, planning strategies, production capacity, quality standards, logistics, and insights on new products (Mohr and Spekman, 1994).
Sharing of information refers to “s a i of mea i f l a d timel i fo matio betwee fi ms” (A de so a d a s, 1990, p 44) I fo matio s a i is truly essential for tightening the dyad relationship (Mohr and Nevin, 1990)
Effective information sharing enhances the buyer-supplier relationship by ensuring that vital information flows seamlessly between all parties involved This leads to a stronger partnership, characterized by improved communication and collaboration Additionally, timely and accurate execution of new information fosters trust and efficiency in the supply chain, ultimately benefiting both buyers and suppliers (Zhou and Benton, 2007).
Narayanan and Raman (2004) discovered that suppliers sometimes conceal information for their own advantage, ultimately leading to failures in the entire supply chain To foster long-term cooperation, it is essential that information sharing is conducted with integrity, as this builds trust between partners.
Information plays a critical role in business relationship (Cannon and Homburg,
Information support from suppliers enhances the value of dyadic relationships by facilitating a smooth flow of information to customers This allows customers to anticipate suppliers' strategic moves and potential industry changes In the fast-paced high-tech sector, the rapid exchange of accurate and continuous information strengthens mutual relationships and builds trust.
H2: Information has a positive effect on Trust
Logistical considerations appear in orders taking and fulfilling Anderson and Weitz
(1992) ed s pplie s to make eav i vestme t i lo istics so called “idios c atic i vestme t”
Negri (1997) emphasized the importance of evaluating logistics quality alongside logistics effectiveness and customer orientation This evaluation involves four key processes: analysis, which interprets customer demand and supplier capacity; planning, which assesses customer expectations and perceptions of quality; production, which focuses on defining, designing, and managing logistics components; and control, which evaluates logistics performance and quality levels.
Logistics quality should be tailored to the service factors of suppliers to meet customer requirements Key elements include service policy, delivery systems, personnel, and internal organization, all of which are essential for maintaining fundamental operations and fostering long-term supplier-customer relationships.
Logistics operations play a crucial role in determining delivery performance, which significantly impacts the value of dyadic relationships (Tho, 2011) According to Ulaga and Eggert (2006), delivery performance encompasses three key aspects: timeliness, flexibility, and accuracy (as cited in Tho, 2011).
Logistics, therefore, plays an important role in building trust and commitment between the dyad on a long-term basis Consequently,
H3: Logistics has a positive effect on Trust
Tailor-made promotions are customized marketing strategies designed specifically for dealers, providing them with a unique advantage over competing programs The promotional mix includes various elements such as advertising, direct marketing, personal selling, sales promotions, and public relations (Kotler, 2000: 551) In contrast to business-to-consumer approaches, these promotional activities are specifically crafted for the B2B context, ensuring that dealers can effectively differentiate themselves in the marketplace.
In the high-tech industry, promotional activities must be customized for each key account to ensure effective marketing campaigns by suppliers Key accounts are vital to a supplier's sales strategy, distinguishing them from traditional customers, which makes mass promotional approaches unsuitable Tailored marketing efforts are essential for maximizing engagement and driving sales with these important clients.
H4: Tailor-made Promotions have a positive effect on Trust.
Commitment
Commitment refers to the adoption of a long-term orientation towards relationships, emphasizing the importance of investing in these connections rather than merely assessing immediate costs and benefits It embodies the readiness to make short-term sacrifices in order to achieve greater long-term rewards.
Buchanen (1974) suggests that commitment comprises subjective facets of elatio s ips “apa t f om p el i st me tal wo t ” Fo i sta ce, commitme t ma consist of affective bonds and felt obligations
Trust and commitment are essential for fostering strong business relationships, leading to positive outcomes such as improved partner performance and cooperative behaviors These positive attitudes include mutual satisfaction and a willingness to collaborate, as highlighted by Morgan and Hunt (1994) and Mohr and Spekman (1994) Enhanced cooperation, increased risk-taking, and reduced opportunistic behavior are indicative of positive partner behaviors, as noted by Ghoshal and Moran (1996) Furthermore, trust and commitment have been shown to improve overall performance by reducing uncertainty and enhancing efficiency, ultimately benefiting economic performance (Anderson and Narus, 1990; Morgan and Hunt, 1994) In the high-tech industry, characterized by its unique challenges, high levels of trust and commitment are crucial and interconnected.
