<span class="text_page_counter">Trang 1</span><div class="page_container" data-page="1">

<b>A STUDY ON PRICING STRATEGY AT THIEN AN BAKERY </b>

A dissertation submitted in partial fulfillment of the requirements for the degree of BSc in Business Management

OFF CAMPUS DIVISION

Full Name of Student: Pham Chu Viet Hoang Student Code: 2124768

Module Title: BAM6001 – Dissertation Module Tutor: Dr. David Ratcliffe Supervisor: Dr. Ron Smith

Date of submission: May 1^st, 2023

</div><span class="text_page_counter">Trang 2</span><div class="page_container" data-page="2">

<b>ABSTRACT </b>

This study focuses on the Vietnamese family-owned business, Thien An Bakery, and aims to examine the impact of pricing decision on the bakery's pricing strategy. The purpose is to provide an in-depth understanding of the factors influencing the bakery's pricing decision and to present data-driven recommendations to enhance its pricing strategy within a competitive market. The research employs a mono-quantitative approach, using quantitative data collected through a questionnaire featuring a Likert scale for 23 observed variables.

The data analysis, conducted using Excel and SPSS 20, encompasses descriptive statistics, reliability testing via Cronbach's Alpha analysis, exploratory factor analysis (EFA), correlation analysis, and regression analysis. The findings reveal that government regulations and policies have the most substantial influence on Thien An Bakery's pricing strategy. Based on these insights, the study proposes objective pricing strategies for the bakery to consider, aiming to increase its market share while addressing the opportunities and challenges associated with various pricing strategies.

</div><span class="text_page_counter">Trang 3</span><div class="page_container" data-page="3">

This thesis has not been ever submitted for any degree in any universities or other training institutions.

</div><span class="text_page_counter">Trang 4</span><div class="page_container" data-page="4">

<b> ACKNOWLEDGMENTS </b>

I would like to express my profound gratitude to my supervisor, Dr. Ron Smith. The completion of this study could not have been possible without his expertise, support, and guidance throughout the process.

I am deeply grateful to all the respondents who participated in the survey, generously sharing their time and insights, which have been crucial for the successful completion of this research. Their cooperation and assistance were instrumental in the success of this study.

Thank you all for your support and contributions to this dissertation. Your collective efforts have played an essential role in making this accomplishment possible.Sincere thanks!

</div><span class="text_page_counter">Trang 5</span><div class="page_container" data-page="5">

1) About Thien An Bakery ... 10

2) The area of study ... 10

3) Research’s aims... 11

a) Research’s questions ... 11

b) Research’s method ... 11

c) Research’s objectives ... 11

d) Significant of the study ... 12

4) Justification for the topic ... 12

5) Summary... 12

CHAPTER 2: LITERATURE REVIEW ... 13

1) Bakery Pricing Strategies ... 13

</div><span class="text_page_counter">Trang 6</span><div class="page_container" data-page="6">

5) Research’s time horizone ... 28

6) Research’s techniques and procedures ... 29

7) Research’s methods of analysis ... 30

Exploratory Factors Analysis (EFA) ... 41

2) Pearson’s correlation analysis ... 45

</div><span class="text_page_counter">Trang 7</span><div class="page_container" data-page="7">

REFERENCES ... 54

APPENDIX 1: ... 64

APPENDIX 2: ... 67

APPENDIX 3: ... 70

</div><span class="text_page_counter">Trang 8</span><div class="page_container" data-page="8">

<b> LIST OF TABLES </b>

Table 1: Sample size ... 27

Table 2: Observation variables construction ... 31

Table 3: Descriptive statistics of sample ... 37

Table 4: Cronbach’s Alpha of Internal Factors ... 38

Table 5: Cronbach’s Alpha of Market Factors ... 38

Table 6: Cronbach’s Alpha of External Factors ... 39

Table 7: Cronbach’s Alpha of Government Regulations and Policies ... 40

Table 8: Cronbach’s Alpha of Pricing Strategy ... 40

Table 9: Cronbach’s Alpha results ... 41

Table 10: KMO and Bartlett's Test of Pricing Decision ... 41

Table 11: Total Variance Explained of Pricing Decision ... 42

Table 12: Rotated Component Matrix of Pricing Decision ... 43

Table 13: KMO and Bartlett's Test of Pricing Strategy ... 44

Table 14: Total Variance Explained of Pricing Strategy ... 44

Table 15: Rotated Component Matrix of Pricing Strategy ... 45

Table 16: Pearson correlation analysis results ... 46

Table 17: Model Summary ... 47

Table 18: ANOVA ... 47

Table 19: Regression Coefficients ... 47

Table 20: Conclusion of hypotheses ... 49

</div><span class="text_page_counter">Trang 9</span><div class="page_container" data-page="9">

<b> LIST OF FIGURES </b>

Figure 1: Framework for value-based pricing... 14

Figure 2: Determinants of Retailer Pricing Strategy ... 22

Figure 3: Summary of conceptual framework ... 23

Figure 4: The Research Onion Framework ... 25

</div><span class="text_page_counter">Trang 10</span><div class="page_container" data-page="10">

EFA Exploratory Factor Analysis

SPSS Statistical Package for the Social Sciences

KMO Kaiser-Meyer-Olkin Measure of Sampling Adequacy

</div><span class="text_page_counter">Trang 11</span><div class="page_container" data-page="11">

<b> CHAPTER 1: Introduction </b>

<b>1) About Thien An Bakery: </b>

Setting a commercial price for a product or service is an important aspect of any business. Price setting is the process of recognizing the present and future market trends in an industry to gain an edge by adjusting the business prices accordingly. It has been firmly established that pricing plays a vital role in the growth of a business because it determines a company's revenue and profit. Attracting customers is becoming increasingly difficult when businesses must compete on price with other stores while also maximizing profits. According to the research conducted by Kühn and Riedel (2014), pricing decisions are heavily influenced by a range of factors, including market size, competition, and risk aversion. Similarly, Lein (2010) also agrees that prominent levels of competition and volatile market demand lead to more frequently adjusted prices. However, the research above has primarily focused on a consistent marketplace where businesses grow consistently. This body of theory presents a problem for industries that confront unforeseen circumstances - for example, the Covid-19 pandemic - which cause the whole market to stall and force numerous companies to go bankrupt. The Covid-19 pandemic has further added to the uncertainty, with commodity prices becoming subject to market fluctuations and altering consumer behavior. Hence, businesses must adopt a scientific and comprehensive pricing strategy that considers the cost of goods sold, competition, and customer demand to maximize their profits.

