The Impact of Public Wi-Fi Expansion on Mobile Network Operators: Analysing Consumer Preferences and Revenue Implications

The Impact of Public Wi-Fi Expansion on Mobile Network Operators: Analysing Consumer Preferences and Revenue Implications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Impact of Public Wi-Fi Expansion on Mobile Network Operators: Analysing Consumer Preferences and Revenue Implications Mohammed Sarkhi, Mustafa Qahtan Alsudani, Hassan Mustafa Zwain, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6619712/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Dec, 2025 Read the published version in International Journal of Intelligent Engineering and Systems → Version 1 posted You are reading this latest preprint version Abstract The rapid expansion of public Wi-Fi has reshaped digital connectivity by providing consumers with cost-effective internet access. However, this growth presents economic challenges to mobile network operators (MNOs) by potentially disrupting their traditional revenue models. This study investigates the economic impact of public Wi-Fi proliferation on MNOs, focusing on consumer preferences, data usage behaviour, and implications for network profitability. Employing a mixed logit model, we analyse survey data from 2,000 respondents in Malaysia to assess how variations in Wi-Fi quality, availability, and affordability affect consumer choices regarding mobile data plans. Results indicate that improved public Wi-Fi access significantly reduces consumer reliance on cellular data, leading to a measurable decline in subscriptions to high-cost mobile plans. Specifically, a 100 Mbps increase in public Wi-Fi speed is associated with a 0.28% decrease in MNO revenue, while comprehensive Wi-Fi deployment across public transport systems contributes to a 1.09% revenue decline. Although Wi-Fi offloading reduces network congestion and infrastructure expenditure for MNOs, it also intensifies competition, prompting the need for innovative and adaptive business strategies. The findings suggest that public Wi-Fi should be positioned as a complementary service rather than a substitute for cellular networks. Strategic collaboration between governments and MNOs is essential to balance digital inclusivity with sustainable commercial models, ensuring continued investment in next-generation wireless infrastructure. Future research should further explore the dynamics between technological advancement, regulatory environments, and shifting consumer behaviours to support a resilient and equitable digital ecosystem. Public Wi-Fi Mobile Network Operators Wi-Fi Offloading Consumer Behaviour Data Plan Adoption Revenue Impact Digital Connectivity Wireless Infrastructure Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Wireless technology has experienced unprecedented growth due to scientific advancements and innovation. Smartphones, which rely on wireless communication, have been instrumental in driving this expansion (Shi, 2025 ). In 2020, smartphone users exceeded 3.6 billion globally, with projections reaching 5.7 billion by 2030 (Mobile Network Subscriptions Worldwide 2028, n.d.). This proliferation of smartphones has led to a corresponding surge in mobile data traffic, primarily driven by streaming video and other content consumption (Widdicks et al., 2019 ). Research indicates that video accounts for approximately 75% of residential internet traffic, a trend expected to continue (Cass, 2014 ). Additionally, wireless networks are increasingly handling traffic previously routed through wired connections, as modern wireless technology has advanced to the point where speed differentials have become negligible for most users (Chavan et al., 2023 ). By 2020, global mobile data traffic reached approximately 49 exabytes per month, with continued growth anticipated. Despite these advances, mobile networks face inherent challenges with rapid data consumption, which can result in reduced speeds and increased latency. While expanding infrastructure through additional base stations or cells offers one solution, it requires substantial and ongoing investment (Kulkarni et al., 2021 ). Heterogeneous networks present an alternative approach, distributing traffic across different network types to optimize performance (Zafar et al., 2023 ). Wi-Fi technology, operating via access points utilizing unlicensed 2.4 and 5 GHz frequency bands, provides a more cost-effective alternative to cellular networks that depend on expensive licensed bands (Chruszczyk et al., 2016 ). With most contemporary devices supporting both Wi-Fi and cellular connectivity, Wi-Fi offloading has emerged as a strategy to mitigate network congestion while reducing consumer data costs. Numerous studies have investigated the benefits of this approach (Cheung & Huang, 2015 ). Governmental bodies and mobile network operators (MNOs) have responded by expanding public Wi-Fi infrastructure to enhance coverage and alleviate cellular network burden. However, this expansion presents a potential revenue challenge for MNOs, as consumers may increasingly opt for free Wi-Fi over paid cellular data services (Oh et al., 2022 ). With mobile data services constituting a significant portion of MNO revenue, these operators must carefully evaluate public Wi-Fi from both beneficial and detrimental perspectives (Awwad, n.d.). MNOs must balance the advantages of reduced network load through Wi-Fi offloading against potential revenue losses as customers gravitate toward free Wi-Fi alternatives (Ayub et al., 2021a ). When assessing the financial viability of open Wi-Fi initiatives, MNOs must consider how decreased mobile data consumption impacts their revenue streams. Previous research has predominantly focused on MNO strategies, such as optimal access point placement and network cost-performance trade-offs (Hernandez et al., 2019 ). Surprisingly, limited research has explored the financial implications of public Wi-Fi availability on consumer behavior, particularly how enhanced Wi-Fi speed or coverage might influence mobile data plan selection. The Malaysian government has demonstrated a strong commitment to expanding public Wi-Fi access as part of its digital inclusion and connectivity strategy. In 2020, Malaysia had established 18,000 public Wi-Fi locations, with plans to increase this number to 41,000 by 2022 (Hetting, 2025 ). This ambitious initiative aligns with the government's broader objectives to strengthen digital infrastructure and ensure high-speed internet accessibility for all citizens. The concurrent deployment of 5G technology and public Wi-Fi in Malaysia exemplifies the potential for such initiatives to stimulate economic growth and enhance communication services. The Malaysian government seeks to understand how public Wi-Fi quality and coverage influence consumer behavior, particularly regarding mobile network operators (Mohamad Yazid et al., 2021 ). While existing research has primarily addressed MNO perspectives, this study aims to provide comprehensive insights into both consumer and MNO viewpoints, evaluating how open Wi-Fi quality and availability affect communication costs. This research will clarify whether public Wi-Fi competes with or complements mobile networks, offering valuable guidance for developing strategic public Wi-Fi expansion, informing government policy, and enabling MNOs to optimize Wi-Fi offloading approaches. Ultimately, these findings will support the Malaysian government and MNOs in creating an integrated public Wi-Fi framework that addresses evolving consumer needs while maximizing benefits for all stakeholders. 2. Literature Review 2.1. Wi-Fi Congestion Wireless devices have experienced a dramatic surge in demand, resulting in increased mobile network congestion (Clarke, 2014 ). Wi-Fi offloading has emerged as an effective management strategy, redirecting portions of mobile data traffic to available Wi-Fi networks (Clarke, 2014 ). Despite Wi-Fi's substantial capacity, quantifying the volume of offloaded data remains critical. Wi-Fi offloading has attracted significant research attention, with studies predominantly examining technical aspects, economic benefits for Mobile Network Operators (MNOs), and user advantages. Numerous investigations have explored the integration of Wi-Fi offloading with 5G technology (Ayub et al., 2021b ). Researchers have identified multiple advantages of Wi-Fi offloading, with delayed Wi-Fi offloading representing a notable approach. This strategy involves postponing non-time-sensitive downloads—such as videos, cloud backups, and non-urgent applications—until Wi-Fi connectivity becomes available (K. Lee et al., 2010 ). Delayed offloading extends to mobile scenarios, such as when devices connect to Wi-Fi in moving vehicles, creating what researchers term "drive-through Internet" (Rosele et al., 2024 ). Research indicates that Wi-Fi offloading can reduce mobile data traffic by 45–65% (Poularakis et al., 2016 ). When users and vehicles delay mobile network usage by 30–60 minutes and 10 minutes respectively, network congestion can decrease by 60–80%. Some studies suggest that strategic implementation of delays could reduce congestion by more than 80% (Kamtam et al., 2024 ). Furthermore, Wi-Fi utilization demonstrates enhanced energy efficiency compared to mobile data, conserving 55–65% of energy consumption when users accept 60-minute download delays (Ron & Lee, 2020 ). While most research focuses on the technical processes of Wi-Fi offloading, less attention has been devoted to voluntary consumer behavior regarding the transition from mobile data to Wi-Fi (Husnjak et al., 2018 ). Several researchers have approached Wi-Fi offloading from an economic rather than technical perspective. Zhuo et al. developed an incentive framework offering discounts to users willing to delay their data consumption. However, their study concentrated on MNO-mandated offloading scenarios rather than user-initiated transitions motivated by cost savings—which constitutes this study's focus. Lee et al. simulated the financial impact of Wi-Fi offloading on both MNOs and users across various mobile plans (unlimited, pay-per-use, tiered, and congestion-based). Using a two-stage game model, they demonstrated potential MNO revenue increases of 21–152% and user savings of 73–319%. Their research contains limitations: insufficient explanation of willingness-to-pay calculations and price elasticity implementations, along with assumptions regarding user tolerance for delays. Poularakis et al. investigated optimal placement strategies for Wi-Fi access points (APs) and pricing effects on Wi-Fi data utilization. Their findings indicated that MNOs could reduce operational costs while offering users 50% price reductions through Wi-Fi offloading. However, previous research has predominantly examined Wi-Fi offloading from the MNO perspective, emphasizing revenue maximization rather than user perceptions of Wi-Fi speed and coverage. This paper will address this gap by measuring changes in user-initiated Wi-Fi usage based on Wi-Fi quality and coverage, and quantifying the corresponding reduction in mobile data network traffic. 