H5: Trust has a positive effect on Commitment
Conceptual Model
A conceptual model, adapted from the work of Willem et al (2004), illustrates the relationships among the factors influencing Key Account Management (KAM) programs, trust, and commitment, as depicted in Figure 2.1.
Summary of hypotheses
In reviewing of the literature, hypotheses are summarized as follows:
The effects of Key Accounts Management Programs factors on Trust:
H1: Responsiveness has a positive effect on Trust
H2: Information has a positive effect on Trust
H3: Logistics has a positive effect on Trust
H4: Tailor-made Promotions have a positive effect on Trust
The effect of Trust on commitment:
H5: Trust has a positive effect on Commitment
Introduction
In this chapter, we will introduce the research methodology, which encompasses the research design, measurement scales for constructs, sampling methods, and the statistical tools utilized for data analysis and hypothesis testing, building upon the theoretical framework discussed in the previous chapter.
Research design
The research will employ a descriptive method to measure its constructs, utilizing a survey for data collection and analysis The study's process is illustrated in Figure 3.1.
The study was conducted in two phases: a pilot survey followed by a main survey Participants included purchasing managers, shop floor managers, and owners, focusing on product categories such as mobile phones, information technology, and electrical appliances.
Measurement
The survey was designed based on the measurement scales from Willem et al (2004) and is divided into three sections The first section contains two questions about the specific brand and supplier the respondents interacted with, ensuring accurate responses throughout the survey The second section includes 37 items that assess constructs such as Responsiveness, Information, Logistics, and Tailor-made services, using a scale from 1 (strongly disagree) to 7 (strongly agree) Lastly, the third section comprises four questions aimed at collecting personal information from the respondents.
Figure 3.1: The process of the study
(Develop hypotheses and research model)
C o bac ’s Alp a + EFA + Re essio
Check the consistency of measurement scale meaning
Testing research model and hypotheses
The independent variable, often referred to as predictors, significantly influences the dependent variable, either positively or negatively In this study, the focus is on key account management programs, which include essential components such as Responsiveness, Information, Logistics, and Tailor-made Promotions.
Respo sive ess is t e exte t to w ic s pplie s deal wit deale ’s p oblems timel a d effectively Responsiveness was measured by 6 criteria introduced by Willem et al (2004):
It is our belief that the service people of firm x…
R2 respond politely to our complaints;
R3 are sincerely interested in our problems;
R4 undertake accurate actions to solve our problems
R5 undertake actions to solve our problems in time; and
R6 respond quickly to our complaints
Information was evaluated based on 8 aspects, this measurement inherited from Willem et al (2004):
We receive from the account manager of firm x…
I1 information about their promotional campaigns for their products;
I2 information about the pricing of their products;
I3 information about their product innovations;
I4 information about their new product introductions;
I5 announcement of plans for trade marketing activities;
I6 announcement of plans for consumer marketing activities;
I7 confirmation about their planned marketing activities; and
I8 samples of in-store promotion materials.
Logistics reflects how suppliers take and fulfill orders, the performance of delivery In this study, logistics was measure by 5 items, taken from Willem et al (2004):
L1 their products at the agreed time;
L2 exactly what we have ordered;
L3 the orders without much unnecessary delay;
L4 the orders remain adequate even in the case of rising demand as a consequence of promotional activities; and
L5 enough info matio abo t t e “o de taki ” i case delive p oblems occ
Tailor-made promotions are customized promotional activities designed for dealers, providing a competitive edge by differentiating them from other programs According to Willem et al (2004), the effectiveness of these tailor-made promotions can be assessed through seven specific criteria.