Thien An Bakery is a renowned bakery with a widespread presence in Ho Chi Minh City. Despite the widespread damage caused by the Covid-19 pandemic to small and large businesses in Ho Chi Minh City, the bakery has achieved considerable success over time. However, Thien An Bakery takes extreme caution in managing their operations to remain competitive in a rapidly changing market. The key differentiating factor for Thien An Bakery from other bakeries is its pricing strategy, which determines the prices of its products and how it reacts to market trends and consumer behavior. Consumer preferences have shifted abruptly because of market volatility, posing a challenge for all industries, including Thien An Bakery. Due to this uncertainty, Thien An Bakery must evolve, adapt to consumer preferences, and abandon traditional methods to maintain their position in the market and continue to generate profits.

<b>2) The area of study: </b>

Survey location: Thien An Bakery in Ho Chi Minh City.

</div><span class="text_page_counter">Trang 12</span><div class="page_container" data-page="12">

Survey participants: Employees at the bakery and Thien An Bakery's customers. Survey timeframe: Data collection will take place from January 2023 to March 2023.

<b>3) Research’s aims: </b>

<b>a) Research’s questions: </b>

This study aims to answer the following research questions:

 What factors in pricing decision impact Thien An Bakery's pricing strategy?

 How do these pricing decision factors influence Thien An Bakery's pricing strategy?  What suggestions can be offered to improve Thien An Bakery's pricing strategy in the

To conduct a literature review on Pricing Decision and Pricing Strategy.

To identify the components of Pricing Decision that affect Pricing Strategy of Thien An Bakery.

To measure the impact of the components of Pricing Decision on Pricing Strategy of Thien An Bakery.

To propose recommendations for the future enhancement of Pricing Strategy at Thien An Bakery.

<b>c) Research’s method: </b>

To answer the above questions, this dissertation will use quantitative research methods, including questionnaire surveys, and primary data. This research will be based on previously published literature on pricing strategy, pricing decision, and data provided by Thien An Bakery executives and employees. The study's purpose is to provide insights and recommendations that can help Thien An Bakery optimize its pricing strategy and better serve its customers while also meeting financial goals.

The collected data will be examined using the SPSS 20 (Statistical Package for the Social Sciences version 20) software. This process will include evaluating the scale

</div><span class="text_page_counter">Trang 13</span><div class="page_container" data-page="13">

reliability via Cronbach's Alpha coefficients, conducting exploratory factor analysis, and utilizing regression analysis to assess the suggested hypotheses.

<b>d) Significance of the study: </b>

This study will contribute to enriching the body of knowledge on price valuation by examining and assessing Thien An Bakery's pricing strategies and approaches, in which knowledge is swiftly and constantly evolving. Consequently, this study will assist in filling the current research shortage in this field and provide practical value to organizations that operate in similarly dynamic settings.

<b>4) Justification for the chosen topic: </b>

In the aftermath of the Covid-19 pandemic, Thien An bakery's current operational status is unfavorable. Despite striving to provide customers with the highest quality bakery products, the bakery's growth rate is gradually slowing down. This may be due to the fact that during quarantine, other bakeries have adopted technology that appeals to customers and promoted their brand along with discounts and special offers, resulting in a decline in Thien An bakery's revenue. As a result, Thien An bakery urgently needs an effective pricing strategy to retain and attract new customers through the communication channels that competitors have utilized, while also creating a unique selling point for customers to choose their products over those of competitors.

The author believes that in the Vietnamese bakery industry, there have been limited studies examining the impact and influence of pricing strategies. Consequently, the author has chosen "A study about Pricing Strategy at Thien An Bakery" as the research topic.

<b>5) Summary: </b>

In Chapter 1, the author presents the selected organization for the study and explains the rationale behind choosing the research topic. Additionally, the author outlines the research objectives, questions, subjects, and scope. The subsequent chapter will provide a review of pertinent literature on pricing strategy and the factors influencing it, as examined and selected by the author.

</div><span class="text_page_counter">Trang 14</span><div class="page_container" data-page="14">

<b> CHAPTER 2: LITERATURE REVIEW </b>

Prior to examining the pricing strategy of Thien An Bakery, it is crucial to recognize the factors contributing to price differences among various bakeries. According to Kotler et al. (2021), price represents the monetary amount that customers must pay in order to obtain or use a product, and it is one of the four Ps in the Marketing Mix. Nilda et al. (2020) determined that price plays a significant role in consumers' purchasing decisions for packaged bread. The diverse pricing of goods across different bakeries stems from each bakery's unique pricing strategy, designed to attract customers. Noble and Gruc (1999) discovered that companies employ various pricing strategies based on their cost structure and competitive circumstances.

In highly competitive markets, a well-executed pricing strategy is essential for any business, not just those in the bakery sector. Companies in these markets face fierce price competition, necessitating the adoption of efficient pricing strategies to maintain their competitive edge (Bourdon, 1992; Heide et al., 2008). The exploration of pricing strategies in the bakery industry is an intriguing area of research, covering a complex interplay of various factors such as production expenses, market demand, and competition, etc. Ultimately, the pricing of bakery products is influenced by key factors like production costs, quality, and customer satisfaction.

<b>1) Bakery Pricing Strategies: a) Cost-Plus Pricing: </b>

Cost-plus pricing is important to many businesses (Guilding et al. 2015) because of its simplicity and ease of utilization Hanson and Kalyanam (1994). Cooper et al. (2016) also agreed on the perspective that cost-plus pricing is considered the best approach based on simplicity. Although cost-plus pricing is widely used, it may not be the optimal approach for maximizing profits (Benfield, 1998) and it may have the potential to mislead firms by making them believe that they have enough information to make pricing decisions Nagle et al. (2016). Overall, it is important to consider both the benefits and drawbacks of cost-plus pricing and carefully evaluate whether it is the appropriate pricing method for a given business.

<b>b) Value-Based Pricing: </b>

Value-based pricing is a strategic approach to pricing that takes into account the perceived value of a product or service to the customer, rather than solely focusing on production costs. Hinterhuber (2008) referenced Docters et al. (2004), who described

</div><span class="text_page_counter">Trang 15</span><div class="page_container" data-page="15">

value-based pricing as "one of the most effective pricing methods.". In 2004, Hinterhuber introduced a comprehensive framework for executing a value-based pricing strategy, which factors in all pertinent dimensions and components for making profitable and lasting pricing choices. He argued this approach can contribute to a successful long-term pricing strategy.