2.2. Public Wi-Fi Public Wi-Fi is typically offered without charge in various locations. In public transit systems and communal spaces, it is provided by mobile network operators (MNOs), national governments, and municipal authorities. Governments implement public Wi-Fi initiatives for multiple reasons: promoting universal internet accessibility, reducing telecommunications expenses, stimulating economic development, enhancing urban quality of life, and marketing their cities (Geerdts & Gillwald, 2017 ). As of 2020, Malaysia had established 743 Community WiFi hotspots nationwide under the Universal Service Provision (USP) programme (Fusion, n.d.). By 2022, the state of Selangor planned to expand its free WiFi Smart Selangor (WiFiSS) access points from 828 to 1,600, targeting areas such as low-cost housing developments, religious and healthcare institutions, community centers, recreational and transportation hubs, and state government facilities (Journal, 2022 ). Malaysia is also implementing Wi-Fi 6 (IEEE 802.11ax), which provides high-speed connectivity for outdoor environments. Beyond government initiatives, businesses also deploy public Wi-Fi to enhance their online visibility, with cafes and restaurants offering free connectivity to attract customers. Mobile network operators establish public Wi-Fi networks to attract users while preventing mobile network saturation (Poularakis et al., 2016 ; Qiu et al., n.d.). Their objective is to ensure customers experience seamless and convenient Wi-Fi connectivity. If MNOs cannot accommodate the escalating demand for mobile data, network quality deterioration may occur, resulting in reduced speeds and compromised service. To mitigate this risk, they redistribute portions of mobile traffic to Wi-Fi networks. This reduction in mobile data traffic simultaneously decreases energy consumption, thereby lowering MNO operational costs. Consequently, numerous MNOs have implemented Wi-Fi offloading strategies (Aijaz et al., 2013 ). Nevertheless, an inherent conflict of interest exists between MNOs and public Wi-Fi (He et al., 2016 ; Poularakis et al., 2016 ; Ron & Lee, 2020 ). MNOs generate revenue from subscribers who pay based on data consumption, with premium plans offering substantial mobile data allowances. Higher user dependency on mobile networks increases willingness to pay, aligning with MNO financial objectives. However, when high-quality Wi-Fi is available without charge, the perceptible difference between mobile data and Wi-Fi diminishes. This reduces consumer incentive to purchase large data allowances, potentially undermining MNO profitability. With widespread Wi-Fi availability, consumers may reduce mobile data consumption or opt for more economical data plans instead of premium packages with extensive data limits (J. Lee et al., 2014 ). Historically, when feature phones dominated the Malaysian market and smartphones had not yet achieved widespread adoption, major manufacturers such as Nokia, Sony Ericsson, and Samsung held prominent market positions. MNOs then generated significant revenue from mobile data charges. During this period, WAP (Wireless Application Protocol) usage incurred substantial costs, and had Wi-Fi been incorporated, users would have leveraged it instead, resulting in diminished MNO revenue. In the contemporary context, Wi-Fi proliferation presents both advantages and disadvantages for MNOs. The disadvantage manifests as potential reductions in mobile data usage or migrations to lower-cost plans, diminishing MNO profits. Conversely, the advantage lies in public Wi-Fi's capacity to alleviate network congestion, thereby enhancing overall service quality. Depending on implementation and usage patterns, Wi-Fi can function as either a complement or substitute for mobile networks. This study aims to analyze and determine this balance. Public WiFi has attracted academic interest; however, this attention has predominantly centered on user security and privacy rather than economic implications. For instance, Aswani et al. analyzed demographic factors influencing WiFi adoption, including age, gender, education, and income. Some studies have examined the economic considerations influencing Wi-Fi security decisions, while others have focused on technical and security-related challenges (Maimon et al., 2022 ). Few researchers have attempted to quantify the economic value of public WiFi. One study evaluated it as an economic product but questioned whether public Wi-Fi should be classified as a public service (Van Den Velden & Sadowski, 2023 ). To our knowledge, no comprehensive study has precisely measured the financial impact of public Wi-Fi. Consequently, our research will focus on calculating consumer savings on data expenses and quantifying MNO revenue losses attributable to public Wi-Fi expansion. Our findings will contribute to a more nuanced understanding of the relationship between public Wi-Fi availability and mobile data consumption patterns. 3. Contributions, Implications and Novelty This research provides a quantitative assessment of the revenue impact on Mobile Network Operators (MNOs) resulting from public Wi-Fi expansion, revealing a 0.28% revenue reduction per 100 Mbps increase in Wi-Fi speed and a 1.09% decline attributable to Wi-Fi deployment in public transportation systems. Through a mixed digit model applied to survey data from 2,000 respondents, we conducted a comprehensive analysis of how Wi-Fi range and quality influence consumer decision-making processes, while simultaneously documenting how Wi-Fi offloading affects network traffic patterns. The findings highlight the necessity for MNOs to develop sustainable investment strategies for mobile network infrastructure in an environment of expanding public Wi-Fi availability. Our findings indicate significant cost efficiencies in Wi-Fi infrastructure deployment; however, these advantages present challenges to conventional MNO revenue models. To maintain profitability in this evolving landscape, MNOs should consider implementing innovative business approaches including dynamic pricing mechanisms, restructured data plans, and strategic collaborations with public networks to extend connectivity reach. From a policy perspective, governments should formulate regulatory frameworks that position public Wi-Fi as a complement to mobile networks rather than a substitute, ensuring balanced digital infrastructure development. The research demonstrates how enhanced Wi-Fi quality reduces consumer dependence on premium mobile data plans, creating a potential feedback loop affecting MNO investment decisions. Notably, excessive public Wi-Fi proliferation may disincentivize MNOs from investing in mobile network advancement, potentially resulting in service quality deterioration over time. This insight suggests the need for balanced public-private approaches to telecommunications infrastructure development. This study represents one of the first empirical measurements of the financial implications of public Wi-Fi deployment. While existing literature predominantly examines MNO strategies and technical implementation considerations, our research distinctively evaluates consumer preferences and willingness-to-pay under varying Wi-Fi conditions. By employing advanced econometric modeling techniques, we provide granular insights into consumer decision-making processes regarding mobile data consumption in environments with differential Wi-Fi accessibility. Furthermore, the research contextualizes its findings within Malaysia's digital inclusion initiatives, offering valuable insights applicable to other emerging markets with expanding public Wi-Fi networks. This contextual relevance enhances the study's applicability to diverse geographical settings with similar telecommunications infrastructure objectives. Our multidimensional approach—considering both economic impacts and behavioral responsesprovides a more comprehensive understanding of the complex interplay between public Wi-Fi expansion and mobile network economics than previous unidimensional analyses. 4. Methodology This research investigates network usage patterns, with particular emphasis on public Wi-Fi utilization. It examines Wi-Fi quality and availability factors, quantifies mobile data savings for users, and analyzes potential benefits for mobile network operators when traffic shifts to Wi-Fi networks. Understanding consumer preferences forms the foundation of this analysis. Consumer selection behavior is analyzed using a mixed logit model, which accounts for the diversity of consumer preferences while acknowledging shared priorities among users. Unlike more rigid models that may inadequately represent real-world behavior, the mixed logit model accommodates variations in consumer preferences, thereby enhancing analytical flexibility and precision (Train & Sonnier, 2005 ). By eliminating restrictive assumptions and recognizing individual differences, the mixed logit model has become widely applied in forecasting consumer decisions and evaluating new product or service offerings (Kim, 2018 ; Train & Sonnier, 2005 ). The model quantifies consumer utility through a dual-component structure. Consumer advantage is conceptualized as comprising a deterministic element (V) that addresses quantifiable factors, and a stochastic element (E) that accounts for unforeseen variables influencing decision-making. This approach allows for a more nuanced understanding of how Wi-Fi quality and availability influence network selection behavior and subsequent impacts on mobile data consumption patterns. The expressions follow like: $$\:{U}_{njt}=\:{V}_{njt}+{e}_{njt}$$ 1 β’ n ∼ N ( b , ∑) or ln β’ n ∼ N ( b , ∑) (2) \(\:{U}_{njt}\) denotes the utility that individual n derives from choosing option j at time t. The expression β_{n}'X_{njt} encompasses observable variables that affect decision-making, whereas ε_{njt} accounts for unobserved influences. In contrast to the conventional logit model, the mixed logit model posits that preference parameters are distributed, thereby offering greater flexibility in modelling consumer behavior. Due to the complex mathematical computations involved in resolving the model, we utilize Maximum Simulated Likelihood (MSL) for estimation. MSL simplifies the procedure by randomly creating preference parameters and then averaging them over many simulations to determine the total choice probability. Key Measures Two key measures derived from this model are Marginal Willingness to Pay (MWTP) and Relative Importance (RI). MWTP indicates how much a consumer is willing to pay for a one-unit improvement in a feature and is calculated as: MWTP_ k = -β_k / β_price where β_k represents the feature’s coefficient, and β_price is the price coefficient. RI measures how much a particular feature influences a consumer’s decision. It is calculated by assessing how much utility changes when a feature moves from its minimum to maximum level using the formula: This percentage helps determine which features have the most impact on decision-making. By applying this approach, we gain deeper insights into consumer behavior regarding network usage, allowing mobile operators to optimize their services and pricing strategies accordingly. The Fig. 1 shows the step-by-step methodology It follows a structured consumer decision analysis framework. It begins with a Consumer Engagement Analysis Funnel, assessing Wi-Fi quality, data conservation, financial benefits, and consumer preferences. These key decision factors integrate into a Flexible Modelling Approach, balancing diverse consumer preferences. The Consumer Utility Equation Methodology quantifies consumer utility using deterministic and stochastic components, considering preference distributions. A Cycle of Estimating Choice Probabilities involves estimating probabilities, generating random parameters, performing simulations, and averaging results. The Consumer Decision Analysis Process then derives key measures, computing Marginal Willingness to Pay (MWTP) and Relative Importance (RI). Finally, Data Analysis to Optimization refines insights, recommends strategies, and optimizes offerings for improved pricing and services. 