Table 3.4: Tailor-made promotions Scale
Tailor-made promotions (Cronbach’s alpha = 889)
Concerning their promotional activities, firm x has
P1 displays that are easy to set up;
P2 promotional activities that fit the holidays (special days, events);
P3 promotional activities that suit the different seasons;
P4 promotional activities that suit actualities
P5 tailor-made promotions for us;
P6 in-store marketing activities supported by significant advertising campaigns
This study examines trust as a dependent variable that mediates the relationship between the sub-dimensions of key account management programs and commitment Trust influences both the effectiveness of key account management initiatives and the level of commitment The measurement of trust in this research is based on five items established by Willem et al (2004).
Table 3.5: Tailor-made promotions Scale
T3 treats the information provided by us with integrity;
T5 based upon earlier businesses, we can say that our company has much trust in firm x
Commitment is defined as the adoption of a long-term orientation towards a relationship (Anderson and Weitz, 1992, p 19) This concept was measured using a six-item scale, which was derived from Willem et al (2004) and partially adapted from Anderson and Weitz (1992).
C1 we are loyal to firm x;
C2 we remain patient when firm x makes mistakes that have negative consequences for us;
C3 we expect to keep most of the products from firm x in our assortment;
C4 we expect to adopt new products from firm x;
C5 our relationship with firm x is from our perspective a mutual long-term commitment; and
C6 we are dedicated to firm x.
Questionnaire translation
The survey questions were translated into Vietnamese by a committee consisting of two translators, the author and his sister, an English teacher This collaborative approach aimed to minimize translation discrepancies that could arise from using an independent translator Following a thorough review of each translation, the team engaged in a group discussion to ensure a consensus on the meaning of the survey questions Ultimately, a draft of the Vietnamese questionnaire was created for a pilot study.
The questionnaire was distributed to respondents through an online survey conducted via Google Docs This method involved creating a virtual webpage for the questionnaire, with links sent to participants through email or other internet communication channels.
The final questionnaire is shown in the Appendix IA.
Pilot study
This phase aimed to enhance the clarity of the survey questionnaire, ultimately improving the quality of data collected from respondents It consists of two key steps: qualitative and quantitative analysis.
An exploratory study was conducted to evaluate the measurement scales used in the research This involved in-depth discussions with three purchasing managers from key accounts in the mobile phone sector to clarify the survey questions and refine the measurement tools This crucial step ensured that the constructs were appropriately adjusted to fit the context of the study.
In the second step, a preliminary collection of 50 samples was conducted to evaluate the reliability of the variables and refine the measures To assess the scales, both Cronbach’s alpha reliability and exploratory factor analysis (EFA) were utilized The results indicated that Cronbach’s alpha values for all variables exceeded 70, aligning with Nunnally's standards for acceptable reliability.
In 1978, it was established that a Cronbach's alpha of 70 or greater is desirable for ensuring internal consistency across variables The findings indicated that all scales achieved Cronbach's alpha values exceeding 80, confirming that they met the necessary reliability standards.
The results of the quantitative pilot study is shown in the Appendix IB
Main study
The study utilized an electronic survey method, distributing questionnaires through email as online survey links via Google Docs to respondents working at selected dealers’ offices and branches across all provinces of Vietnam The primary aim of this survey was to validate the measurement instruments and assess the research model.
In the study, items with a low item-total correlation (below 0.3) were eliminated through reliability analysis, following Nunnally's (1978) guidelines Only items with a Cronbach's alpha coefficient of 0.7 or higher were retained Subsequently, exploratory factor analysis (EFA) was conducted, leading to the removal of items with loading factors below 0.4 Finally, multiple linear regression (MLR) was applied to the remaining items to evaluate the research model and test the hypotheses.
Research sampling
While larger sample sizes are often considered to enhance the quality of a study, they also come with increased costs Thus, it is essential to determine an appropriate sample size that balances the trade-offs, drawing from the experiences of previous research (Ho & Chu, 2005, p 263).
This study utilized Exploratory Factor Analysis (EFA) to evaluate the research model and hypotheses, adhering to the recommended minimum ratio of 5 observations per item (Hair et al., 2006) Consequently, the research aimed to gather approximately 200 to 210 respondent samples.
