Changing Grievance-Reporting Modes in India's National Consumer Helpline: Evidence from Selected E-Commerce Complaints, 2021-2025

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1. Introduction

The expansion of online commerce has changed the way consumers search and buy and engage with goods and services. Although digital commerce brings more benefits, including convenience and access, it also produces issues like misleading, product defects, service breakdowns, and consumer dissatisfaction handling effectively. These are very important points that show the necessity of having mechanisms in place that people can actually use for making complaints, that will listen to what they say, and that can operate via different communication channels (Chawla & Kumar, 2022) [7]. India's Consumer Protection Act, 2019 and the Consumer Protection (E-Commerce) Rules, 2020 made major changes to the regulatory consumer protection system that was previously in existence as the market continues to go digital (Government of India, 2019; Department of Consumer Affairs, 2020) [14, 9]. One of the key features of the system is the National Consumer Helpline (NCH) managed by the Department of Consumer Affairs that gives an avenue for consumers to make complaints before a case is taken to the courts. The Helpline has over the years become a multi-channel interface consisting of, among others Web Phone, the NCH App UMANG SMS, WhatsApp, and others methods to reach out. Meanwhile, the Department of Consumer Affairs announced in December 2022 its plan to integrate the Helpline with Whatsapp. Also, the unveiling of NCH 2. 0 in December of the same year 32023 was another major technological development of the mechanism of resolving complaints (Department of Consumer Affairs, 2022; Press Information Bureau, 2023) [10, 25]. Such legal and administrative changes are essential aspects of understanding the trends in channel of complaint submission utilization that some of the channels might have been introduced after the research period started. The fact that consumers can choose from many ways of reporting their problems doesn't mean they'll spread their complaints evenly across them or use each reporting mode at the same rate all the time. Different research strands have looked together at factors affecting the likelihood that consumers will file a complaint, their choice of a complaint channel, online systems allowing complaints to be submitted by consumers, and online dispute resolution. These works have demonstrated that a person's choice of channel and his or her behavior can be influenced not only by the characteristics of the channel or channels available, but also by consumer or situational factors (Robertson, 2012; Frasquet, Ieva, & Ziliani, 2019; Miquel-Romero, Frasquet, & Molla-Descals, 2020; Wolf & Steul-Fischer, 2023) [27, 12, 13, 22]. Even so, the study of administrative records about complaints has not received the attention it merits. One of the very few works which has examined data from Indian National Commission for Consumer Disputes (NCH) has only identified complainants' patterns, the regions of complaint origination, and types of complaints (Faishal & Longkumer, 2026) [11, 21], not addressing what happened to the complaint distribution among formal reporting modes. This clarification is needed as even though administrative complaint records allow us to trace how complaints made it into the formal system, they won’t tell us why a person chose to come forward by the particular mode. Shifts in the share of the different reporting modes could indicate change in consumers behavior or even more so change in the channel availability itself, change in the channel's technological infrastructure, change in awareness, change in system capacity or changes to others factors of which the combination is hard to disentangle just through aggregated records. So, the paper analyzes the observed utilization of grievances-reporting modes, not the consumer preferences that it might indirectly reflect. To begin with, the research presents how the distribution of selected complaints related to e-commerce that were lodged to the NCH has changed over time from 2021 to 2025. The dataset used for the research are officially published complaint records about grievances over misleading advertisements or the sale of counterfeit/substandard products.

Apart from looking at the mode of reporting and the year, the article has a further objective. The researchers will carry out statistical change’s assessment of consistent reporting modes and analyze their reporting-mode composition on a year by basis. The work wants to demonstrate how access to the national consumer-redress mechanism changed during a time period which included both major institutional and technological changes. As a side effect, it also works as a source of complementary information to previously conducted NCH work that was mainly about complaint volume, geographic concentration, and grievance composition. The work will focus on the mode of registration of formal complaints that consumers are directed to.