5. Survey In this survey, virtual market conditions are studied with the cellular plans. The survey was conducted between age 21 and 50 group people consists of male and female in Malaysia. The survey was held in finance, IT sector, manufacturing, retail sectors in Malaysia. Among the whole survey around 2000 participation was there from various demographics. The survey was conducted on Poll-Pool. The communication cellular system for 3G,4G and 5G network, free call plans, call usage time, data usage are collected from different respondents in Poll-pool. Respondent characteristics are presented in Fig. 2 . Around 2,000 respondents reported using a smartphone plan. On average, they paid 164.92 MYR (37.10 USD) per month before discounts and 136.33 MYR (30.67 USD) after discounts. About 450 users (26.1%) had an unlimited data plan. Their average mobile data usage, excluding Wi-Fi, was 17 GB per month. The most common activities were web searches (40.5%), messaging and social media (18%), reading news (13%), and streaming live videos (5%). In terms of Wi-Fi usage, 70% of respondents indicated that they either use it "quite frequently" or "a lot." When including those who rated their usage as "average," the figure rises to 93.3%, suggesting that the majority of respondents regularly utilize Wi-Fi. When asked why they use Wi-Fi, 75% of respondents cited the high cost of cellular data as a significant burden (multiple responses allowed). About 20% respondents reported sudden Wi-Fi disconnections or unavailability, while 21.7% mentioned that there is poor coverage. Around 15% had trouble entering passwords in public places, and another 15% faced slow speeds or connection errors. In total, about 35% of the complaints were about slow Wi-Fi. Around 70% would use Wi-Fi more if the speed improved. Meanwhile, 9.3% would rely less on Wi-Fi if mobile data costs were lower, and 16.8% said having more mobile data would affect Wi-Fi use. This shows that price and data limits play a big role in Wi-Fi usage. Most public Wi-Fi use (70%) occurred in residential areas. followed by public transportation (13%) and outdoor spaces, such as plazas, beaches, and mountains (9%). The choice experiment method studies what consumers prefer by showing different virtual product features and collecting their responses (Green, 1978). In this study, a virtual mobile plan was shown, and choices were analyzed. This helps understand how consumers decide on mobile plans and how Wi-Fi speed and coverage affect their choices and willingness to use Wi-Fi. This research examined various factors, including mobile data speed, Wi-Fi speed, the number of Wi-Fi hotspots, and the monthly cost of a phone plan. A 2018 report by the Ministry of Science and ICT assessed network quality and found that LTE had an average download speed of 150.68 Mbps, public Wi-Fi reached 354.07 Mbps, and subway Wi-Fi averaged 59.33 Mbps. The study concluded that mobile networks had an average speed of 150 Mbps, public Wi-Fi operated at 350 Mbps, and Wi-Fi on subways ran at 59 Mbps. In the 2020 iteration of the same survey, the fastest MNO's 5G download speed reached 795.57 Mbps, while the slowest MNO’s LTE download speed was 109.47 Mbps. Theoretical speeds for 5G are expected to support up to 7 Gbps, and Malaysia MNOs, alongside local governments, have also introduced a Wi-Fi support plan to provide speeds of up to 1.2 Gbps using the IEEE 802.11ax standard. Consequently, the speed parameters in this study were defined in alignment with anticipated improvements from future technological advancements. The monthly plan includes both cellular data amounts and pricing to see how Wi-Fi speed and coverage changes affect mobile plans and data use. This helps understand how consumers adjust their plans and choose to use Wi-Fi instead of mobile data. The plan options were based on prices and data offers from the top three major mobile providers in Malaysia. Table 2 shows the factors and options studied in this research. Presenting all the possibilities to respondents would have been overwhelming and would have made it more difficult for them to digest the options, especially Given the numerous possible combinations of attributes in this study (4 × 4 × 3 × 4 = 192), there are a total of 192 different ways these factors can be arranged. Consequently, the number of options was lowered to 24 using a fractional factorial design. Each responder was required to evaluate eight sets of choices, each consisting of three possibilities. To ensure the accuracy of responses, the same question was presented twice to each participant, with any inconsistencies resulting in the exclusion of those responses. Only 13,142 choice sets were used in the final analysis since 2,690 (17.0%) of the 15,832 total choice sets were deemed to contain inconsistent replies. Table 1 Characteristics and Tiers Characteristics Tiers User Experienced 100 Mbps / 400 Mbps / 1 Gbps / 2 Gbps. Cellular speed Wi-Fi speed (user-experienced) 50 Mbps / 300 Mbps / 900 Mbps / 1500 Mbps Places with Wi-Fi availability Residential (house, cafe, library, etc.) Monthly rate plan 91.74 MYR Per month (includes 3 GB of data). 152.91MYR per month Per month (includes 10 GB of data). 214.07 MYR (data volume provided: 100 GB) 397.47 MYR Per month (includes 300 GB of data). 6. Results The analysis was done using the mixed logit method. For the MSL simulation, 100 random samples were taken multiple times. The model resulted in the following equation. Model 1 : \(\:{U}_{nj}=\:{\gamma\:}_{1}{Z}_{cellular}+\:{\gamma\:}_{2\:}{Z}_{wi-fi}+\:{\gamma\:}_{3}{D}_{bus-metro}+\:{\gamma\:}_{4}{D}_{public}+\:{\gamma\:}_{5}{Z}_{plan\:}\) + \(\:{e}_{nj}\) (4) Model 2: \(\:{U}_{nj}\) = \(\:{\gamma\:}_{1}{Z}_{cellular}+\:{\gamma\:}_{2\:}{Z}_{wi-fi}+\:{\gamma\:}_{3}{D}_{bus-metro}+\:{\gamma\:}_{4}{D}_{public}+\:{\gamma\:}_{5}{Z}_{plan\:\:}+\:{\gamma\:}_{6}{Z}_{wi-fi\:\times\:\:plan\:}\) + \(\:{\gamma\:}_{7}{Z}_{bus\:and\:metro\:\times\:plan\:}\) + \(\:{\gamma\:}_{8}{Z}_{public\:\times\:plan\:}\) + \(\:{e}_{nj}\) (5) In model 1, \(\:{Z}_{cellular}\) indicates the cellular communications transmission speed, \(\:{Z}_{wi-fi}\) is the Wi-Fi speed and \(\:{Z}_{bus-metro}\) , \(\:{Z}_{public}\) are some of the dummy variables corresponding to the Wi-Fi supported places. These variables have a digital logic. Table 2 Estimation Results of Mixed Logit model Type Characteristic Model 1 Model 2 Compute S.E. RI Estimate S.E. RI Coef. Variance Cellular speed Wi-Fi Speed Wi-Fi (Bus & Metro) Wi-Fi (Public) Monthly Rate Plan Cellular Speed Wi-Fi Speed Wi-Fi(Bus & Metro) Wi-Fi(public) Monthly Rate Plan 0.57 0.83 0.11 0.60 − 0.69 0.15 0.03 1 0.75 0.50 0.02 0.03 0.01 0.06 0.05 0.05 0.14 0.07 0.06 0.02 11.0% 12.1% 0.08% 6% 69% 0.54 1.71 0.44 0.56 –0.61 0.23 0.29 1.02 0.7 0.5 0.03 0.08 0.11 0.13 0.03 0.05 0.07 0.07 0.08 0.02 7% 15% 2.5% 3.5% 39% The table presents coefficient estimates, standard errors (S.E.), and relative importance (RI) values for two models examining consumer preferences for various network attributes. The coefficient section quantifies how each attribute influences utility, while the variance section illustrates preference heterogeneity across individuals. In Model 1, cellular speed demonstrates a positive coefficient of 0.57 (S.E.=0.02) with a relative importance of 10%, indicating that faster cellular networks positively influence consumer choice while accounting for approximately one-tenth of the decision-making process. Wi-Fi speed exhibits a stronger effect with a coefficient of 0.83 (S.E.=0.13) and an RI of 12.1%, suggesting consumers place greater value on Wi-Fi speed improvements compared to cellular network enhancements. Wi-Fi availability in transportation systems (buses and metro) shows a positive influence (coef.=0.71, RI = 8%), while Wi-Fi in public spaces emerges as particularly impactful (coef.=0.95, RI = 15%), representing the most influential positive attribute in this model. The monthly rate plan coefficient of -0.69 (RI = 6.9%) confirms that higher costs reduce utility, though this factor plays a relatively moderate role in overall preference formation. Examining variance parameters in Model 1 reveals substantial heterogeneity in consumer valuation of public Wi-Fi (variance = 0.75), indicating significant individual differences in how consumers value this attribute. This suggests diverse consumer segments with varying appreciation for public Wi-Fi availability. Model 2 reveals notable shifts in attribute importance. Cellular speed demonstrates reduced impact (coef.=0.30, S.E.=0.03, RI = 7%) compared to Model 1. Conversely, Wi-Fi speed gains prominence (coef.=1.13, S.E.=0.21, RI = 15%), indicating stronger consumer emphasis on Wi-Fi performance. While Wi-Fi availability in transportation (coef.=0.61, RI = 5%) and public spaces (coef.=0.48, RI = 13%) maintain positive influences, their relative importance differs from Model 1. The most striking difference appears in the monthly rate plan's role, which maintains a negative coefficient (-0.65, S.E.=0.03) but exhibits a dramatically increased relative importance of 39%, suggesting that cost considerations dominate consumer decision-making in this model specification. Variance patterns also shift significantly, with Wi-Fi speed showing higher preference heterogeneity (variance = 0.29), while monthly rate plan variance remains minimal (0.02), indicating more uniform cost sensitivity across consumers. The comparison between Models 1 and 2 highlights how consumer preferences can vary substantially under different modeling assumptions. Model 1 emphasizes the value of public Wi-Fi availability, while Model 2 demonstrates stronger consumer emphasis on Wi-Fi speed and, most prominently, price sensitivity. The variance parameters further illustrate that certain attributes (particularly public Wi-Fi in Model 1 and Wi-Fi speed in Model 2) elicit diverse consumer responses, while others (especially monthly rate plans in Model 2) generate more consistent reactions. These findings carry important strategic implications for service providers, suggesting the need for segmented approaches that recognize heterogeneous preferences regarding network costs, speeds, and Wi-Fi availability. Network operators should consider developing differentiated offerings that address these varying consumer priorities while acknowledging the dominant role of price sensitivity in overall decision-making processes. The Fig. 3 illustrates how consumers' Willingness to Pay (WTP) for mobile data plans decreases when they have access to different types of Wi-Fi options, with each line representing a distinct scenario: faster Wi-Fi speeds, Wi-Fi on buses and metros, and Wi-Fi availability anywhere. Along the horizontal axis, monthly communication expenses (in 1,000 KRW) increase from left to right, while the vertical axis shows how much users would reduce their WTP (also in 1,000 KRW) if provided with the respective Wi-Fi option. All three lines slope upward, indicating that individuals paying higher monthly costs are more inclined to reduce those expenses if given a reliable Wi-Fi alternative. Among the three scenarios, faster Wi-Fi speed (represented by the top line) consistently produces the largest reduction in WTP, suggesting that users place a premium on speed and reliability. As their monthly expenses increase, they perceive even greater value in shifting away from expensive data plans when a fast Wi-Fi option is available. In comparison, providing Wi-Fi on buses and metros (the middle line) also leads to a notable drop in WTP, but not as significantly as improving Wi-Fi speed. This result highlights the importance of connectivity during commutes, though it does not outweigh the benefit of having universally faster Wi-Fi. The "Wi-Fi Anywhere" scenario (the bottom line) shows a smaller overall effect on WTP reduction, implying that while broad coverage is appealing, it does not match the perceived benefit of either higher speeds or targeted availability in high-use locations like public transportation. In practical terms, these findings suggest that mobile subscribers with higher monthly expenditures are the most responsive to improvements in Wi-Fi quality or coverage. From a service provider's perspective, fast, reliable Wi-Fi represents the strongest driver of users potentially downgrading their data plans, especially among higher-spending customers. Meanwhile, coverage in public transportation and broad, everyday access can still motivate users to reduce their mobile spending, but not to the same extent as speed improvements. Policymakers and telecommunications companies can leverage these insights to develop targeted strategies—such as upgrading open Wi-Fi networks, expanding coverage in critical transit areas, or refining pricing models—to balance consumer savings with sustainable revenue streams and effective network management. This bar chart, Fig. 4 compares users' willingness to pay (WTP) for mobile data across three different Wi-Fi scenarios—"Wi-Fi speed up," "Wi-Fi speed up + Bus & Metro," and "Wi-Fi speed up + public"—at various monthly communication expense levels. Each cluster of three bars represents a specific expense category on the x-axis, while the y-axis displays the WTP in units of 1,000 KRW. The "Wi-Fi speed up" bars (blue) consistently appear tallest within each group, demonstrating that enhanced Wi-Fi speed has the strongest influence on consumers' willingness to pay for mobile plans. The "Wi-Fi speed up + Bus & Metro" bars (green) consistently occupy the middle position, indicating that extending high-speed Wi-Fi to public transportation provides moderate value but doesn't match the impact of universal speed improvements. The "Wi-Fi speed up + public" scenario (orange) generally shows the lowest WTP across expense categories, suggesting that basic open Wi-Fi availability generates less perceived value than more targeted enhancements. The data reveals a clear correlation between monthly communication expenses and sensitivity to Wi-Fi improvements. As expense values increase along the x-axis, the bar heights for each scenario also rise, reflecting greater potential savings or higher perceived value among users with higher baseline spending. This progressive increase is particularly pronounced for the Wi-Fi speed improvement scenario, highlighting how consumers with premium mobile plans place even greater value on having access to fast, reliable wireless connections. These findings offer actionable insights for telecommunications operators and policymakers seeking to enhance consumer value or optimize service offerings. The data suggests prioritizing Wi-Fi speed improvements would yield the greatest impact on consumer behavior, while strategic coverage expansions in transportation networks represent a valuable secondary focus. This approach would be especially effective for targeting higher-spending consumers, who demonstrate the greatest responsiveness to Wi-Fi enhancements. Implementing such prioritized improvements could help balance consumer cost savings with sustainable service delivery models in evolving telecommunications markets. 