Figure 1. Framework for value-based pricing (Hinterhuber, 2004)

Liozu et al. (2012) observed that companies that effectively implement based pricing share common traits, such as the ability to navigate significant transformational change. Meanwhile, Tatyana (2011) posited that value-based pricing can offer a competitive edge to businesses. Collectively, these studies indicate that value-based pricing is a crucial strategy for achieving profitable and sustainable pricing outcomes. However, it should not serve as the exclusive foundation for pricing decision, as all relevant factors must be carefully weighed.

<b>value-c) Dynamic Pricing: </b>

Dynamic pricing is a versatile pricing strategy that sets different prices according to varying demand levels. Kotler et al. (2021) described dynamic pricing as a flexible approach that continually adjusts prices to accommodate the specific characteristics and requirements of individual customers and situations. This enables bakeries to modify their prices in response to fluctuations in demand or production expenses, ensuring competitiveness and profitability.

</div><span class="text_page_counter">Trang 16</span><div class="page_container" data-page="16">

Transchel and Minner (2009) illustrated that dynamic pricing can enhance operational efficiency and profitability within the economy. Liu and Zhang (2013) discovered that this pricing strategy can yield higher profits for firms offering high-quality products, while Chen et al. (2018) indicated that it can boost profits for products with a limited shelf life. Zhao and Zheng (2000) determined that optimal pricing for perishable goods declines as inventory decreases and that implementing dynamic pricing can lead to revenue increases of up to 7.3 percent. However, one drawback of this pricing approach is that it may prove difficult to execute for bakeries with limited resources.

<b>d) Competition-based pricing: </b>

Competition-based pricing is a strategy that involves setting product prices based on competitor pricing. Companies adopting this approach often reduce their prices below those of their competitors to attract customers and gain market share (Chen et al., 2018). Matsa (2011) discovered that competition-based pricing strategies can stimulate firms' competitiveness by encouraging investments in product quality, which may be advantageous for a bakery seeking to distinguish itself from rivals (Moorthy, 1988).

However, Klemperer (1995) points out that firms with brand loyalty may opt for higher prices to capitalize on their committed customers, which could be a disadvantage for a bakery aiming to attract new clientele. Conversely, Griffith and Rust (1997) caution that managers may tend to engage in excessive competition, resulting in reduced profits when companies' performance falls short of expectations. In summary, competition-based pricing strategies offer both advantages and disadvantages for bakeries, with the success of such an approach depending on factors like product quality, pricing, and differentiation.

<b>2) Factors influencing pricing decision: </b>

Jobber and Shipley (2012) determined that successful high-pricing strategies are influenced by aspects such as customers' ability to pay, brand value, level of competition, and the relationship between demand and supply.

<b>a) Internal factors: Direct Costs: </b>

The direct costs of a bakery encompass expenses related to primary ingredients such as wheat flour, sugar, eggs, and fats or oils (Wilderjans et al., 2013), as well as labor costs for bakers and the operational expenses of equipment like ovens and other baking tools. Therkelsen et al. (2014) and Masanet et al. (2012) offer insights into the considerable energy consumption and possibilities for energy efficiency improvements in

</div><span class="text_page_counter">Trang 17</span><div class="page_container" data-page="17">

the baking area. Taking production costs into account is crucial when making pricing decisions, and businesses must consider these factors when establishing their prices.

<b>Indirect Costs: </b>

Indirect costs, also known as overhead costs, are expenses not directly associated with the production process but essential for the overall functioning of the business. Spiegel et al. (2006) examines the efficacy of food quality management within the bakery industry. Marketing strategies, encompassing advertising, promotion, personal selling, direct digital marketing, and public relations, can positively impact bakery sales (Constantin 2009, Kiumarsi et al., 2014). Furthermore, Kiumarsi et al. (2014) advises that SMEs should focus on improving packaging, increasing product value, and adopting appropriate advertising and promotional strategies to enhance sales. Fusté-Forné and Filimon (2021) studies the utilization of social media by bakeries during the COVID-19 pandemic, finding that social media aided in maintaining customer relationships and promoting products. Vries et al. (2018) investigates the influence of social media campaigns on small and medium-sized food industry enterprises, offering insights into how bakeries can effectively engage with customers through social media.

<b>Fixed Costs: </b>

Fixed costs in a bakery refer to expenses that remain constant, irrespective of production or sales levels, including rent, property taxes, insurance, and depreciation on equipment and buildings. Therkelsen (2014) pinpointed energy efficiency approaches that can lower energy expenses for bakeries, which can be applied to bakeries worldwide. Martin (1998) recommends employing simulation in place of physical validation for new routes, which can help decrease distribution costs.

<b>Variable Costs: </b>

Variable costs in a bakery correspond with production or inventory levels. Although similar in concept to direct costs, variable costs specifically refer to expenses that fluctuate based on production levels, while direct costs are associated with individual products. Chambers et al. (2006) discovered that variable cost functions influence equilibrium product positions, profits, and market coverage.

<b>Regulatory Costs: </b>

Businesses must adhere to government regulations, which can result in extra expenses such as safety standards, environmental rules, and business licensing fees. Chambers et al. (2019) determined that an expansion in regulations leads to increased prices, which disproportionately impacts SMEs. Ollinger and Moore (2009) analyzed the

</div><span class="text_page_counter">Trang 18</span><div class="page_container" data-page="18">

cost of complying with food safety regulations, concluding that mandated tasks represent the costliest aspect of these rules. These findings imply that regulatory costs can raise bakery product prices, with the weight of these expenses falling unevenly on smaller businesses.

<b>Opportunity Costs: </b>

Opportunity costs refer to the potential advantages that might have been gained if resources had been allocated in a different manner, and they play a crucial role in pricing decisions. For instance, Klemperer (1995) explores how companies decide between establishing low prices to seize market share and setting high prices to capitalize on committed customers. This concept is further supported by Sandoval-Chavez and Beruvides (1998), who demonstrate that opportunity losses constitute a considerable portion of the cost of quality in a continuous-process industry. Building on these ideas, Levinthal (2009) underscores the significance of making pricing decisions grounded in the opportunity cost of utilizing resources in one area versus another, emphasizing the interconnected nature of resource allocation and pricing strategy. Northcraft and Neale (1986) contend that opportunity costs are frequently overlooked by decision-makers, resulting in less-than-optimal choices. Meanwhile, Zhao et al. (2021) investigate the consequences of various resource allocation approaches on the formation of strategic alliances, discovering that the stability and success of these allocation patterns hinge on the innovation intentions of the firms and the extent of resource distribution.

<b>b) Market factors: </b>

<b>Customer preferences and willingness: </b>

Dodds et al. (1991) discovered that price positively influenced perceived quality but negatively affected perceived value and willingness to buy. Roy et al. (2014) determined that consumers' purchasing decisions depend on their price expectations, with some individuals being more sensitive to price changes. Albari and Safitri (2018) revealed that fixed and relative prices positively impact consumers' purchasing decisions. Gunadi and Evangelidis (2022) demonstrated that consumers are more likely to postpone purchases when product prices have previously increased, especially for substantial increases.