2. Literature Review and Research Gap

2.1. Literature Review

E-commerce has grown fast, and so has the need for solid consumer protection and a system for grievance redress. Consumers are continuously facing issues like misleading ads, poor quality products, service breakdowns, and post-delivery disagreements. Chawla and Kumar (2022) [7] in the Indian setting stress consumer protection's necessity in the world of digital commerce. Consumer awareness studies point to the significance of having a grievance redress mechanism that is a channel that one can easily use to claim one's rights. Grievance records from the administration are quite handy to get an idea of what kind of complaints were actually taken and processed by the formal redress system. The data from such records can't give an accurate view of all consumer problems as they represent a very narrow set of reported complaints, a fact Balasubramaniam, Sane, and Sharma (2023) [4] have confirmed to an extent that there might be a sizeable portion of consumer grievances that never made it to formal reporting systems. This fact becomes even more important when one uses administrative complaint data to understand consumer redress patterns.

An important area of investigation on this is about how consumer complaining and complaining management have changed with digitalization. Studies on electronic complaining state that consumers have become more capable to express their dissatisfaction and have more opportunities to get in touch with companies thanks to digital environments (Balaji, Jha, & Royne, 2015; Beh, Sajtos, & Cao 2020) [3]. Apart from the expression of complaints, the social-media and digital complaint channels may also lead differences with the traditional ones as far as the communication processes around the complaints are concerned, for instance (Sugathan, Rossmann, & Ranjan, 2018; Arora, Chakraborty, & Narayanamurthy, 2025; Yin & Sadowski, 2024) [30, 2]. On top of that it is pointed out that a combination of accessibility and responsiveness characterizes digital channels of redress that are studied through the lens of online complaint handling and webcare (Sreejesh, Paul, & Unnithan, 2022) [29]. Even after a complaint is made, consumers still depend on post-complaint experiences and service recovery partly for judging the outcomes of a service failure (Rohden & de Matos, 2022; Das et al., 2026) [28, 8].

The matter of complaint-channel is mainly relevant to this study. Convenience and channel characteristics have been shown by Robertson (2012) [27] to affect complaints resolution mechanism preference. Also, research on multichannel retailers indicates that the complaint channel usage patterns might differ across the channels and the stages of the complaint process (Frasquet, Ieva, & Ziliani 2019, 2021) [12, 13]. Miquel-Romero, Frasquet, and Molla-Descals (2020) [22] further underpin the role of the channel context in post-buy complaint management. Sugathan, Rossmann, and Ranjan (2018) [30] however, have identified the differences in consumer perception in complaint handling across the social-media and traditional environment. Other studies suggest that the way consumers respond to complaints can be linked to certain personal traits (Jasper & Waldhart, 2013; Berry et al., 2018) [16, 6]. In an attempt to put together the whole matter of this broader literature Wolf and Steul-Fischer (2023) pinpoint factors that are related to channels as also those that are connected to consumers and situational ones which influence channel selection decisions in a multi-channel setting. Formal complaints and dispute resolution procedures have been considered as mechanisms for getting relief through complaints by complaining. Research also shows that they provide insight into the situation. For instance, Ong and Teh (2016) [23] looked at consumer expectations about dispute resolving procedures for online sales whereas Abedi, Zeleznikow, and Bellucci (2019) [1] focused on the development of trust in users towards online mediation services. A different approach is to consider formalization through online complaint resolution. A study, for example, Rana et al. (2016) [26], who had a look at consumers' adoption of an online public grievance-redress system in India, could lead. Meanwhile, consumer mediation is one of the topics in a current discussion of consumer satisfaction, online justice, and the quality of electronic services of consumers' complaint sites, which was also touched upon by Zhao & Chen, Patel et al. (2025) [24], and Kulshrestha (2026) [17] (2023) (Zhao & Chen, 2024; Patel et al., 2025; Kulshrestha, 2026) [34, 17, 24]. Making use of more than thousands of complaint tickets (that is, administrative complaints) Kumar shows further the usefulness of grievance forms not only as a tool for revealing consumers' real-life problems with official institutions but also on revealing that there is a need for consumer grievances. A particular interest has arisen in NCH, which means the "National Consumer Helpline (NCH)" administrative data. With the help of this public grievance database, they were able to analyze seasonal variations in the number of people who filed a complaint, the proportion of complainants at different places throughout India, as well as categorizing complainants of e-commerce as a separate group of NCH complaints. These records of complaints have been recognized as not only capable of painting a map of complaints by their temporal, geographical and categories of complaint features but also for uncovering an additional element of addressing consumer complaints: where the complaint has originated. Truth is the two methods are so distinct is one of the reasons for looking into the use of different grievance reporting ways. As the consumer's preferences or motivations can be known, whereas examining the use of the different reporting modalities reveals the consumers' actual behavior. This is so because the combined data from the administrative records reflect the submissions but not necessarily the motivations. The change brought about by this method is that the use of grievance databases over time will allow us to follow the trend of usage modes of different reporting modalities in this case.