7. Conclusion This study highlights the evolving relationship between public Wi-Fi and cellular data services, revealing both opportunities and challenges for consumers and Mobile Network Operators (MNOs). While mobile data offers greater versatility by enabling seamless connectivity on the move, the expansion of high-quality public Wi-Fi—especially with advancements such as IEEE 802.11ax—presents a viable alternative in many settings. Governments, like Malaysia's, are already exploring initiatives to improve public access, aiming to lower communication costs and support underserved communities. However, this expansion also introduces competition that may reduce MNO revenues and potentially limit future investments in mobile network infrastructure. Despite these concerns, public Wi-Fi also offers benefits to MNOs through Wi-Fi offloading, which reduces network congestion and operational costs. Many MNOs have adopted such strategies, although they face trade-offs in the form of decreased cellular data consumption. The study finds that consumers are highly sensitive to mobile plan pricing and are more likely to shift usage toward improved public Wi-Fi, further challenging MNOs’ revenue streams. Importantly, the research suggests that public Wi-Fi should serve as a complementary service rather than a replacement for cellular data. This balanced approach allows consumers to enjoy affordable access while preserving the financial sustainability of private network operators. Policymakers, MNOs, and regulatory bodies must work together to develop frameworks that encourage infrastructure investment, maintain service quality, and protect consumer interests. The study also acknowledges several limitations, including a lack of focus on technical challenges such as security risks, network reliability, and battery consumption. It primarily considers voluntary Wi-Fi offloading, omitting the effects of operator-enforced switching policies. Furthermore, value-added features of mobile plans—such as bundled entertainment or premium services—may significantly influence user decisions but were not explored in depth. Future research should expand on these aspects, incorporating real-world usage data and consumer insights to better understand behavioral trends and network dynamics. As digital connectivity continues to evolve, a thoughtful, evidence-based approach is essential to ensure equitable, efficient, and sustainable integration of Wi-Fi and mobile networks for all stakeholders. Declarations Ethics approval and consent to participate Not applicable. This study does not involve any human participants or animals. Consent for publication Not applicable. Availability of data and material The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors' contributions MS and DA conceptualized the study. Methodology was developed by MS, MQA, and AHM . Formal analysis was conducted by HMZ and AHM , while investigation was performed by HMZ . Data curation was handled by MS . The original draft was written by MQA . Manuscript review and editing involved MS, MQA, HMZ, and DA . Supervision and overall guidance was provided by DA . All authors reviewed and approved the final manuscript. Acknowledgements The authors gratefully acknowledge the support provided by Al-Furat Al-Awsat Technical University, particularly the Department of Communication Engineering, for fostering an environment conducive to research and academic excellence. We extend our sincere appreciation to the faculty and staff for their guidance and assistance throughout this study. 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Computers, Environment and Urban Systems , 72 , 13–24. https://doi.org/10.1016/j.compenvurbsys.2018.06.004 Kulkarni, V., Walia, J., Hämmäinen, H., Yrjölä, S., Matinmikko-Blue, M., & Jurva, R. (2021). Local 5G services on campus premises: Scenarios for a make 5G or buy 5G decision. Digital Policy, Regulation and Governance , 23 (4), 337–354. https://doi.org/10.1108/DPRG-12-2020-0178 Lee, J., Yi, Y., Chong, S., & Jin, Y. (2014). Economics of WiFi offloading: Trading delay for cellular capacity. IEEE Transactions on Wireless Communications , 13 (3), 1540–1554. Scopus. https://doi.org/10.1109/TWC.2014.010214.130949 Lee, K., Lee, J., Yi, Y., Rhee, I., & Chong, S. (2010). Mobile data offloading: How much can WiFi deliver? Proceedings of the 6th International COnference , 1–12. https://doi.org/10.1145/1921168.1921203 Maimon, D., Howell, C. J., Jacques, S., & Perkins, R. C. (2022). Situational awareness and public Wi-Fi users’ self-protective behaviors. 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IEEE Transactions on Network and Service Management. https://doi.org/10.1109/TNSM.2016.2521352 Qiu, L., Rui, H., & Whinston, A. (n.d.). Hotspot Economics: Procurement of Third-Party WiFi Capacity for Mobile Data O≠ oading . Ron, D., & Lee, J.-R. (2020). Expectation Maximization Based Power-Saving Method in Wi-Fi Direct. IEEE Access , 8 , 158600–158611. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3014673 Rosele, N., Mohd Zaini, K., Ahmad Mustaffa, N., Abrar, A., Fadilah, S. I., & Madi, M. (2024). Digital transformation in wireless networks: A comprehensive analysis of mobile data offloading techniques, challenges, and future prospects. Journal of King Saud University - Computer and Information Sciences , 36 (5), 102071. https://doi.org/10.1016/j.jksuci.2024.102071 Shi, X. (2025). The Historical Development and Applications Analysis of Wireless Communication Technology. Theoretical and Natural Science , 80 (1), 58–63. https://doi.org/10.54254/2753-8818/2025.GL20171 Train, K., & Sonnier, G. (2005). Mixed Logit with Bounded Distributions of Correlated Partworths. In R. Scarpa & A. Alberini (Eds.), Applications of Simulation Methods in Environmental and Resource Economics (Vol. 6, pp. 117–134). Springer-Verlag. https://doi.org/10.1007/1-4020-3684-1_7 Van Den Velden, J., & Sadowski, B. M. (2023). Creating public value with municipal Wi-Fi networks: A bottom-up methodology. Digital Policy, Regulation and Governance , 25 (2), 77–103. https://doi.org/10.1108/DPRG-12-2019-0107 Widdicks, K., Hazas, M., Bates, O., & Friday, A. (2019). Streaming, Multi-Screens and YouTube: The New (Unsustainable) Ways of Watching in the Home. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems , 1–13. https://doi.org/10.1145/3290605.3300696 Zafar, A., Samad, F., Syed, H. J., Ibrahim, A. O., Alohaly, M., & Elsadig, M. (2023). An Advanced Strategy for Addressing Heterogeneity in SDN-IoT Networks for Ensuring QoS. Applied Sciences , 13 (13), Article 13. https://doi.org/10.3390/app13137856 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 31 Dec, 2025 Read the published version in International Journal of Intelligent Engineering and Systems → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6619712","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456323181,"identity":"bfb08943-7050-4b3d-8a5f-3085389cf28f","order_by":0,"name":"Mohammed Sarkhi","email":"","orcid":"","institution":"Al-Furat Al-Awsat Technical University","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Sarkhi","suffix":""},{"id":456323184,"identity":"2c9a4c1b-d72d-4ee2-9e8f-f5a481788e01","order_by":1,"name":"Mustafa Qahtan Alsudani","email":"","orcid":"","institution":"Imam Ja’afar Al-sadiq 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(RI)\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6619712/v1/50436f52a3553619e1e180b6.jpg"},{"id":82880498,"identity":"a37dadd1-3b2c-4d3a-b780-00728626c31a","added_by":"auto","created_at":"2025-05-16 10:38:35","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34094,"visible":true,"origin":"","legend":"\u003cp\u003eDemographic Characteristics of the result\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6619712/v1/5f7a4877eb99fa22b1e3fd47.jpg"},{"id":82880506,"identity":"49bb069f-589b-4b93-93c0-28b83d61c903","added_by":"auto","created_at":"2025-05-16 10:38:35","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53224,"visible":true,"origin":"","legend":"\u003cp\u003eWTP drop by plan and Wi-Fi quality.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6619712/v1/982d2e2bb7158200d1680708.jpg"},{"id":82880537,"identity":"5c17ff7d-f4b3-40f0-b8af-ee7117fe9280","added_by":"auto","created_at":"2025-05-16 10:38:37","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":73561,"visible":true,"origin":"","legend":"\u003cp\u003eWTP changes by rate plan, Wi-Fi quality, and interactions.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6619712/v1/786397eaa0592e9a22c5e67c.jpg"},{"id":104911602,"identity":"f8bcb446-a483-4a0c-90e7-3fccb1abc11b","added_by":"auto","created_at":"2026-03-18 15:21:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1101860,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6619712/v1/df74c869-e9f9-4e4d-b4e7-277009a0e236.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Public Wi-Fi Expansion on Mobile Network Operators: Analysing Consumer Preferences and Revenue Implications","fulltext":[{"header":"1. Introduction ","content":"\u003cp\u003eWireless technology has experienced unprecedented growth due to scientific advancements and innovation. Smartphones, which rely on wireless communication, have been instrumental in driving this expansion (Shi, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In 2020, smartphone users exceeded 3.6\u0026nbsp;billion globally, with projections reaching 5.7\u0026nbsp;billion by 2030 (Mobile Network Subscriptions Worldwide 2028, n.d.). This proliferation of smartphones has led to a corresponding surge in mobile data traffic, primarily driven by streaming video and other content consumption (Widdicks et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Research indicates that video accounts for approximately 75% of residential internet traffic, a trend expected to continue (Cass, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Additionally, wireless networks are increasingly handling traffic previously routed through wired connections, as modern wireless technology has advanced to the point where speed differentials have become negligible for most users (Chavan et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By 2020, global mobile data traffic reached approximately 49 exabytes per month, with continued growth anticipated.