A study was conducted involving eight key dealers selected through their relationships with ATO, representing significant accounts for various high-tech suppliers These dealers operate in diverse sectors, including mobile phones (e.g., Nokia, Samsung), electronic appliances, and information technology With an extensive selling chain system, they contribute substantially to suppliers' sales The dealers, which include Thế Giới Di Động, Vin Thông A, Nguyễn Kim, Viettel, Điện Máy Chợ Lớn, Thiên Hòa, Phước Lập, and FPT Retail, have branches and shops distributed throughout the country.
The study focused on respondents who play a crucial role in the buying decision process, including purchasing managers and business owners primarily based in Ho Chi Minh City, while shop and floor managers were from other provinces in Vietnam The research aimed to evaluate the hypotheses and model from the perspective of dealers with significant influence in the purchasing decisions.
In accordance with the principles of sampling that emphasize lower costs, accurate results, rapid data collection, and accessibility of the population (Donald & Pamela, 2003), a convenient non-probability sampling method was employed This approach was deemed the most feasible given the constraints of time and budget Additionally, leveraging industry experience, a curated list of key accounts was utilized to select the most suitable respondents for the survey.
The primary survey was conducted between August 10 and September 12, 2012, by the author and colleagues, who distributed 250 links to the questionnaire via email to targeted respondents Additionally, respondents were encouraged to share the link with their peers at the same professional level To ensure a sufficient response rate, the author followed up with reminders until the desired number of completed questionnaires was achieved.
After 32 days of conducting fieldwork, 208 responses were collected These data would be gone through a cleaning process and then used for analysis.
Statistical tools
Data would be cleaned before analyzing Data cleaning was made to prevent any possible mistakes which are probably missing data or unreasonable answers (Nguyen,
In a 2011 study, a questionnaire featuring 44 multiple-choice questions was administered, consisting of 38 quantitative questions and 6 social-demographic qualitative questions The responses were meticulously reviewed to identify any illogical answers that contradicted the overall responses Since all questions were marked as "required," there were no missing answers in the collected data.
To assess the reliability of the measurement scales, Cronbach's Alpha was utilized to determine internal consistency According to Nunally (1978), a scale is considered to have acceptable internal consistency if it achieves a Cronbach's Alpha of 70 or higher.
The reliability of measurements was tested using Cronbach's Alpha, but it is essential to establish their validity as well Two key types of validity are discriminant and convergent validity, which can be assessed through exploratory factor analysis (EFA) EFA is an interdependence technique that examines the relationships between variables without distinguishing between dependent and independent ones This method helps to condense a set of observations into a more meaningful structure, where items with loading factors below 0.5 should be excluded to enhance the analysis.
2011) Items w ic ave loadi facto s’ diffe e ce less t a 30 ( iA iB 3) should also be deleted The eigenvalue was defined to be minimum level at 1
If the exploratory factor analysis (EFA) reveals more factors than those anticipated in the theoretical framework, it is essential to reassess the new factors for reliability Consequently, adjustments to the research model must be made prior to testing the research model and its associated hypotheses.
Multi-linear regression (MLR) was utilized to analyze the impact of key account programs on factors such as trust and commitment Before conducting the regression analysis, the variables were calculated using the mean equation, and the assumptions of MLR were assessed for their suitability.
Conclusion
This chapter outlines the research methodology and introduces the research process, including the testing of measurement scales and data collection for analysis The subsequent chapter will present the research results and findings for discussion.
Introduction
This chapter provides a comprehensive demographic and statistical analysis of the samples, including the reliability testing of measurement scales, Exploratory Factor Analysis, and Multiple Linear Regression analysis to evaluate the hypotheses The analysis will be conducted using Microsoft Excel and SPSS 16.0 software, with detailed explanations and findings addressing the research questions presented throughout the study.
Data cleaning
A total of 208 responses were collected, with a thorough review of the social demographic questions and logical checks performed After assessing a reverse-scored question (question 38), 6 responses were deemed illogical Consequently, the analysis was conducted on the remaining 202 valid samples, with no missing data identified in the dataset.