2.2. Research Gap

The literature in complaint handling has been rich so far, but most focus is on determinants of consumers' choice of a complaint channel, consumers' characteristics, perceived convenience, service quality, or behavioural intents (Robertson, 2012; Frasquet, Ieva, & Ziliani, 2019, 2021; Wolf & Steul-Fischer, 2023) [27, 12, 13]. Research on digital grievance platforms has mainly focused on factors of technology acceptance, consumer satisfaction trust service recovery, and online dispute resolution (Rana et al (Rana et al., 2016; Abedi, Zeleznikow, & Bellucci, 2019; Kumar et al., 2023) [26, 1, 18]. 2016; Abedi, Zeleznikow, & Bellucci, 2019; Kumar Singh Kumar, & Sauraw, 2023) [1, 18]. While such research contributes Really to the understanding of consumer complaint behaviour, there is a dearth of studies that have actually investigated the longitudinal changes in the distribution of grievances to multi-point reporting channels in a large public consumer redress system. India's national consumer helpline, National Consumer Helpline (NCH), is one such system where this gap needs filling through further research. The only existing work that used NCH's administrative records looked at complaint levels and geographic concentration, and also grievance composition (Faishal & Longkumer, 2026) [11, 21]. At the same time administrative complaint records have been utilized more widely to analyze the public services- citizen relationship (Kumar, 2025) [19]. Yet, how a complaint gets to the NCH through which mode of channel has never been explored yet. This is one significant way of understanding consumer access to the formal redress system. So longitudinal study of such a factor is crucial, because if the share of Web telephone mobile app, messaging service or other channels change relative to each other, such trends may indicate important shifts in the grievance system usage pattern.

What further complicates the situation is that reporting channels themselves might change during the period of observation. So any shifts in the relative usage recorded should not blindly be interpreted as a change in consumers' choice at individual level. We attempt to handle this issue by differentiating the utilization of available complaining channels from the motivations of the complainants at consumer level. And by considering the availability of differing channels we conduct the statistical analysis. So this study, using NCH database records for selected electronic transactions during 2021, 2025, moves the question from the existing focus in the NCH studies of how many complaints were made, where they were from, and what they were about, to one that also asks about how those complaints were lodged in the formal redress system and how that method was distributed over the time period.

2.3. Research Questions

The overall objective of this study is to examine how distribution of the selected e commerce grievances across National Consumer Helpline reporting modes changed between 2021 and 2025, while also accounting for differences in the mode availability during the study period. Accordingly, the study shows the following research questions:

RQ1: How did the annual distribution of selected e-commerce grievances across NCH reporting modes change between 2021 and 2025?

RQ2: Among the clearly defined reporting modes that were established throughout the study period, was reporting mode distribution associated with year, and which year mode combinations contributed most strongly to the observed association?

RQ3: Did a substantial difference in reporting mode distribution persist between 2024 and 2025 when the major named reporting modes, including WhatsApp, were available in both years?

3. Research Methodology

3.1. Research Design and Data Source

In order to carry out this study, a quantitive longitudinal design was chosen to work with administrative data that was not collected for our purpose. We will look into shifts on the distribution of e-commerce related grievances registered via different channels of National Consumer Helpline (NCH) at one time over five years' period: 2021 to 2025. The object of our investigation will be the cumulative number of grievances that were reported through the different modes of reporting in each year as far as this information is available to us which means we will be looking for trends in the number of complaints lodged through the government agencies without going too deep into the individual level characteristics of consumers.