\u003c/p\u003e \u003cp\u003eDespite these advances, mobile networks face inherent challenges with rapid data consumption, which can result in reduced speeds and increased latency. While expanding infrastructure through additional base stations or cells offers one solution, it requires substantial and ongoing investment (Kulkarni et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Heterogeneous networks present an alternative approach, distributing traffic across different network types to optimize performance (Zafar et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Wi-Fi technology, operating via access points utilizing unlicensed 2.4 and 5 GHz frequency bands, provides a more cost-effective alternative to cellular networks that depend on expensive licensed bands (Chruszczyk et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). With most contemporary devices supporting both Wi-Fi and cellular connectivity, Wi-Fi offloading has emerged as a strategy to mitigate network congestion while reducing consumer data costs. Numerous studies have investigated the benefits of this approach (Cheung \u0026amp; Huang, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGovernmental bodies and mobile network operators (MNOs) have responded by expanding public Wi-Fi infrastructure to enhance coverage and alleviate cellular network burden. However, this expansion presents a potential revenue challenge for MNOs, as consumers may increasingly opt for free Wi-Fi over paid cellular data services (Oh et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). With mobile data services constituting a significant portion of MNO revenue, these operators must carefully evaluate public Wi-Fi from both beneficial and detrimental perspectives (Awwad, n.d.). MNOs must balance the advantages of reduced network load through Wi-Fi offloading against potential revenue losses as customers gravitate toward free Wi-Fi alternatives (Ayub et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). When assessing the financial viability of open Wi-Fi initiatives, MNOs must consider how decreased mobile data consumption impacts their revenue streams. Previous research has predominantly focused on MNO strategies, such as optimal access point placement and network cost-performance trade-offs (Hernandez et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Surprisingly, limited research has explored the financial implications of public Wi-Fi availability on consumer behavior, particularly how enhanced Wi-Fi speed or coverage might influence mobile data plan selection.\u003c/p\u003e \u003cp\u003eThe Malaysian government has demonstrated a strong commitment to expanding public Wi-Fi access as part of its digital inclusion and connectivity strategy. In 2020, Malaysia had established 18,000 public Wi-Fi locations, with plans to increase this number to 41,000 by 2022 (Hetting, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This ambitious initiative aligns with the government's broader objectives to strengthen digital infrastructure and ensure high-speed internet accessibility for all citizens.\u003c/p\u003e \u003cp\u003eThe concurrent deployment of 5G technology and public Wi-Fi in Malaysia exemplifies the potential for such initiatives to stimulate economic growth and enhance communication services. The Malaysian government seeks to understand how public Wi-Fi quality and coverage influence consumer behavior, particularly regarding mobile network operators (Mohamad Yazid et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While existing research has primarily addressed MNO perspectives, this study aims to provide comprehensive insights into both consumer and MNO viewpoints, evaluating how open Wi-Fi quality and availability affect communication costs. This research will clarify whether public Wi-Fi competes with or complements mobile networks, offering valuable guidance for developing strategic public Wi-Fi expansion, informing government policy, and enabling MNOs to optimize Wi-Fi offloading approaches. Ultimately, these findings will support the Malaysian government and MNOs in creating an integrated public Wi-Fi framework that addresses evolving consumer needs while maximizing benefits for all stakeholders.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Wi-Fi Congestion\u003c/h2\u003e \u003cp\u003eWireless devices have experienced a dramatic surge in demand, resulting in increased mobile network congestion (Clarke, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Wi-Fi offloading has emerged as an effective management strategy, redirecting portions of mobile data traffic to available Wi-Fi networks (Clarke, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Despite Wi-Fi's substantial capacity, quantifying the volume of offloaded data remains critical. Wi-Fi offloading has attracted significant research attention, with studies predominantly examining technical aspects, economic benefits for Mobile Network Operators (MNOs), and user advantages. Numerous investigations have explored the integration of Wi-Fi offloading with 5G technology (Ayub et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearchers have identified multiple advantages of Wi-Fi offloading, with delayed Wi-Fi offloading representing a notable approach. This strategy involves postponing non-time-sensitive downloads\u0026mdash;such as videos, cloud backups, and non-urgent applications\u0026mdash;until Wi-Fi connectivity becomes available (K. Lee et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Delayed offloading extends to mobile scenarios, such as when devices connect to Wi-Fi in moving vehicles, creating what researchers term \"drive-through Internet\" (Rosele et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Research indicates that Wi-Fi offloading can reduce mobile data traffic by 45\u0026ndash;65% (Poularakis et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). When users and vehicles delay mobile network usage by 30\u0026ndash;60 minutes and 10 minutes respectively, network congestion can decrease by 60\u0026ndash;80%. Some studies suggest that strategic implementation of delays could reduce congestion by more than 80% (Kamtam et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, Wi-Fi utilization demonstrates enhanced energy efficiency compared to mobile data, conserving 55\u0026ndash;65% of energy consumption when users accept 60-minute download delays (Ron \u0026amp; Lee, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While most research focuses on the technical processes of Wi-Fi offloading, less attention has been devoted to voluntary consumer behavior regarding the transition from mobile data to Wi-Fi (Husnjak et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral researchers have approached Wi-Fi offloading from an economic rather than technical perspective. Zhuo et al. developed an incentive framework offering discounts to users willing to delay their data consumption. However, their study concentrated on MNO-mandated offloading scenarios rather than user-initiated transitions motivated by cost savings\u0026mdash;which constitutes this study's focus. Lee et al. simulated the financial impact of Wi-Fi offloading on both MNOs and users across various mobile plans (unlimited, pay-per-use, tiered, and congestion-based). Using a two-stage game model, they demonstrated potential MNO revenue increases of 21\u0026ndash;152% and user savings of 73\u0026ndash;319%. Their research contains limitations: insufficient explanation of willingness-to-pay calculations and price elasticity implementations, along with assumptions regarding user tolerance for delays.\u003c/p\u003e \u003cp\u003ePoularakis et al. investigated optimal placement strategies for Wi-Fi access points (APs) and pricing effects on Wi-Fi data utilization. Their findings indicated that MNOs could reduce operational costs while offering users 50% price reductions through Wi-Fi offloading. However, previous research has predominantly examined Wi-Fi offloading from the MNO perspective, emphasizing revenue maximization rather than user perceptions of Wi-Fi speed and coverage. This paper will address this gap by measuring changes in user-initiated Wi-Fi usage based on Wi-Fi quality and coverage, and quantifying the corresponding reduction in mobile data network traffic.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Public Wi-Fi\u003c/h2\u003e \u003cp\u003ePublic Wi-Fi is typically offered without charge in various locations. In public transit systems and communal spaces, it is provided by mobile network operators (MNOs), national governments, and municipal authorities. Governments implement public Wi-Fi initiatives for multiple reasons: promoting universal internet accessibility, reducing telecommunications expenses, stimulating economic development, enhancing urban quality of life, and marketing their cities (Geerdts \u0026amp; Gillwald, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). As of 2020, Malaysia had established 743 Community WiFi hotspots nationwide under the Universal Service Provision (USP) programme (Fusion, n.d.). By 2022, the state of Selangor planned to expand its free WiFi Smart Selangor (WiFiSS) access points from 828 to 1,600, targeting areas such as low-cost housing developments, religious and healthcare institutions, community centers, recreational and transportation hubs, and state government facilities (Journal, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Malaysia is also implementing Wi-Fi 6 (IEEE 802.11ax), which provides high-speed connectivity for outdoor environments. Beyond government initiatives, businesses also deploy public Wi-Fi to enhance their online visibility, with cafes and restaurants offering free connectivity to attract customers.\u003c/p\u003e \u003cp\u003eMobile network operators establish public Wi-Fi networks to attract users while preventing mobile network saturation (Poularakis et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Qiu et al., n.d.). Their objective is to ensure customers experience seamless and convenient Wi-Fi connectivity. If MNOs cannot accommodate the escalating demand for mobile data, network quality deterioration may occur, resulting in reduced speeds and compromised service. To mitigate this risk, they redistribute portions of mobile traffic to Wi-Fi networks. This reduction in mobile data traffic simultaneously decreases energy consumption, thereby lowering MNO operational costs. Consequently, numerous MNOs have implemented Wi-Fi offloading strategies (Aijaz et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNevertheless, an inherent conflict of interest exists between MNOs and public Wi-Fi (He et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Poularakis et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ron \u0026amp; Lee, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). MNOs generate revenue from subscribers who pay based on data consumption, with premium plans offering substantial mobile data allowances. Higher user dependency on mobile networks increases willingness to pay, aligning with MNO financial objectives. However, when high-quality Wi-Fi is available without charge, the perceptible difference between mobile data and Wi-Fi diminishes. This reduces consumer incentive to purchase large data allowances, potentially undermining MNO profitability.\u003c/p\u003e \u003cp\u003eWith widespread Wi-Fi availability, consumers may reduce mobile data consumption or opt for more economical data plans instead of premium packages with extensive data limits (J. Lee et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Historically, when feature phones dominated the Malaysian market and smartphones had not yet achieved widespread adoption, major manufacturers such as Nokia, Sony Ericsson, and Samsung held prominent market positions. MNOs then generated significant revenue from mobile data charges. During this period, WAP (Wireless Application Protocol) usage incurred substantial costs, and had Wi-Fi been incorporated, users would have leveraged it instead, resulting in diminished MNO revenue. In the contemporary context, Wi-Fi proliferation presents both advantages and disadvantages for MNOs. The disadvantage manifests as potential reductions in mobile data usage or migrations to lower-cost plans, diminishing MNO profits. Conversely, the advantage lies in public Wi-Fi's capacity to alleviate network congestion, thereby enhancing overall service quality. Depending on implementation and usage patterns, Wi-Fi can function as either a complement or substitute for mobile networks. This study aims to analyze and determine this balance.