Nelson (1970) argued that limited consumer information about quality significantly affects their willingness to buy. Both Zeithaml (1988) and Sánchez-Fernández & Iniesta-Bonillo (2007) propose that perceived value is a multi-dimensional concept encompassing cognitive and affective components. Myers et al. (1973) found that income was a more

</div><span class="text_page_counter">Trang 19</span><div class="page_container" data-page="19">

reliable predictor of buying behavior than social class for various products and services, while Myers et al. (1971) determined that income better predicted the products found in homes.

Forman (2007) indicated that a store's physical location influences the benefits of purchasing online versus in a local retail store, with consumers shifting away from online shopping when a local store opens. Léo (2002) asserted that consumer satisfaction plays a critical role in location decisions for commercial malls, with accessibility being a secondary factor. Consumer sensitivity to price changes depends on factors such as the type of good, the magnitude and direction of price changes, and their level of product information.

<b>Competition: </b>

The competitive landscape in the bakery industry features a diverse array of strategies, each designed to attract customers and maintain market share with a unique approach. Anh (2020) indicates that the retail market in Vietnam is experiencing growth, with both domestic and foreign businesses expanding their distribution networks. Trung & Maruyama (2007) and Trung & Maruyama (2012) explore the operations and evolution of domestic modern retailers, as well as the structure and background of multinational competitors. Kopalle et al. (2009) emphasize the importance for retailers to comprehend the competitive effects on pricing in order to achieve profitability, as well as understanding the impact of competition within the industry.

<b>Supply and demand: </b>

Seasonal events and holidays throughout the year can lead to shifts in consumer preferences for bakery products. Trung and Maruyama (2007) found that pricing played a pivotal role in consumers' selection of shopping outlets for seasonal food and beverages. Aviv and Pazgal (2008) revealed that while strategic consumer behavior reduced the benefits of price segmentation for seasonal items, implementing announced pricing policies could be advantageous for sellers. Furthermore, Hirche et al. (2021) identified a consistent influence of public holidays on short-term sales increases across all products.

As consumer preferences change over time, businesses must adapt to meet the evolving demands. Martinez and Gomez (2019) observed a growing desire among indulgent consumers for healthy and sustainable baked goods. Decock and Cappelle (2005) noted an increased appetite for tastier bread and specialty breads from around the world. To address this demand, Kieliszek et al. (2018) and Rahaie et al. (2012) explored the use of nutrient-dense ingredients, such as dietary fibers, phenolic antioxidants, marine ingredients, and n-3 fatty acids, in the production of health-focused bread. Peris et al.

</div><span class="text_page_counter">Trang 20</span><div class="page_container" data-page="20">

(2019) discussed replacing less healthy ingredients with nutritionally superior alternatives, like substituting carbohydrates with legume mucilages or gums, and swapping saturated fats for oils or butters rich in omega-3 fatty acids.

<b>c) External Factors: Economic conditions: </b>

Inflation impacts the price of bakery products through various factors, including exchange rates, commodity prices, and supply chains. Chand (2010) highlights the consequences of food inflation on consumers, emphasizing that rising food prices can result in nutritional deficiencies. Walsh (2011) contends that food prices should not be excluded from inflation measures, as food inflation tends to be more persistent and volatile than non-food inflation. This may lead to decreased demand for bakery products since food prices are more sensitive to inflation than non-food prices. Andreyeva et al. (2010) discovered that food consumed away from home is responsive to price changes due to inflation, resulting in an 8% to 10% reduction in food consumption.

An increase in disposable income also affects food demand, which can potentially influence the pricing decisions of businesses. Guo et al. (2000) observed that a rise in income over time corresponded with a shift in demand for inferior and normal food groups, as well as an increase in the income elasticity for luxury foods. Smith et al. (2009) found that price and income do influence consumer purchases of organic produce to some degree.

During economic recessions, prices become a barrier for consumers who are focused on cutting spending on non-essential goods. Businesses must adapt to the situation by reducing prices to maintain sales. Lădaru et al. (2021) noted that during the COVID-19 pandemic, interest in bakery products significantly increased, possibly due to concerns about food insecurity. Shama (1993) discussed marketing strategies for small and large firms during a recession, such as offering cheaper products and quantity discounts, lowering prices, and increasing promotion efforts.

<b>Sociocultural factors: </b>

The growing awareness of health and fitness has spurred demand for healthier consumer products, such as cereals, low or gluten-free foods, and sugar-free items, which can influence pricing strategies. Dipietro et al. (2016) discovered that health consciousness significantly predicts behavioral intentions and purchase decisions, while the perception of menu information positively affects both behavioral intentions and the perception of food quality. Similarly, Ali and Ali (2020) found that health consciousness is the primary psychological factor impacting the willingness to pay for health and wellness

</div><span class="text_page_counter">Trang 21</span><div class="page_container" data-page="21">

food products.

Furthermore, Alsubhi et al. (2022) determined that consumers aiming to maintain a healthy lifestyle are more likely to pay a price premium for healthier food options. Health consciousness is an essential factor influencing the willingness to pay for healthier products; however, its impact may vary depending on other factors such as advertising content and demographic variables. Consequently, businesses should consider these factors when developing pricing strategies to cater to the evolving health-conscious market.

<b>Technological advancements: </b>

The adoption of e-commerce and online platforms is a crucial transformation that bakeries should consider, as it enables them to reach a broader customer base while offering more competitive prices. Rahman et al. (2022) discovered that consumers perceive social commerce platforms as timesaving and motivating, which can influence their purchasing behavior, while the adoption of social commerce also encourages pastry consumers to buy pastry products via these platforms. Additionally, Jang et al. (2021) determined that mandatory customer participation in mobile apps positively impacts bakery purchase behavior.

Nilda et al. (2020) found that price significantly influences the purchasing decisions of packaged bread, implying that online platforms offering competitive pricing may affect the demand for bakery products. Consequently, embracing e-commerce and online platforms can provide bakeries with new opportunities for growth and competitiveness in the evolving market landscape.

<b>d) Government Regulations and Policies: Food safety standards and regulations: </b>

Mohand et al. (2017) discovered the influence of public food safety regulations on anticipated prices within domestic markets. Hammoudi et al. (2009) observed that both public and private food safety standards can impact a firm's internal organization and strategic behavior. Ravenswaay and Hoehn (1996) emphasized that consumers gain advantages from food safety policies that minimize contamination and exposure to harmful substances. In summary, although food safety regulations can alter food prices, the benefits they provide tend to outweigh the associated costs, as these regulations serve as an effective means of enhancing public health and safety.