The data was drawn from the Department of Consumer Affairs, Ministry of Consumer Affairs, Food & Public Distribution, Government of India as the Lok Sabha Question No. 5415 was put forward and was responded to on 25th March 2026 (Government of India, 2026) [15]. The official supplement has the mode-wise and year-wise count of NCH complaints related to false/bogus advertisement and sale of counterfeit/substandard goods in the e-commerce sector. The categories of the complaint submission modes in the source are Web Phone NCH App WhatsApp UMANG, SMS and Others. Over the span of five years mode-wise count comprises of complaint registrations in total of 44,737. As the source has it, the category "Others" remains unchanged and is not considered as a reporting mode which means being specific.

This is why, the study dataset is less comprehensive if we consider the actual e-commerce consumer grievances in India, and the dataset should never be thought of an indicator that all complaints from online transactions were registered. The dataset gives only the count per administrative department. It is not possible to know the level of detail about the complainant or the reason why a particular mode is selected nor is it possible to find out about the outcome of grievance-resolution through the dataset. Considering the dataset, the study focuses on the utilization of reporting modes within National Consumer Helpline (NCH) data, without making inference about consumer preferences of individual consumers, motivations or the overall impact of consumer harm.

3.2. Data Processing and Descriptive Analysis

By means of calendar years and categories listed in the source document, reporting methods' grievance count were tabulate. The numbers were cross-validated with the total figures annually before conducting analysis. For every year, the share (%) of reporting mode was determined by the ratio of that mode registration divided by the entire complaints that year multiplied by 100. The annual percentages shares were cross-verified with the numbers of frequencies since the number of complaint registrations varied a lot between 5 years; because of this, comparison based only on numbers could mask how reporting-mode composition has changed. For the exploratory part, they kept all 7 categories: Web Phone NCH App WhatsApp UMANG, SMS, and Others that the source mentioned. Also, special attention was paid to interpreting WhatsApp because it was a reporting mode that was not available for the whole observation period. Consumer Affairs Division put in a proposal for the integration of National Consumer Helpline with WhatsApp by the last quarter of 2022 (Department of Consumer Affairs, 2022) [10], while the administrative series used in this study shows the complaints from WhatsApp as from 2023. Based on that, the zero WhatsApp counts in 2021 and 2022 were considered that reporting mode was unavailable at that time, not because of consumer non-use. That's why, the description of the utilization of WhatsApp starts from 2023. So, change of reporting-mode composition the annual frequencies and percentages were used and visually displayed as a100% stacked bar chart. It is a method of displaying the data that makes it possible to make comparisons based purely on the shares of each mode regardless of the total figures of complaints in a year. The purpose of the descriptive analysis here is not to point to the underlying reasons behind changes in reporting-mode preferences but to describe and understand the patterns for the composition of registered complaints that have changed.

3.3. Statistical Analysis

Pearson's chi-square test of independence was used to investigate whether the distribution of grievance registrations by different reporting modes was related to the year. The main five-year comparison was limited to five reporting channels that existed over the entire 2021, 2025 period: Web, hone, mobile App, UMANG, and SMS. As the primary test included five reporting modes, the paper excluded WhatsApp, the sixth reporting mode, since it was introduced during the survey period, causing zero (structural) counts in 2021 and 2022. The category "Others" also had very low frequencies, so it was excluded from the main analysis since the official source does not clearly report the composition of this category every year. So, the main analysis involved a 5 x 5 contingency table of 38,193 grievances. Statistical results were considered significant at the 5 percentage level. The chi-square test was performed for each combination where the cell count was more significant than five. Since a statistically significant chi-square statistic alone does not represent the strength of the association, Cramer’s V was also calculated for measuring the effect size. Once a significant result was found through the overall chi-square test, adjusted standardized residuals were checked to figure out the specific yearly-modal combinations that were most responsible for explaining the relationship. Frequencies that are higher than those expected under statistical independence yield positive residuals, whereas frequencies that are lower than expected ones result in negative residuals. Residuals with absolute value of around 1.96 and up were regarded as cells of substantial difference for independence to help the interpretation. They were regarded more as structural indicators than as independent tests of change. Another study based on a restricted period only covering 2024 and 2025 was done with the help of six clearly identified reporting modes: Web Phone NCH mobile App WhatsApp UMANG, and SMS. Such an analysis aimed to determine if significant changes in the makeup of reporting modes through time still existed in two years when WhatsApp and the mentioned modes were simultaneously made available. The remaining category "Others" was also left out from the analysis. The secondary analysis so had the 2 x 6 contingency table with 24,820 complaints of consumer grievance registrations, and in the secondary analysis also, Pearson's chi-square test, Cramer’s V, and adjusted standardized residuals were applied in the same analytical structure. Considering that the dataset is made up of aggregate records from a bureaucratic office rather than from data collected on individuals, statistical methods are mainly used to describe the extent and trend of the association of NCH consumer grievances. So, the results should be regarded more as showing a pattern or tendency than as establishing causal links or measuring consumer preferences of a whole population.