\u003c/p\u003e \u003cp\u003ePublic WiFi has attracted academic interest; however, this attention has predominantly centered on user security and privacy rather than economic implications. For instance, Aswani et al. analyzed demographic factors influencing WiFi adoption, including age, gender, education, and income. Some studies have examined the economic considerations influencing Wi-Fi security decisions, while others have focused on technical and security-related challenges (Maimon et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Few researchers have attempted to quantify the economic value of public WiFi. One study evaluated it as an economic product but questioned whether public Wi-Fi should be classified as a public service (Van Den Velden \u0026amp; Sadowski, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To our knowledge, no comprehensive study has precisely measured the financial impact of public Wi-Fi. Consequently, our research will focus on calculating consumer savings on data expenses and quantifying MNO revenue losses attributable to public Wi-Fi expansion. Our findings will contribute to a more nuanced understanding of the relationship between public Wi-Fi availability and mobile data consumption patterns.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Contributions, Implications and Novelty","content":"\u003cp\u003eThis research provides a quantitative assessment of the revenue impact on Mobile Network Operators (MNOs) resulting from public Wi-Fi expansion, revealing a 0.28% revenue reduction per 100 Mbps increase in Wi-Fi speed and a 1.09% decline attributable to Wi-Fi deployment in public transportation systems. Through a mixed digit model applied to survey data from 2,000 respondents, we conducted a comprehensive analysis of how Wi-Fi range and quality influence consumer decision-making processes, while simultaneously documenting how Wi-Fi offloading affects network traffic patterns. The findings highlight the necessity for MNOs to develop sustainable investment strategies for mobile network infrastructure in an environment of expanding public Wi-Fi availability.\u003c/p\u003e \u003cp\u003eOur findings indicate significant cost efficiencies in Wi-Fi infrastructure deployment; however, these advantages present challenges to conventional MNO revenue models. To maintain profitability in this evolving landscape, MNOs should consider implementing innovative business approaches including dynamic pricing mechanisms, restructured data plans, and strategic collaborations with public networks to extend connectivity reach. From a policy perspective, governments should formulate regulatory frameworks that position public Wi-Fi as a complement to mobile networks rather than a substitute, ensuring balanced digital infrastructure development.\u003c/p\u003e \u003cp\u003eThe research demonstrates how enhanced Wi-Fi quality reduces consumer dependence on premium mobile data plans, creating a potential feedback loop affecting MNO investment decisions. Notably, excessive public Wi-Fi proliferation may disincentivize MNOs from investing in mobile network advancement, potentially resulting in service quality deterioration over time. This insight suggests the need for balanced public-private approaches to telecommunications infrastructure development.\u003c/p\u003e \u003cp\u003eThis study represents one of the first empirical measurements of the financial implications of public Wi-Fi deployment. While existing literature predominantly examines MNO strategies and technical implementation considerations, our research distinctively evaluates consumer preferences and willingness-to-pay under varying Wi-Fi conditions. By employing advanced econometric modeling techniques, we provide granular insights into consumer decision-making processes regarding mobile data consumption in environments with differential Wi-Fi accessibility.\u003c/p\u003e \u003cp\u003eFurthermore, the research contextualizes its findings within Malaysia's digital inclusion initiatives, offering valuable insights applicable to other emerging markets with expanding public Wi-Fi networks. This contextual relevance enhances the study's applicability to diverse geographical settings with similar telecommunications infrastructure objectives. Our multidimensional approach\u0026mdash;considering both economic impacts and behavioral responsesprovides a more comprehensive understanding of the complex interplay between public Wi-Fi expansion and mobile network economics than previous unidimensional analyses.\u003c/p\u003e"},{"header":"4. Methodology","content":"\u003cp\u003eThis research investigates network usage patterns, with particular emphasis on public Wi-Fi utilization. It examines Wi-Fi quality and availability factors, quantifies mobile data savings for users, and analyzes potential benefits for mobile network operators when traffic shifts to Wi-Fi networks. Understanding consumer preferences forms the foundation of this analysis.\u003c/p\u003e \u003cp\u003eConsumer selection behavior is analyzed using a mixed logit model, which accounts for the diversity of consumer preferences while acknowledging shared priorities among users. Unlike more rigid models that may inadequately represent real-world behavior, the mixed logit model accommodates variations in consumer preferences, thereby enhancing analytical flexibility and precision (Train \u0026amp; Sonnier, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). By eliminating restrictive assumptions and recognizing individual differences, the mixed logit model has become widely applied in forecasting consumer decisions and evaluating new product or service offerings (Kim, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Train \u0026amp; Sonnier, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe model quantifies consumer utility through a dual-component structure. Consumer advantage is conceptualized as comprising a deterministic element (V) that addresses quantifiable factors, and a stochastic element (E) that accounts for unforeseen variables influencing decision-making. This approach allows for a more nuanced understanding of how Wi-Fi quality and availability influence network selection behavior and subsequent impacts on mobile data consumption patterns. The expressions follow like:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{U}_{njt}=\\:{V}_{njt}+{e}_{njt}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eβ\u0026rsquo; \u003cem\u003en\u003c/em\u003e \u0026sim; \u003cem\u003eN\u003c/em\u003e(\u003cem\u003eb\u003c/em\u003e, \u0026sum;) \u003cem\u003eor ln\u003c/em\u003e β\u0026rsquo; \u003cem\u003en\u003c/em\u003e \u0026sim; \u003cem\u003eN\u003c/em\u003e(\u003cem\u003eb\u003c/em\u003e, \u0026sum;) (2)\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{U}_{njt}\\)\u003c/span\u003e \u003c/span\u003edenotes the utility that individual n derives from choosing option j at time t. The expression β_{n}'X_{njt} encompasses observable variables that affect decision-making, whereas ε_{njt} accounts for unobserved influences. In contrast to the conventional logit model, the mixed logit model posits that preference parameters are distributed, thereby offering greater flexibility in modelling consumer behavior. Due to the complex mathematical computations involved in resolving the model, we utilize Maximum Simulated Likelihood (MSL) for estimation. MSL simplifies the procedure by randomly creating preference parameters and then averaging them over many simulations to determine the total choice probability. Key Measures Two key measures derived from this model are Marginal Willingness to Pay (MWTP) and Relative Importance (RI). MWTP indicates how much a consumer is willing to pay for a one-unit improvement in a feature and is calculated as: MWTP_ k = -β_k / β_price where β_k represents the feature\u0026rsquo;s coefficient, and β_price is the price coefficient. RI measures how much a particular feature influences a consumer\u0026rsquo;s decision. It is calculated by assessing how much utility changes when a feature moves from its minimum to maximum level using the formula:\u003c/p\u003e \u003cp\u003e\u003cimg 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\" width=\"609\" height=\"47\"\u003e\u003c/p\u003e\u003cp\u003eThis percentage helps determine which features have the most impact on decision-making. By applying this approach, we gain deeper insights into consumer behavior regarding network usage, allowing mobile operators to optimize their services and pricing strategies accordingly. The Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the step-by-step methodology It follows a structured consumer decision analysis framework. It begins with a Consumer Engagement Analysis Funnel, assessing Wi-Fi quality, data conservation, financial benefits, and consumer preferences. These key decision factors integrate into a Flexible Modelling Approach, balancing diverse consumer preferences. The Consumer Utility Equation Methodology quantifies consumer utility using deterministic and stochastic components, considering preference distributions. A Cycle of Estimating Choice Probabilities involves estimating probabilities, generating random parameters, performing simulations, and averaging results. The Consumer Decision Analysis Process then derives key measures, computing Marginal Willingness to Pay (MWTP) and Relative Importance (RI). Finally, Data Analysis to Optimization refines insights, recommends strategies, and optimizes offerings for improved pricing and services.\u003c/p\u003e "},{"header":"5. Survey","content":"\u003cp\u003eIn this survey, virtual market conditions are studied with the cellular plans. The survey was conducted between age 21 and 50 group people consists of male and female in Malaysia. The survey was held in finance, IT sector, manufacturing, retail sectors in Malaysia. Among the whole survey around 2000 participation was there from various demographics. The survey was conducted on Poll-Pool. The communication cellular system for 3G,4G and 5G network, free call plans, call usage time, data usage are collected from different respondents in Poll-pool. Respondent characteristics are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAround 2,000 respondents reported using a smartphone plan. On average, they paid 164.92 MYR (37.10 USD) per month before discounts and 136.33 MYR (30.67 USD) after discounts. About 450 users (26.1%) had an unlimited data plan. Their average mobile data usage, excluding Wi-Fi, was 17 GB per month. The most common activities were web searches (40.5%), messaging and social media (18%), reading news (13%), and streaming live videos (5%).\u003c/p\u003e \u003cp\u003eIn terms of Wi-Fi usage, 70% of respondents indicated that they either use it \"quite frequently\" or \"a lot.\" When including those who rated their usage as \"average,\" the figure rises to 93.3%, suggesting that the majority of respondents regularly utilize Wi-Fi. When asked why they use Wi-Fi, 75% of respondents cited the high cost of cellular data as a significant burden (multiple responses allowed). About 20% respondents reported sudden Wi-Fi disconnections or unavailability, while 21.7% mentioned that there is poor coverage. Around 15% had trouble entering passwords in public places, and another 15% faced slow speeds or connection errors. In total, about 35% of the complaints were about slow Wi-Fi. Around 70% would use Wi-Fi more if the speed improved. Meanwhile, 9.3% would rely less on Wi-Fi if mobile data costs were lower, and 16.8% said having more mobile data would affect Wi-Fi use. This shows that price and data limits play a big role in Wi-Fi usage. Most public Wi-Fi use (70%) occurred in residential areas. followed by public transportation (13%) and outdoor spaces, such as plazas, beaches, and mountains (9%).\u003c/p\u003e \u003cp\u003eThe choice experiment method studies what consumers prefer by showing different virtual product features and collecting their responses (Green, 1978). In this study, a virtual mobile plan was shown, and choices were analyzed. This helps understand how consumers decide on mobile plans and how Wi-Fi speed and coverage affect their choices and willingness to use Wi-Fi.