Description of the qualified respondents
In a study involving 202 qualified samples, 71.78% were male and 28.22% were female respondents The participants were primarily engaged in the mobile phones (38.61%), IT (34.65%), and electrical appliances (26.73%) sectors A significant majority, 68.81%, interacted with distributors, while 31.19% worked directly with brand owners The educational background of respondents showed that 72.28% were college or university graduates, with only 4 owners participating and 59 serving as purchasing managers Additionally, 68.81% of the respondents held positions as shop or floor managers, with 62.9% working in branches and the remainder at the head office For further details on descriptive statistics, please refer to Appendix II.
Table 4.1: Characteristics of the samples
Social-demographic variables (n 2) Frequency Percentage (%) Types of products the respondents deal in
Place of work of the respondents
Position level of the responders at work
Education level of the respondents
Under College - University 44 21.78 College - University 146 72.28
Reliability Test of measurement
According to Nunally (1978), a Cronbach's Alpha of 70 or higher is considered acceptable for assessing internal consistency in a scale The reliability tests conducted revealed that all variables exceeded this threshold, confirming that the measurement scales are reliable.
In particular, responsiveness was measured by 6 items (from R1 to R6) had a
C o bac ’s Alpha of 949 Information was measured by 8 items (from I1 to I8) had a
C o bac ’s Alpha of 891 The C o bac ’s Alpha of Logistics which was measured by
5 items (from L1 to L5) was 883 Tailor-made promotion programs were measured by
7 items (from P1 to P7) was 889 Trust measured by 5 items (from T1 to T5) had a
The reliability of the measurement scales was assessed, revealing a Cronbach's Alpha of 867 for the first scale and 922 for the commitment scale, which comprised six items (C1 to C6) A summary of these reliability coefficients is presented in Table 4.3, with additional details available in Appendix III.
Table 4.2: Summary of Cronbach’s Alpha of measurement scales
Exploratory Factor Analysis
After assessing the reliability of the variables, an Exploratory Factor Analysis (EFA) was conducted to identify and filter the components that explain the correlations among these variables This analysis aimed to reduce the number of variables while ensuring that the new components effectively capture the key characteristics of the original data The extracted components represent distinct facets of the studied constructs and can be utilized as independent variables for subsequent analyses.
In Exploratory Factor Analysis (EFA), two primary extraction methods are utilized: Principal Component Analysis (PCA) and Principal Factors Analysis (PFA) When the objective is to uncover underlying structures, Principal Factors Analysis is preferred, while Principal Component Analysis is recommended for data reduction Despite their different purposes, both methods often produce comparable results (Statsoft, 2008).
This study utilized principal components analysis with Varimax rotation, which proved advantageous for subsequent multiple linear regression (MLR) analysis By employing this method, the number of variables was effectively reduced, facilitating a more streamlined approach for the upcoming regression analysis phase.
The results of the exploratory factor analysis (EFA), presented in Table 4.4, revealed the extraction of five factors, contrary to the four factors anticipated based on the literature review and research model These five factors exhibit eigenvalues greater than one, contributing to a significant total variance explained.
The KMO and Bartlett’s test results indicated that exploratory factor analysis (EFA) was suitable, with a KMO value of 817 (p = 000) However, one item (P4) was found to have a factor loading difference of less than 30 (.572 - 534), leading to its removal from further analysis.
Extraction Method: Principal Component Analysis
Rotation Method: Varimax with Kaiser Normalization
A new factor comprising items I8, I3, and I4 was identified, reflecting key aspects of product development, including product innovations, new product introductions, and samples of in-store promotion materials This factor was retained and designated as Product Information Meanwhile, the other items (I1, I2, I5, I6, and I7) were maintained within the existing factor, which was renamed Marketing and Sales Information.
The reliability analysis of the two new variables confirmed their internal consistency, with both exceeding the acceptable threshold of 70 Specifically, Marketing and Sales Information achieved a Cronbach's Alpha of 901, indicating strong reliability, while Product Information also demonstrated acceptable reliability, ensuring these variables are suitable for further analysis.
The results of the exploratory factor analysis (EFA) revealed new variables with Cronbach's Alpha values, indicating their measurement scale reliability For a detailed overview of the reliability testing results for these new variables, please refer to Appendix IV.