<b>Business licensing and fees: </b>

Klapper et al. (2004) discovered that expensive regulations hinder the

</div><span class="text_page_counter">Trang 22</span><div class="page_container" data-page="22">

establishment of new firms, particularly in industries with naturally high entry rates. This may create barriers for new businesses and potentially result in increased prices for consumers. On the other hand, Stel et al. (2007) determined that minimum capital requirements and labor market regulations reduce entrepreneurship rates. Zapletal (2017) found that while occupational licensing does not impact the equilibrium number of practitioners or service prices for consumers, it does decrease both practitioner entry and exit rates.

<b>Taxation and import/export regulations: </b>

Auld (1974) discovered indications that increasing indirect taxes have played a role in raising the prices of consumer goods, emphasizing the importance of tax considerations in pricing decisions. Viner (1923) contends that fluctuations in price levels can make the connection between taxation and prices more complex, suggesting that tax laws should consider changes in the value of the monetary unit to ensure more accurate pricing decisions. Feldman and Ruffle (2015) observed that prices inclusive of taxes result in lower demand compared to prices exclusive of taxes, highlighting the impact of taxation on consumer behavior. Furthermore, Hawkins and Wallace (2006) investigated the link between income sources and demand decisions, revealing that tax exemptions can create substantial income sources, which in turn influence the demand for goods and services, and subsequently affect pricing strategies.

b) ―Managing the influence of internal and external determinants on international

</div><span class="text_page_counter">Trang 23</span><div class="page_container" data-page="23">

industrial pricing strategies" by Howard Forman and James M. Hunt (2005):

The author regards internal factors, external factors, and market factors as the determinants that shape pricing strategy and the decision-making process. Although the primary driving factor was not explicitly mentioned, the author emphasizes the importance of considering external factors, such as international experience, product technology, and demand characteristics, when selecting a pricing strategy.

c) "An Empirical Analysis of Determinants of Retailer Pricing Strategy" by Venkatesh Shankar and Ruth N. Bolton (2004):

The author identifies several variables that influence pricing strategy, including Market Factors, Chain Factors, Store Factors, Category Factors, Customer Factors, Brand Factors, and Competition Factors. These variables are valuable for outlining alternative pricing strategies.

</div><span class="text_page_counter">Trang 24</span><div class="page_container" data-page="24">

Figure 2. Determinants of Pricing Strategy (Shankar and Bolton, 2004)

Specifically, the author finds that Category and Chain factors account for a considerable amount of variation in pricing strategy. However, the study's results indicate that Competition Factors contribute the most to the variance in pricing strategy.

<b>4) Research hypotheses and summary conceptual framework: </b>

As discussed in the previous section, researchers have established a strong connection between the determining factors of pricing decision and pricing strategy. The specific theories mentioned in the text include:

The influence of internal factors on pricing strategy, such as cost structure, product quality, and marketing objectives.

The role of market factors in shaping pricing strategy, which includes customer preferences, competition, and supply and demand.

The impact of external factors on pricing strategy, encompassing economic conditions, sociocultural factors, and technological advancements.

The effect of government regulations and policies on pricing strategy, covering food safety standards, business licensing and fees, and taxation and import/export regulations.

These theories serve as the foundation for the development of the four hypotheses that will be tested in the study:

Hypothesis 1 (H1): Internal Factors have a positive impact on the Pricing Strategy. Hypothesis 2 (H2): Market Factors have a positive impact on the Pricing Strategy. Hypothesis 3 (H3): External Factors have a positive impact on the Pricing Strategy.

Hypothesis 4 (H4): Government Regulations and Policies have a positive impact on the Pricing Strategy.

</div><span class="text_page_counter">Trang 25</span><div class="page_container" data-page="25">

Figure 3. Summary of conceptual framework (Source: Author, 2023)

<b>5) Summary: </b>

In Chapter 2, the author comprehensively outlined and summarized the theoretical foundations related to pricing decisions and pricing strategies. Concurrently, the author introduced previous studies on the impact of various factors linked to pricing decision and the connections between these concepts, ultimately guiding the selection and development of a suitable pricing strategy. Additionally, the majority of the studies referenced have established a robust theoretical framework through systematic scientific research. The subsequent chapter will discuss the research methodology and data employed for scale construction, hypothesis testing, and research model evaluation.

</div><span class="text_page_counter">Trang 26</span><div class="page_container" data-page="26">

<b>CHAPTER 3: RESEARCH METHODOLOGY </b>

This chapter centers on the research methodology associated with the study, addressing the location and duration, methods and design, instruments, data collection techniques, and data analysis approaches. This chapter will describe the methods implemented for data collection and analysis in this study. A detailed explanation of the research design and methods will be offered, encompassing aspects such as the study site, sample data collection method design, sample size, data type, and data management procedures. Additionally, the interpretations drawn from this study will be based on the data gathered and analyzed. Data collection for this investigation took place from January 2023 to March 2023. The study progressively adopts a layered approach from internal to external research in order to address the research questions, utilizing Saunders' research framework (Saunders et al., 2012).

Figure 4. The Research Onion Framework (Saunders et al., 2012)

<b>1) Research’s philosophy: </b>

In this study, the positivist research theory assumes that phenomena within the bakery industry can be comprehended and explained using objective, measurable, and

</div><span class="text_page_counter">Trang 27</span><div class="page_container" data-page="27">

quantifiable data. Positivism research philosophy is deemed suitable for this investigation, as it employs quantitative methods to gain a comprehensive understanding of the factors influencing Thien An Bakery's pricing strategies. Park et al. (2019) portray positivism as a research paradigm emphasizing the identification of explanatory connections or causal relationships through quantitative methods. Ryan (2018) notes that positivism values objectivity and the confirmation or refutation of hypotheses, while interpretivism values subjectivity, and critical theory examines the broader oppressive aspects of politics or societal influences.

This research embraces an objectivist ontological standpoint, which assumes the existence of a singular, independent reality that remains unaffected by human perception. This viewpoint aligns with the positivist approach and is apt for examining the relationships between factors that influence pricing strategies in the bakery sector. An empiricist epistemological perspective is adopted in this study, consistent with positivism. The analysis of the impact of pricing strategies on the bakery's financial success relies on quantitative data collected through surveys, financial records, and industry reports. This approach facilitates statistical analysis and the identification of trends, patterns, and correlations between variables. In line with positivist ideology, this research aims to minimize the influence of personal values and biases on the study's methodology and conclusions. To achieve this, the investigation employs a structured and transparent research design, standardized data collection instruments, and rigorous data analysis procedures. The research findings are presented objectively, focusing on facts rather than expressing subjective opinions.