\u003c/p\u003e \u003cp\u003eThis research examined various factors, including mobile data speed, Wi-Fi speed, the number of Wi-Fi hotspots, and the monthly cost of a phone plan. A 2018 report by the Ministry of Science and ICT assessed network quality and found that LTE had an average download speed of 150.68 Mbps, public Wi-Fi reached 354.07 Mbps, and subway Wi-Fi averaged 59.33 Mbps. The study concluded that mobile networks had an average speed of 150 Mbps, public Wi-Fi operated at 350 Mbps, and Wi-Fi on subways ran at 59 Mbps. In the 2020 iteration of the same survey, the fastest MNO's 5G download speed reached 795.57 Mbps, while the slowest MNO\u0026rsquo;s LTE download speed was 109.47 Mbps. Theoretical speeds for 5G are expected to support up to 7 Gbps, and Malaysia MNOs, alongside local governments, have also introduced a Wi-Fi support plan to provide speeds of up to 1.2 Gbps using the IEEE 802.11ax standard. Consequently, the speed parameters in this study were defined in alignment with anticipated improvements from future technological advancements.\u003c/p\u003e \u003cp\u003eThe monthly plan includes both cellular data amounts and pricing to see how Wi-Fi speed and coverage changes affect mobile plans and data use. This helps understand how consumers adjust their plans and choose to use Wi-Fi instead of mobile data. The plan options were based on prices and data offers from the top three major mobile providers in Malaysia. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the factors and options studied in this research.\u003c/p\u003e \u003cp\u003ePresenting all the possibilities to respondents would have been overwhelming and would have made it more difficult for them to digest the options, especially Given the numerous possible combinations of attributes in this study (4 \u0026times; 4 \u0026times; 3 \u0026times; 4\u0026thinsp;=\u0026thinsp;192), there are a total of 192 different ways these factors can be arranged. Consequently, the number of options was lowered to 24 using a fractional factorial design. Each responder was required to evaluate eight sets of choices, each consisting of three possibilities. To ensure the accuracy of responses, the same question was presented twice to each participant, with any inconsistencies resulting in the exclusion of those responses. Only 13,142 choice sets were used in the final analysis since 2,690 (17.0%) of the 15,832 total choice sets were deemed to contain inconsistent replies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics and Tiers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTiers\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUser Experienced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 Mbps / 400 Mbps / 1 Gbps / 2 Gbps.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCellular speed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWi-Fi speed (user-experienced)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 Mbps / 300 Mbps / 900 Mbps / 1500 Mbps\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlaces with Wi-Fi availability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidential (house, cafe, library, etc.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly rate plan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.74 MYR Per month (includes 3 GB of data).\u003c/p\u003e \u003cp\u003e152.91MYR per month Per month (includes 10 GB of data).\u003c/p\u003e \u003cp\u003e214.07 MYR (data volume provided: 100 GB)\u003c/p\u003e \u003cp\u003e397.47 MYR Per month (includes 300 GB of data).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"6. Results","content":"\u003cp\u003eThe analysis was done using the mixed logit method. For the MSL simulation, 100 random samples were taken multiple times. The model resulted in the following equation.\u003c/p\u003e \u003cp\u003eModel 1 : \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{U}_{nj}=\\:{\\gamma\\:}_{1}{Z}_{cellular}+\\:{\\gamma\\:}_{2\\:}{Z}_{wi-fi}+\\:{\\gamma\\:}_{3}{D}_{bus-metro}+\\:{\\gamma\\:}_{4}{D}_{public}+\\:{\\gamma\\:}_{5}{Z}_{plan\\:}\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{e}_{nj}\\)\u003c/span\u003e\u003c/span\u003e (4)\u003c/p\u003e \u003cp\u003eModel 2: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{U}_{nj}\\)\u003c/span\u003e\u003c/span\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{1}{Z}_{cellular}+\\:{\\gamma\\:}_{2\\:}{Z}_{wi-fi}+\\:{\\gamma\\:}_{3}{D}_{bus-metro}+\\:{\\gamma\\:}_{4}{D}_{public}+\\:{\\gamma\\:}_{5}{Z}_{plan\\:\\:}+\\:{\\gamma\\:}_{6}{Z}_{wi-fi\\:\\times\\:\\:plan\\:}\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{7}{Z}_{bus\\:and\\:metro\\:\\times\\:plan\\:}\\)\u003c/span\u003e\u003c/span\u003e+ \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{8}{Z}_{public\\:\\times\\:plan\\:}\\)\u003c/span\u003e\u003c/span\u003e+ \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{e}_{nj}\\)\u003c/span\u003e\u003c/span\u003e (5)\u003c/p\u003e \u003cp\u003eIn model 1, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{cellular}\\)\u003c/span\u003e\u003c/span\u003e indicates the cellular communications transmission speed, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{wi-fi}\\)\u003c/span\u003e\u003c/span\u003e is the Wi-Fi speed and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{bus-metro}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{public}\\)\u003c/span\u003e\u003c/span\u003e are some of the dummy variables corresponding to the Wi-Fi supported places. These variables have a digital logic.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\" widht=\"100%;\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimation Results of Mixed Logit model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCellular speed \u003c/p\u003e \u003cp\u003eWi-Fi Speed \u003c/p\u003e \u003cp\u003eWi-Fi (Bus \u0026amp; Metro)\u003c/p\u003e \u003cp\u003eWi-Fi (Public)\u003c/p\u003e \u003cp\u003eMonthly Rate Plan\u003c/p\u003e \u003cp\u003eCellular Speed\u003c/p\u003e \u003cp\u003eWi-Fi Speed\u003c/p\u003e \u003cp\u003eWi-Fi(Bus \u0026amp; Metro)\u003c/p\u003e \u003cp\u003eWi-Fi(public)\u003c/p\u003e \u003cp\u003eMonthly Rate Plan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003cp\u003e 0.83\u003c/p\u003e \u003cp\u003e 0.11\u003c/p\u003e \u003cp\u003e 0.60\u003c/p\u003e \u003cp\u003e \u0026minus;\u0026thinsp;0.69\u003c/p\u003e \u003cp\u003e 0.15\u003c/p\u003e \u003cp\u003e 0.03\u003c/p\u003e \u003cp\u003e 1\u003c/p\u003e \u003cp\u003e 0.75\u003c/p\u003e \u003cp\u003e 0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003cp\u003e0.03\u003c/p\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e0.06\u003c/p\u003e \u003cp\u003e0.05\u003c/p\u003e \u003cp\u003e0.05\u003c/p\u003e \u003cp\u003e0.14\u003c/p\u003e \u003cp\u003e0.07\u003c/p\u003e \u003cp\u003e0.06\u003c/p\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.0%\u003c/p\u003e \u003cp\u003e12.1%\u003c/p\u003e \u003cp\u003e0.08%\u003c/p\u003e \u003cp\u003e6%\u003c/p\u003e \u003cp\u003e69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003cp\u003e1.71\u003c/p\u003e \u003cp\u003e0.44\u003c/p\u003e \u003cp\u003e0.56\u003c/p\u003e \u003cp\u003e\u0026ndash;0.61\u003c/p\u003e \u003cp\u003e0.23\u003c/p\u003e \u003cp\u003e 0.29\u003c/p\u003e \u003cp\u003e 1.02\u003c/p\u003e \u003cp\u003e 0.7\u003c/p\u003e \u003cp\u003e 0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003cp\u003e0.08\u003c/p\u003e \u003cp\u003e0.11\u003c/p\u003e \u003cp\u003e0.13\u003c/p\u003e \u003cp\u003e0.03\u003c/p\u003e \u003cp\u003e0.05\u003c/p\u003e \u003cp\u003e0.07\u003c/p\u003e \u003cp\u003e0.07\u003c/p\u003e \u003cp\u003e0.08\u003c/p\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003cp\u003e15%\u003c/p\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003cp\u003e3.5%\u003c/p\u003e \u003cp\u003e39%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe table presents coefficient estimates, standard errors (S.E.), and relative importance (RI) values for two models examining consumer preferences for various network attributes. The coefficient section quantifies how each attribute influences utility, while the variance section illustrates preference heterogeneity across individuals.\u003c/p\u003e \u003cp\u003eIn Model 1, cellular speed demonstrates a positive coefficient of 0.57 (S.E.=0.02) with a relative importance of 10%, indicating that faster cellular networks positively influence consumer choice while accounting for approximately one-tenth of the decision-making process. Wi-Fi speed exhibits a stronger effect with a coefficient of 0.83 (S.E.=0.13) and an RI of 12.1%, suggesting consumers place greater value on Wi-Fi speed improvements compared to cellular network enhancements.\u003c/p\u003e \u003cp\u003eWi-Fi availability in transportation systems (buses and metro) shows a positive influence (coef.=0.71, RI\u0026thinsp;=\u0026thinsp;8%), while Wi-Fi in public spaces emerges as particularly impactful (coef.=0.95, RI\u0026thinsp;=\u0026thinsp;15%), representing the most influential positive attribute in this model. The monthly rate plan coefficient of -0.69 (RI\u0026thinsp;=\u0026thinsp;6.9%) confirms that higher costs reduce utility, though this factor plays a relatively moderate role in overall preference formation.\u003c/p\u003e \u003cp\u003eExamining variance parameters in Model 1 reveals substantial heterogeneity in consumer valuation of public Wi-Fi (variance\u0026thinsp;=\u0026thinsp;0.75), indicating significant individual differences in how consumers value this attribute. This suggests diverse consumer segments with varying appreciation for public Wi-Fi availability.\u003c/p\u003e \u003cp\u003eModel 2 reveals notable shifts in attribute importance. Cellular speed demonstrates reduced impact (coef.=0.30, S.E.=0.03, RI\u0026thinsp;=\u0026thinsp;7%) compared to Model 1. Conversely, Wi-Fi speed gains prominence (coef.=1.13, S.E.=0.21, RI\u0026thinsp;=\u0026thinsp;15%), indicating stronger consumer emphasis on Wi-Fi performance. While Wi-Fi availability in transportation (coef.=0.61, RI\u0026thinsp;=\u0026thinsp;5%) and public spaces (coef.=0.48, RI\u0026thinsp;=\u0026thinsp;13%) maintain positive influences, their relative importance differs from Model 1.\u003c/p\u003e \u003cp\u003eThe most striking difference appears in the monthly rate plan's role, which maintains a negative coefficient (-0.65, S.E.=0.03) but exhibits a dramatically increased relative importance of 39%, suggesting that cost considerations dominate consumer decision-making in this model specification. Variance patterns also shift significantly, with Wi-Fi speed showing higher preference heterogeneity (variance\u0026thinsp;=\u0026thinsp;0.29), while monthly rate plan variance remains minimal (0.02), indicating more uniform cost sensitivity across consumers.\u003c/p\u003e \u003cp\u003eThe comparison between Models 1 and 2 highlights how consumer preferences can vary substantially under different modeling assumptions. Model 1 emphasizes the value of public Wi-Fi availability, while Model 2 demonstrates stronger consumer emphasis on Wi-Fi speed and, most prominently, price sensitivity. The variance parameters further illustrate that certain attributes (particularly public Wi-Fi in Model 1 and Wi-Fi speed in Model 2) elicit diverse consumer responses, while others (especially monthly rate plans in Model 2) generate more consistent reactions.\u003c/p\u003e \u003cp\u003eThese findings carry important strategic implications for service providers, suggesting the need for segmented approaches that recognize heterogeneous preferences regarding network costs, speeds, and Wi-Fi availability. Network operators should consider developing differentiated offerings that address these varying consumer priorities while acknowledging the dominant role of price sensitivity in overall decision-making processes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates how consumers' Willingness to Pay (WTP) for mobile data plans decreases when they have access to different types of Wi-Fi options, with each line representing a distinct scenario: faster Wi-Fi speeds, Wi-Fi on buses and metros, and Wi-Fi availability anywhere. Along the horizontal axis, monthly communication expenses (in 1,000 KRW) increase from left to right, while the vertical axis shows how much users would reduce their WTP (also in 1,000 KRW) if provided with the respective Wi-Fi option. All three lines slope upward, indicating that individuals paying higher monthly costs are more inclined to reduce those expenses if given a reliable Wi-Fi alternative.