Table 4.4: Summary of Cronbach’s Alphas with 2 new variables
A revised research model could be drawn as the Figure 4.1
Hypotheses then were complemented and listed as follows:
Key Accounts Management factors on Trust:
H1: Responsiveness has a positive effect on Trust
H2a: Marketing and Sales Information has a positive effect on Trust
H2b: Product Information has a positive effect on Trust
H3: Logistics has a positive effect on Trust
H4: Tailor-made promotions has a positive effect on Trust
H5: Trust has a positive effect on Commitment
Analysis of correlations
Before testing the proposed hypotheses and research model, a correlation analysis was conducted between the dependent and independent variables, as well as among the dependent variables Following this, multiple linear regression (MLR) analysis was performed to validate these relationships As indicated in Table 4.5, the analysis revealed anticipated positive correlations between the factors of Key Account Programs and Trust (H1 to H4), as well as a positive relationship between Trust and Commitment.
* Correlation is significant at the 0.05 level (2-tailed)
** Correlation is significant at the 0.01 level (2-tailed)
Test of Hypotheses
4.7.1 Test the appropriateness of model and assumptions for MLR
Before MLR was run to test the hypotheses, the appropriateness of the model and variables and assumptions of MLR must be met
There was one dependent variable (Trust) for the model 1 to run MLR and it was quantitative variable Therefore, the appropriateness of the model and variables was met
The assumptions of Multiple Linear Regression (MLR) were evaluated to ensure the reliability of the results Two key assumptions were examined: the relationships between the dependent variable and independent variables, as well as the presence of heteroskedasticity, and the normal distribution of the residuals.
The findings in Appendix VI.a confirm that the assumption of a linear relationship and constant variance is satisfied Additionally, the Q-Q plot in Appendix VI.b indicates that the residuals are closely aligned with the reference line, further supporting the assumption of normal distribution for the residuals.
4.7.2 The effects of KAM Programs’ factors on trust (H1, H2a, H2b, H3, and H4)
The correlation analysis revealed existing relationships among the variables To validate these relationships, multiple linear regression was utilized to test the hypotheses, employing the ENTER method due to the confirmatory nature of the research.
The study examined five independent variables—Responsiveness, Marketing and Sales Information, Product Information, Logistics, and Tailor-made Promotions—to determine their impact on the dependent variable, Trust The findings, presented in Tables 4.6, 4.7, and 4.8, indicated that multicollinearity was not a concern, as all Variance Inflation Factors (VIF) were below 2.20 Notably, Table 4.6 revealed that these independent variables accounted for 74.3% of the variance in Trust (adjusted R² = 743, F(5, 196) = 117.36, p = 000), suggesting a strong fit between the data and the model.
Table 4.6: Multiple Linear Regression: Model 1 Summary
Std Error of the Estimate
1 866 a 750 743 33938 a Predictors: (Constant), Tailor-made Promotion,
Responsiveness, Product Information, Logistics, Marketing and
Table 4.7: Multiple Linear Regression: Model 1 ANOVA
Model Sum of Squares df Mean Square F Sig
Total 90.165 201 a Predictors: (Constant), Tailor-made Promotion, Responsiveness, Product Information,
Logistics, Marketing and Sales Information b Dependent Variable: Trust
Table 4.8: Multiple Linear Regression: Model 1 Coefficients
Tailor-made Promotion 089 050 090 1.780 077 497 2.011 a Dependent Variable: Trust
The regression analysis revealed significant findings, demonstrating a positive relationship between Responsiveness and Trust (β = 206, p = 000), thereby supporting Hypothesis H1 Additionally, the results affirmed Hypotheses H2a, H2b, and H3, indicating that both Marketing and Sales Information (β = 429, p = 000) and Product Information positively influenced Trust.
=.189, p = 000) and Logistics had a positive relation with Trust (=.144, p = 005)
However, the results did not support the H4, interpreted the fact that there was no statistically significant 1 relationship between Tailor-made Promotions and Trust Figure 4.2 summarized the results of model 1
4.7.3 The effect of Trust on Commitment
Multiple linear regression (MLR) was conducted to examine the relationship between Trust and Commitment (H5), with results detailed in tables 4.9, 4.10, and 4.11 The analysis confirmed that multicollinearity was not an issue, as the Variance Inflation Factor (VIF) remained below 2.20.