<b>2) Research’s approach: </b>

This study will be a deductive study as it will examine the collected data from surveys, and questionnaires. Bowles (1994) contends that deductive arguments feature premises that definitively support their conclusions. Similarly, Goel and Dolan (2004) define deductive arguments as those where the conclusion logically stems from the premises. A quantitative research approach can be employed to investigate the pricing strategies adopted by Thien An Bakery. This process will encompass collecting historical sales data, such as the average selling prices of different products, scrutinizing pricing tactics like promotions and discounts, and administering surveys and questionnaires to gather customer feedback regarding pricing, perceived value for money, and willingness to pay for bakery products.

This study aims to outline the approach for implementing a valuation strategy, with a primary focus on data obtained from surveys, and questionnaires conducted with Thien

</div><span class="text_page_counter">Trang 28</span><div class="page_container" data-page="28">

An Bakery employees and customers’ responses collected at a specific moment in time.

<b>3) Research’s strategies: </b>

This investigation took place at Thien An Bakery and encompassed various groups, including employees and customers. Feedback from customers visiting the bakery for purchases or services will be gathered for research purposes. Both employees and customers will be given questionnaires to provide their insights. This approach ensures the collection of all pertinent and relevant information.

<b>a) Sampling and Sample Size: </b>

In summary, the interviewees encompass all individuals with an interest in Thien An Bakery. The study consists of approximately 90 participants, including customers, and bakery employees. Setia (2016) outlines two distinct sampling methods: probability and non-probability. Probability sampling relies on random occurrences, whereas non-probability sampling depends on the researcher's discretion and the accessibility and availability of the population under study. While the majority of respondents are customers, it is impractical to expect all of them to complete the entire questionnaire for sampling purposes. As a result, purposeful, non-probability sampling will be employed. Sampling will be conducted through questionnaires targeting customers enjoying Thien An Bakery products on-site and employees during their breaks. This approach also aims to gauge the satisfaction level regarding product pricing from both customers and the bakery's employees and it will be scheduled at their convenience to minimize any disruption to their routine.

Table 1. Sample size (Source: Author, 2023)

<b>b) Type and Sources of Data: </b>

The primary emphasis of this research will be on primary data. Data collection techniques were employed to gather information from participants at Thien An Bakery directly.

Primary Data:

Primary data refers to information specifically collected for research purposes. The

</div><span class="text_page_counter">Trang 29</span><div class="page_container" data-page="29">

researcher utilized self-administered questionnaires for respondents to collect data. Data Collection:

Data collection in this study will be conducted through questionnaires and person surveys carried out by the researchers.

in-Questionnaire:

The questionnaire and surveys are designed for scheduling with a focus on ended questions. Its purpose is to compare the opinions of customers and bakers regarding current product pricing to further analyze the bakery's current pricing strategy. The questionnaire is aimed at addressing the research objectives and answering the research questions.

open-Responses to the questions were provided on a 5-point Likert scale, defined as follows: (1) strongly agree, (2) agree, (3) neutral, (4) disagree, and (5) strongly disagree. The complete questionnaire form and additional information will be present in Appendix 1.

Data Validity and Reliability:

Research validity pertains to the accuracy and trustworthiness of the study outcomes. Sullivan (2011) states that validity in research concerns the precision of measurements, emphasizing that assessment tools must be both reliable and valid to ensure credible results. Prior to actual data collection, modifications are implemented for the purpose of conducting validity checks, which assist in identifying any irrelevant ambiguities or redundancies.

<b>4) Research’s choices: </b>

In this study, a deductive approach will be employed, utilizing a mono-quantitative data collection method as the most appropriate choice. The quantitative approach is widely used in social research, involving variable measurement and statistical analysis to derive conclusions (Watson, 2015; Hammersley, 2012; Jackson et al., 2007). The aim of this quantitative data collection approach is to reveal insights into the reasoning behind Thien An Bakery's existing pricing strategy. This will be achieved by examining customer perceptions of pricing satisfaction, perceived value for money, and their willingness to pay.

Ultimately, the gathered numerical data and percentages will prove valuable for understanding related topics concerning pricing strategies and the decision-making processes behind them.

<b>5) Research’s time horizon: </b>

The timeframe that the study is conducted and assessed within is referred to as the research time horizon. We will use a cross-sectional time horizon in this study to

</div><span class="text_page_counter">Trang 30</span><div class="page_container" data-page="30">

evaluate the success of Thien An Bakery's pricing strategy. Jesson (2001) outlines the primary characteristics of the cross-sectional survey method, emphasizing its main principles and discussing the stages involved in the research process. Data collection is done over a cross-sectional time horizon at a particular moment in time. This method is appropriate for the current study since it enables us to examine the effectiveness and current situation of Thien An Bakery.

For this investigation, a cross-sectional temporal horizon was used since it was practical and comparable. Zangirolami-Raimundo et al. (2018) observe that cross-sectional studies are particularly valuable in descriptive research, but when employed in analytical studies, the findings must be interpreted cautiously and sensibly by researchers with substantial expertise in the relevant field of knowledge. A cross-sectional time horizon enables data collection and analysis to be completed within a reasonable amount of time given the constrained time and resources available for this project. This study can compare Thien An's pricing strategy with that of other Vietnamese bakeries by gathering data at the same time.

<b>6) Research’s techniques and procedures: </b>

Data analysis plays a crucial role in finding solutions to research problems. This study will employ deductive data analysis to examine quantitative questions and answers, utilizing both descriptive and inferential statistics.

Descriptive statistics comprise a set of tools designed to explore, summarize, and depict research data. They offer an overview of the sample data characteristics and convey this information in an accessible manner. Acosta (2021) posits that descriptive statistics are vital for defining the problem, comprehending the data, and communicating results. Descriptive statistics can be employed to numerically and graphically represent data distribution, calculate measures of central tendency, and assess variation (Ramachandran, 1982; Ibe, 2014). To analyze the data, suitable computer software like the Statistical Package for Social Science (SPSS) and Excel will be used. Initially, each questionnaire will be assigned a code (e.g., Questionnaire 1, Questionnaire 2), which will later be abbreviated as Q1, Q2, and so on, depending on the number of questionnaires. Each question and answer will be coded and inputted into the software program. Descriptive statistics, specifically frequencies and crosstabulation, were employed to assist in identifying patterns, trends, and relationships, thus simplifying the researcher's task of comprehending and interpreting the study's implications. Data presentation included tables, pie charts, and bar graphs, which effectively conveyed the findings' interpretation. Supplementary explanatory notes were provided alongside the descriptive

</div><span class="text_page_counter">Trang 31</span><div class="page_container" data-page="31">

data for added clarity.