\u003c/p\u003e \u003cp\u003eAmong the three scenarios, faster Wi-Fi speed (represented by the top line) consistently produces the largest reduction in WTP, suggesting that users place a premium on speed and reliability. As their monthly expenses increase, they perceive even greater value in shifting away from expensive data plans when a fast Wi-Fi option is available. In comparison, providing Wi-Fi on buses and metros (the middle line) also leads to a notable drop in WTP, but not as significantly as improving Wi-Fi speed. This result highlights the importance of connectivity during commutes, though it does not outweigh the benefit of having universally faster Wi-Fi.\u003c/p\u003e \u003cp\u003eThe \"Wi-Fi Anywhere\" scenario (the bottom line) shows a smaller overall effect on WTP reduction, implying that while broad coverage is appealing, it does not match the perceived benefit of either higher speeds or targeted availability in high-use locations like public transportation. In practical terms, these findings suggest that mobile subscribers with higher monthly expenditures are the most responsive to improvements in Wi-Fi quality or coverage.\u003c/p\u003e \u003cp\u003eFrom a service provider's perspective, fast, reliable Wi-Fi represents the strongest driver of users potentially downgrading their data plans, especially among higher-spending customers. Meanwhile, coverage in public transportation and broad, everyday access can still motivate users to reduce their mobile spending, but not to the same extent as speed improvements. Policymakers and telecommunications companies can leverage these insights to develop targeted strategies\u0026mdash;such as upgrading open Wi-Fi networks, expanding coverage in critical transit areas, or refining pricing models\u0026mdash;to balance consumer savings with sustainable revenue streams and effective network management.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis bar chart, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e compares users' willingness to pay (WTP) for mobile data across three different Wi-Fi scenarios\u0026mdash;\"Wi-Fi speed up,\" \"Wi-Fi speed up +\u0026thinsp;Bus \u0026amp; Metro,\" and \"Wi-Fi speed up +\u0026thinsp;public\"\u0026mdash;at various monthly communication expense levels. Each cluster of three bars represents a specific expense category on the x-axis, while the y-axis displays the WTP in units of 1,000 KRW.\u003c/p\u003e \u003cp\u003eThe \"Wi-Fi speed up\" bars (blue) consistently appear tallest within each group, demonstrating that enhanced Wi-Fi speed has the strongest influence on consumers' willingness to pay for mobile plans. The \"Wi-Fi speed up +\u0026thinsp;Bus \u0026amp; Metro\" bars (green) consistently occupy the middle position, indicating that extending high-speed Wi-Fi to public transportation provides moderate value but doesn't match the impact of universal speed improvements. The \"Wi-Fi speed up +\u0026thinsp;public\" scenario (orange) generally shows the lowest WTP across expense categories, suggesting that basic open Wi-Fi availability generates less perceived value than more targeted enhancements.\u003c/p\u003e \u003cp\u003eThe data reveals a clear correlation between monthly communication expenses and sensitivity to Wi-Fi improvements. As expense values increase along the x-axis, the bar heights for each scenario also rise, reflecting greater potential savings or higher perceived value among users with higher baseline spending. This progressive increase is particularly pronounced for the Wi-Fi speed improvement scenario, highlighting how consumers with premium mobile plans place even greater value on having access to fast, reliable wireless connections.\u003c/p\u003e \u003cp\u003eThese findings offer actionable insights for telecommunications operators and policymakers seeking to enhance consumer value or optimize service offerings. The data suggests prioritizing Wi-Fi speed improvements would yield the greatest impact on consumer behavior, while strategic coverage expansions in transportation networks represent a valuable secondary focus. This approach would be especially effective for targeting higher-spending consumers, who demonstrate the greatest responsiveness to Wi-Fi enhancements. Implementing such prioritized improvements could help balance consumer cost savings with sustainable service delivery models in evolving telecommunications markets.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis study highlights the evolving relationship between public Wi-Fi and cellular data services, revealing both opportunities and challenges for consumers and Mobile Network Operators (MNOs). While mobile data offers greater versatility by enabling seamless connectivity on the move, the expansion of high-quality public Wi-Fi\u0026mdash;especially with advancements such as IEEE 802.11ax\u0026mdash;presents a viable alternative in many settings. Governments, like Malaysia's, are already exploring initiatives to improve public access, aiming to lower communication costs and support underserved communities. However, this expansion also introduces competition that may reduce MNO revenues and potentially limit future investments in mobile network infrastructure.\u003c/p\u003e \u003cp\u003eDespite these concerns, public Wi-Fi also offers benefits to MNOs through Wi-Fi offloading, which reduces network congestion and operational costs. Many MNOs have adopted such strategies, although they face trade-offs in the form of decreased cellular data consumption. The study finds that consumers are highly sensitive to mobile plan pricing and are more likely to shift usage toward improved public Wi-Fi, further challenging MNOs\u0026rsquo; revenue streams.\u003c/p\u003e \u003cp\u003eImportantly, the research suggests that public Wi-Fi should serve as a complementary service rather than a replacement for cellular data. This balanced approach allows consumers to enjoy affordable access while preserving the financial sustainability of private network operators. Policymakers, MNOs, and regulatory bodies must work together to develop frameworks that encourage infrastructure investment, maintain service quality, and protect consumer interests.\u003c/p\u003e \u003cp\u003eThe study also acknowledges several limitations, including a lack of focus on technical challenges such as security risks, network reliability, and battery consumption. It primarily considers voluntary Wi-Fi offloading, omitting the effects of operator-enforced switching policies. Furthermore, value-added features of mobile plans\u0026mdash;such as bundled entertainment or premium services\u0026mdash;may significantly influence user decisions but were not explored in depth.\u003c/p\u003e \u003cp\u003eFuture research should expand on these aspects, incorporating real-world usage data and consumer insights to better understand behavioral trends and network dynamics. As digital connectivity continues to evolve, a thoughtful, evidence-based approach is essential to ensure equitable, efficient, and sustainable integration of Wi-Fi and mobile networks for all stakeholders.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study does not involve any human participants or animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMS and DA\u003c/strong\u003e conceptualized the study. Methodology was developed by \u003cstrong\u003eMS, MQA, and AHM\u003c/strong\u003e. Formal analysis was conducted by \u003cstrong\u003eHMZ and AHM\u003c/strong\u003e, while investigation was performed by \u003cstrong\u003eHMZ\u003c/strong\u003e. Data curation was handled by \u003cstrong\u003eMS\u003c/strong\u003e. The original draft was written by \u003cstrong\u003eMQA\u003c/strong\u003e. Manuscript review and editing involved \u003cstrong\u003eMS, MQA, HMZ, and DA\u003c/strong\u003e. Supervision and overall guidance was provided by \u003cstrong\u003eDA\u003c/strong\u003e. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the support provided by Al-Furat Al-Awsat Technical University, particularly the Department of Communication Engineering, for fostering an environment conducive to research and academic excellence. We extend our sincere appreciation to the faculty and staff for their guidance and assistance throughout this study. This research reflects the collaborative efforts and dedication of all those involved, to whom we express our deepest gratitude.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAijaz, A., Aghvami, H., \u0026amp; Amani, M. (2013). A survey on mobile data offloading: Technical and business perspectives. \u003cem\u003eIEEE Wireless Communications\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(2), 104\u0026ndash;112. IEEE Wireless Communications. https://doi.org/10.1109/MWC.2013.6507401\u003c/li\u003e\n\u003cli\u003eAwwad, A. (n.d.). \u003cem\u003eThe impact of Over The Top service providers on the Global Mobile Telecom Industry: A quantified analysis and recommendations for recovery\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eAyub, A., Jangsher, S., Butt, M. M., Maud, A. R., \u0026amp; Bhatti, F. A. (2021a). 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An Advanced Strategy for Addressing Heterogeneity in SDN-IoT Networks for Ensuring QoS. \u003cem\u003eApplied Sciences\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(13), Article 13. https://doi.org/10.3390/app13137856\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Public Wi-Fi, Mobile Network Operators, Wi-Fi Offloading, Consumer Behaviour, Data Plan Adoption, Revenue Impact, Digital Connectivity, Wireless Infrastructure","lastPublishedDoi":"10.21203/rs.3.rs-6619712/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6619712/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid expansion of public Wi-Fi has reshaped digital connectivity by providing consumers with cost-effective internet access. However, this growth presents economic challenges to mobile network operators (MNOs) by potentially disrupting their traditional revenue models. This study investigates the economic impact of public Wi-Fi proliferation on MNOs, focusing on consumer preferences, data usage behaviour, and implications for network profitability. Employing a mixed logit model, we analyse survey data from 2,000 respondents in Malaysia to assess how variations in Wi-Fi quality, availability, and affordability affect consumer choices regarding mobile data plans. Results indicate that improved public Wi-Fi access significantly reduces consumer reliance on cellular data, leading to a measurable decline in subscriptions to high-cost mobile plans. Specifically, a 100 Mbps increase in public Wi-Fi speed is associated with a 0.28% decrease in MNO revenue, while comprehensive Wi-Fi deployment across public transport systems contributes to a 1.09% revenue decline. Although Wi-Fi offloading reduces network congestion and infrastructure expenditure for MNOs, it also intensifies competition, prompting the need for innovative and adaptive business strategies. The findings suggest that public Wi-Fi should be positioned as a complementary service rather than a substitute for cellular networks. Strategic collaboration between governments and MNOs is essential to balance digital inclusivity with sustainable commercial models, ensuring continued investment in next-generation wireless infrastructure. Future research should further explore the dynamics between technological advancement, regulatory environments, and shifting consumer behaviours to support a resilient and equitable digital ecosystem.\u003c/p\u003e","manuscriptTitle":"The Impact of Public Wi-Fi Expansion on Mobile Network Operators: Analysing Consumer Preferences and Revenue Implications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 10:38:13","doi":"10.21203/rs.3.rs-6619712/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"00d4e6bb-dc5b-47d1-8f7d-2bc65ff3fbc5","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T15:21:11+00:00","versionOfRecord":{"articleIdentity":"rs-6619712","link":"https://doi.org/10.22266/ijies2026.0131.15","journal":{"identity":"international-journal-of-intelligent-engineering-and-systems","isVorOnly":true,"title":"International Journal of Intelligent Engineering and Systems"},"publishedOn":"2026-01-01 00:00:00","publishedOnDateReadable":"January 1st, 2026"},"versionCreatedAt":"2025-05-16 10:38:13","video":"","vorDoi":"10.22266/ijies2026.0131.15","vorDoiUrl":"https://doi.org/10.22266/ijies2026.0131.15","workflowStages":[]},"version":"v1","identity":"rs-6619712","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6619712","identity":"rs-6619712","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}