The adjusted R² value of 707, with a p-value of 000, indicates that 70.7% of the variance in Commitment is explained by the model These regression results validate hypothesis H4, demonstrating a positive effect of Trust on Commitment.
=.842, p = 000) Figure 4.3 summarized the result of the model 2
Table 4.9: Multiple Linear Regression: Model 2 Summary
Std Error of the Estimate
Table 4.10: Multiple Linear Regression: Model 2 ANOVA
Squares df Mean Square F Sig
Total 103.571 201 a Predictors: (Constant), Trust b Dependent Variable: Commitment
Table 4.11: Multiple Linear Regression: Model 2 Coefficients
Conclusions
This chapter presents the analysis of the study's results, utilizing Exploratory Factor Analysis to eliminate insignificant variables The statistical findings revealed that the initial hypothesis was not supported as anticipated in the literature review Subsequently, new variables were derived from the original variables, such as the Information variable, which provided a more effective explanation of the dependent variable.
Findings of the study
This study aims to explore the impact of Key Account Management Programs on commitment through trust, specifically from the perspective of Vietnamese dealers in the high-tech industry The analysis yielded surprising results, as not all anticipated hypotheses were validated; however, there were logical explanations for these outcomes.
The initial research question explores the relationships between Key Account Management Programs, encompassing four sub-dimensions, and Trust, as well as the subsequent impact of Trust on Commitment The findings reveal that certain aspects of Key Account Management Programs significantly enhance Trust, aligning with the conclusions of Willem et al (2004), who also identified a positive correlation between these programs and Trust.
(included its sub-dimensions) had a positive effect on Trust in the field of FMCG.
Specifically, Marketing and Sales Information has the strongest effect on Trust ( =.429, p = 000), Responsiveness stands the second on the strength of the effect on Trust ( =.206, p = 000), then Product Information ( =.189, p = 000) and finally
Logistics has a modest positive effect on Trust (β = 0.144, p = 0.005), while Tailor-made Promotions, expected to significantly impact Trust, showed no significant relationship In the high-tech industry, characterized by short product life cycles, the ability to quickly respond to market changes is crucial, making Information vital in supplier-dealer relationships Suppliers who fail to respond actively risk losing business, emphasizing the importance of Marketing and Sales Information, as well as continuously updated Product Information As technology evolves, consumers seek new evaluation criteria, necessitating greater cooperation between parties Responsiveness significantly influences Trust, reflecting how suppliers address issues and complaints The study confirms that Logistics, encompassing service policies, delivery systems, and personnel, is closely linked to Trust and fosters long-term supplier-customer relationships Despite expectations, Tailor-made Promotions were not significantly related to Trust; in the high-tech sector, such promotions are often viewed as standard offerings rather than unique contributions to Trust Additionally, the research reaffirms that Trust has a strong positive effect on Commitment (β = 0.842, p = 0.000), aligning with findings by Jhih-Ming Lai et al.
(2009) who reported that partner trust positively influences partner commitment
The second research question examined the strength of the effects of Key Account Management programs' sub-dimensions According to the results presented in Table 5.1, Marketing and Sales Information has a strong impact on Trust, while Responsiveness, Product Information, and Logistics show comparatively lower effects on Trust.
Table 5.1: The effects of sub-dimensions of Key Account Programs on Trust
The findings indicate that Marketing and Sales Information significantly impacts dealers' Trust, which in turn enhances their Commitment Suppliers are advised to prioritize this aspect by allocating more resources to improve personal interactions between their service personnel and dealers, fostering stronger relationships that promote trust and long-term commitment, as suggested by Tho (2011) Additionally, Responsiveness should be a key focus for resource allocation due to its substantial influence on Trust, while other factors like Logistics and Product Information also warrant consideration However, the analysis advises against heavy investment in Tailor-made Promotions, as they do not significantly affect Trust.