For quantitative data, preliminary preparations are needed, such as eliminating duplicate or incomplete responses and applying inferential statistics for calculating processing measures. Marshall and Jonker (2011) highlights that inferential statistics are essential for drawing conclusions from a sample and generalizing them to a broader population.

<b>7) Research’s methods of analysis: </b>

<b>a) Descriptive Analysis: </b>

Descriptive analysis will be utilized to summarize and showcase the fundamental characteristics of the gathered data. This process will include determining measures of data, such as customer demographics like age, gender, occupation, and monthly income range; customer preferences, such as the frequency of bakery visits and favorite types of bakery products; and their opinions about the bakery's pricing strategy. Descriptive analysis will offer an overview of the study's main variables, helping to identify patterns and trends in the data that can serve as a foundation for additional analysis.

<b>b) Cronbach’s Alpha coefficients: </b>

Cronbach's Alpha coefficients, a widely used statistical measure, evaluate the extent to which questions on a scale correlate with one another. These values range from 0 to 1, and values above 0.7 are typically considered acceptable, indicating a good consistency among the items. However, it's important to note that values above 0.9 might suggest redundancy, which could necessitate the removal of some items.

Taber (2018) further elaborates on this point, emphasizing that a high alpha value does not inherently guarantee the reliability of a research instrument. In fact, an excessively high value could be counterproductive when developing a test for scientific knowledge. This highlights the importance of carefully considering Cronbach's Alpha coefficients in the context of a study's specific goals and objectives.

In the study, the author evaluates the pricing decision scales, which encompass five observed variables for each independent factor. However, the dependent factor, namely the pricing strategy variable, includes only three observed variables. The survey questions provided in Appendix 1 correspond to the observed variables of these factors.

Internal Factors:

IF 1 Bakery costs affect the prices of Thien An Bakery products.

</div><span class="text_page_counter">Trang 32</span><div class="page_container" data-page="32">

IF 2 The cost of ingredients affects Thien An Bakery's prices.

IF 3 Product quality is important for deciding prices at Thien An Bakery. IF 4 Thien An Bakery's marketing goals affect their prices.

IF 5 Thien An Bakery's reputation influences their prices. Market Factors:

MF 1 Customer preferences impact Thien An Bakery's prices. MF 2 Competition affects Thien An Bakery's pricing decisions.

MF 3 Holidays and special events influence Thien An Bakery's prices.

MF 4 ^{How customers react to price changes matters for Thien An Bakery's}prices.

MF 5 Supply and demand affect Thien An Bakery's prices. External Factors:

EF 1 ^{Economic conditions like inflation and income impact Thien An Bakery's}prices.

EF 2 Social trends, like healthy eating, affect Thien An Bakery's prices. EF 3 Online shopping influences Thien An Bakery's pricing decisions.

EF 4 New technology in the bakery industry affects Thien An Bakery's prices. EF 5 Customer age, gender, and location influence Thien An Bakery's prices.

Government Regulations and Policies:

GRP 1 Food safety rules affect Thien An Bakery's prices.

GRP 2 Business fees and licenses impact Thien An Bakery's pricing decisions. GRP 3 Taxes influence Thien An Bakery's prices.

GRP 4 ^{Environmental regulations, such as waste management and packaging}rules, influence Thien An Bakery's prices.

GRP 5 ^{Changes in tax policies, such as sales tax or corporate tax rates,}influence Thien An Bakery's pricing strategy.

</div><span class="text_page_counter">Trang 33</span><div class="page_container" data-page="33">

<b>c) Exploratory Factors Analysis (EFA): </b>

Once the questionnaires and surveys have been validated, Exploratory Factor Analysis (EFA) can be employed as a next step. EFA is a statistical technique that aims to validate the scales of items in a questionnaire by representing a multidimensional data set using fewer variables. Conway and Huffcutt (2003) suggest that researchers tend to make better decisions when EFA has a more significant role in the research, underscoring the necessity for meticulous and deliberate analysis. To ensure the analysis is meaningful, it is essential to consider the following measures and tests when conducting EFA and interpreting the results:

KMO (Kaiser-Meyer-Olkin Measure of Sampling Adequacy): This measure helps determine if the data is suitable for factor analysis. Higher values (closer to 1) indicate better suitability for factor analysis.

Bartlett's Test of Sphericity: This test checks if sufficient correlations exist among variables for factor analysis. A significant result (p-value < 0.05) means the data is suitable.

Percentage of total variance explained: This demonstrates the proportion of total variation in the data explained by the factors. A higher percentage suggests a better-fitting model, as the factors explain a larger portion of the variance.

Eigenvalue: This value represents the amount of variance explained by each factor. Factors with eigenvalues greater than 1 are typically considered important.

Factor loading: This indicates the strength of the relationship between each variable and the factors. Values closer to 1 or -1 signify a strong relationship, while values closer to 0 imply a weak relationship. Factor loadings can be used to interpret the meaning of the factors and to determine which variables contribute most to each factor.

By considering these measures and tests, researchers can effectively utilize EFA to analyze questionnaire data and identify key factors that contribute to the overall model.

<b>d) Pearson's Correlation Analysis: </b>

Pearson's correlation coefficients act as a powerful statistical tool to evaluate the extent of linear relationships between independent and dependent variables. Williams (1996) clarifies that Pearson's correlation coefficient is a quantitative metric for assessing the strength of a linear relationship between two variables. Schober et al. (2018) offer a comprehensive overview of correlation coefficients, including Pearson's correlation coefficient, which is widely used to gauge the strength of a linear relationship between two continuous variables. The assessment of correlation significance is typically achieved by

</div><span class="text_page_counter">Trang 34</span><div class="page_container" data-page="34">

comparing the p-value (notated as "sig.") to a predetermined significance level, often set at 0.05. A p-value below 0.05 indicates that the correlation is statistically significant.

A Pearson correlation coefficient below 0 denotes a negative correlation between the two variables, where one variable's value declines as the other variable increases.

A Pearson correlation coefficient equal to 0 implies a lack of linear association between the variables, suggesting that alterations in one variable do not impact the other in a linear fashion.

A Pearson correlation coefficient above 0 indicates a positive correlation between the independent and dependent variables. In such instances, a rise in one variable is associated with an increase in the other variable. A strong positive correlation signifies a tight relationship between the variables, with changes occurring in the same direction.

<b>e) Regression Analysis: </b>

The relationship between pricing decisions and the bakery's pricing strategy will be explored using regression analysis. This method involves modeling the connection between a dependent variable and one or more independent variables, enabling researchers to predict the dependent variable's value based on the independent variables' values. As a result, this approach will help the researcher understand the influence of pricing decisions on the bakery's pricing strategy. Orme and Buehler (2001) introduce multiple regression as a versatile data analysis method that investigates the relationship between a dependent variable and other factors expressed as independent variables. Multiple regression analysis will be utilized to estimate the impact of various factors on the bakery's pricing strategy, including internal factors, external factors, market factors, and government regulations and policies.

The multiple linear regression would be employed to analyze the impact of dependent variables such as:

Internal Factors (X1) Market Factors (X2) External Factors (X3)

Government Regulations and Policies (X4)

The multiple linear regression model can be expressed as: PS = β0 + β1X1 + β2X2 + β3X3 + β4X4 + ε

</div><span class="text_page_counter">Trang 35</span><div class="page_container" data-page="35">

of independent variables on dependent variables X1, X2, X3, and X4, respectively ε is the error term, representing unexplained variance in the model

By fitting this model to the collected data, the author can estimate the coefficients (β1, β2, β3, β4) and assess the impact of each independent variable on the bakery product price. This will help the author to understand the factors that influence pricing decisions in the bakery industry and how they can be used to develop an effective pricing strategy. Upon entering data into the SPSS software, the researcher will evaluate the coefficients, placing particular emphasis on standardized coefficients, as the values in this column enable the testing of hypotheses established in chapter two. Additionally, the researcher will utilize the Model Summary, ANOVA, and Coefficients tables following data processing with regression analysis.

Model Summary: The Model Summary provides information on how well the regression model fits the data. This includes the R-squared value, which represents the proportion of the dependent variable's variation explained by the independent variables. A higher R-squared value indicates a better fit for the model. The adjusted R-squared value takes into account the number of independent variables in the model.

ANOVA: The ANOVA table evaluates the significance of the regression model. The primary focus is on the F-statistic and its p-value. If the p-value is smaller than the predetermined significance level (typically 0.05), it indicates that the model is significant and at least one independent variable influences the dependent variable. Kim (2017) elaborates that ANOVA is employed to compare group variances, proving useful in medical research to prevent alpha level inflation.

Coefficients: The Coefficients table shows the estimated effect of each independent variable on the dependent variable. It tells you if the effect is positive or negative and how big the effect is.

<b>8) Research’s ethics: </b>

In every study, adhering to ethical principles is crucial to ensure research with minimal issues. Research ethics encompass informed consent, respect for human dignity, and principles of autonomy, beneficence, nonmaleficence, and justice (Artal et al., 2017; Vanclay et al., 2013). All data collection instruments included an informed consent form. Furthermore, the researcher will maintain the complete confidentiality of any personal information concerning respondents by implementing a highly confidential treatment for all data gathered. There will be no coercion of participants; employees who do not wish to answer survey questions have the full right to decline participation. The study respects the dignity and autonomy of respondents and is conducted with credibility and rigor, free from

</div><span class="text_page_counter">Trang 36</span><div class="page_container" data-page="36">

bias, to promote high-quality research outcomes. The use of judgmental sampling helps reduce bias in respondent selection.

<b>9) Summary: </b>

Based on the literature review in Chapter 2, Chapter 3 of this study presents the research process and methods utilized to examine the collected data. The chapter also details the questionnaire design, which employs a Likert scale for 23 observed variables, defines the target sample size, and elaborates on the survey methodology. Furthermore, Chapter 3 explains how the data will be processed using SPSS software, incorporating reliability through correlation and regression analysis. In Chapter 4, the collected data will be processed using SPSS software, and the analysis of research results will be discussed, covering aspects such as sample descriptive statistics, scale reliability assessment using correlation analysis, and regression analysis.

</div><span class="text_page_counter">Trang 37</span><div class="page_container" data-page="37">

<b>CHAPTER 4: FINDINGS AND ANALYSIS </b>

Chapter 4 will encompass the processing of data collected from the survey questionnaire, utilizing Excel software and SPSS 20 to generate results for presentation. Initially, the author will present the characteristics using the descriptive statistics method. Subsequently, the author will carry out statistical analyses, review, and evaluate the reliability of the questions using Cronbach's Alpha reliability scales, while also examining the factors for their relevance to the proposed research model. If any problematic variables are detected, the author will promptly remove them and re-run the analysis process with SPSS software. Ultimately, the author will conduct correlation and regression analyses to test the hypotheses proposed in Chapter 2 - Literature Review.

<b>1) Descriptive statistics: a) Data Preparation: </b>

Prior to initiating data analysis, incomplete or inconsistent responses were rectified or removed from the dataset, as well as any duplicate entries. After the data filtration process, missing data were managed by assigning mean values to continuous variables. Furthermore, to ensure the robustness of the analysis, any responses containing unsuitable values were excluded from the dataset.

The number of invalid samples: 0

The final number of valid samples set for analysis is 90. The descriptive statistical analysis results for the sample encompass data on Thien An Bakery's staff and customers, including distribution by gender, age, and monthly income range. These findings are depicted in Table 2 below.

Characteristics

Frequency

Percent (%)

1. Gender

</div><span class="text_page_counter">Trang 38</span><div class="page_container" data-page="38">

3. Monthly income range

Table 3. Descriptive statistics of sample (Source: Author)

In the surveyed samples, there were 48 male respondents, accounting for 53.33% of the observation sample, while 42 respondents were female, representing 46.67% of the survey sample.

Of the 90 respondents, 22 responses were under 18 years old, and another 22 responses were between 28 and 39 years old, both comprising 24.44% of the sample. Respondents over 50 years old made up 22.22% of the sample with a total of 20 individuals. The number of respondents in the age groups 18 to 27 years old and 40 to 50 years old was equal, with 13 responses each, both contributing to 14.44% of the sample.

As shown in Table 3, the results reveal that 15.56% of respondents had a monthly income range of 5 million VND or less. Meanwhile, the group with an income between 5 and 10 million VND constituted 21.11% of the sample. The second-highest percentage, 22.22%, was observed in the 10-20 million VND income range, while the highest proportion was in the 20-30 million VND range, accounting for 24.44%. Finally, respondents with a monthly income exceeding 30 million VND represented 16.67% of the sample.

In summary, after conducting a descriptive statistical analysis of the data, the results indicate that the majority of respondents belong to the age groups of under 18 years old and 28 to 39 years old, with 22 out of 90 respondents in each category.

</div>