Practical implications
This study emphasizes the significance of Key Account Management Programs and their sub-dimensions, focusing on how suppliers can effectively allocate limited resources to foster trust and commitment within high-tech industry partnerships The findings offer valuable practical insights for managers seeking to enhance their relationships with key accounts.
This study highlights the significant role of trust in fostering commitment within the dynamic high-tech industry To secure long-term, mutually beneficial business partnerships and minimize transaction defaults, managers must prioritize understanding their partners' trust levels through cooperative behaviors.
Effective Key Account Management Programs are crucial for success in the high-tech industry, as they require proper investment in various sub-dimensions This study highlights the importance of elements such as Marketing and Sales Information and Responsiveness in enhancing these programs.
Effective management of Product Information and Logistics significantly enhances Trust, prompting managers to prioritize resources in these areas The study highlights that Marketing and Sales Information, Responsiveness, and Product Information should receive the largest share of resource allocation, primarily driven by the sales force This necessitates managers to focus on enhancing the competencies of their sales teams to foster trust and build relationships through personal interactions While Logistics also plays a crucial role in building Trust, investment in Tailor-made Promotions is discouraged, as it does not substantially contribute to Trust between parties This finding contrasts with previous research by Willem et al (2004) in the FMCG sector, where Tailor-made Promotions were seen as beneficial, underscoring the differing dynamics between industries, with FMCG relying more heavily on promotions compared to the high-tech sector.
Contributions of the study
The study explores and validates the connections between sub-dimensions of Key Account Management Programs and Trust, as well as the relationship between Trust and Commitment This research enhances the existing literature on Key Account Management Programs, Trust, and Commitment, with findings that align with prior studies regarding these relationships.
Building on Willem et al (2004), this study addresses the need for broader research by examining a diverse range of brands within the high-tech industry, focusing on eight key accounts The findings contribute valuable insights to existing research challenges in this field.
Limitations and recommendations for future research
This study has some limitations and encourages attentions for further research in the future
The study was limited to just 8 key accounts within the high-tech industry, indicating a need for further investigation across a broader range of dealers and industries Future research should aim to validate the model by incorporating a more diverse sample to uncover varying perspectives.
This confirmatory study aims to test hypotheses and validate the proposed model Utilizing exploratory factor analysis and multiple linear regression techniques, the research highlights the need for further investigation using confirmatory factor analysis to explore the reverse effects of the research constructs.
The study primarily involved shop managers, who typically have a limited influence in the purchasing decision-making process In contrast, purchasing managers and owners, who possess greater authority, represented a smaller percentage of respondents Future research is recommended to include more participants with a "store voice" to provide a more comprehensive understanding of the dynamics involved.
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APPENDIX IA: QUESTIONNAIRE (Vietnamese version)
Quản tr ờ đại h c Kinh tế đ ến hành nghiên c u khoa h c
Nghiên cứu này không chỉ phục vụ cho mục đích cá nhân mà còn đóng góp quan trọng cho khoa học Tất cả các điểm của nghiên cứu đều có giá trị và giúp ích rất nhiều cho sự phát triển của lĩnh vực này.
2 Công ty A yế ế ế đ y y đ ấ đ ể đ ấ đ đ 4: phân vân ấ ấ đ đ đ y * 1 2 3 4 5 6 7 y * 1 2 3 4 5 6 7 y đế * 1 2 3 4 5 6 7 y để ả yế ấ đề * 1 2 3 4 5 6 7 y để ả yế ấ đề * 1 2 3 4 5 6 7 y * 1 2 3 4 5 6 7
23)Công ty X * 1 2 3 4 5 6 7 y ế ế * 1 2 3 4 5 6 7 ề ạ đ yế y ạ đ ạ ạy ả o * 1 2 3 4 5 6 7 26)Công ty X * 1 2 3 4 5 6 7 đ y * 1 2 3 4 5 6 7 đ y ấ đ y * 1 2 3 4 5 6 7
APPENDIX IB: Results of the quantitative pilot study
Summary of Cronbach’s Alpha of initial measurement scales
Kaiser-Meyer-Olkin Measure of Sampling Adequacy .789
Extraction Method: Principal Component Analysis
Rotation Method: Varimax with Kaiser Normalization.