4. Results

4.1. Descriptive Changes in Grievance-Reporting Modes

Table 1 details the annual breakdown of e-commerce issues (grievances) by seven reporting-mode categories. These are based on the National Consumer Helpline data collected from 2021 up to 2025. The total number of grievances registered in the data is 44,737 for the periods covered. Since the amount of grievances recorded each year differs a fair bit, both numbers and percentages are provided. Percentages are mostly used to understand how the share of reporting modes has changed over the years.

Note: Each column represents the percentage distribution of the selected e-commerce grievances recorded in the corresponding year. WhatsApp was introduced as an NCH reporting mode during the study period; therefore, the zero values in 2021 and 2022 reflect non-availability rather than observed consumer non-use. Source: Authors’ calculations based on National Consumer Helpline data reported in Lok Sabha Unstarred Question No. 5415, Department of Consumer Affairs, Government of India, answered 25 March 2026. Fig 1: Annual Composition of Selected E-Commerce Grievances by NCH Reporting Mode, 2021–2025 The descriptive statistics indicate a substantial reallocation of the grievance registrations across various NCH reporting channels during the period of review. It may be inferred from these data that the Web is the main reporting channel throughout the early years and its grievance share was 44.81% in 2021, 51.98% in 2022, and still high at 49.04% in 2023, after a decline to 32.85% in 2024 and 24.88% in 2025. Similar is the overall picture of the utilization of the app by the customer at the beginning, it had 31.69% share in 2021, 27.91% share in 2022, and 31.51% share in 2023, followed by a decrease to 26.34% in 2024 and 5.99% in 2025. If we look at phone-based grievances then it's quite visible that these follow a completely different route. The Phone share was quite small during 2021-2023 at about 10.29% - 14.05% and the share jumped to 18.96% in 2024 and 43.43% in 2025. Phone became the main channel of reporting grievances in the last year. WhatsApp can be considered only since 2023 as this app appeared during the observation period. Its contribution was 6.26% for the year 2023 and then increased to 19.22% for 2024, and then to 21.42% in 2025. Roughly these other reporting channels were just 3% to 5% in one year only and they declined sharply over the years. For instance, UMANG decreased from 4.70% in 2021 and 5.52% in 2022 to 0.73% in 2025 whereas for SMS it never exceeded 1 percentage except in 2025 when it reached 1.17%. The last category "Others" varied from year to another that it finally accounted for 2.38% of grievance registrations in 2025. Most dramatic changes are between 2024 and 2025 that Phone share jumped 24.47 percentage points whereas the NCH App share plunged by 20.35 percentage points and finally the Web share also plummeted by 7.97 percentage points. On the same interval WhatsApp has increased by only 2.20 percentage points.

In short, Table 1 & Figure 1 together present evidence of changes happening from a consumer's point of view in their choice of a platform for submitting a complaint at the five-year level. Here though we do not address the reasons behind why people were moving between different channels or whether they were showing a preference. Aspects such as the significance at the statistical level, size, and the cell-level details behind this reallocation are addressed in the next paragraphs.

4.2. Chi-Square Analysis and Effect Size

To assess whether distribution of the grievance registrations across consistently established reporting modes differed by year, Pearson's chi-square test of independence was applied to five-mode contingency table covering Web, Phone, NCH App, UMANG, and SMS. The primary analysis included 38,193 grievance registrations across five years. The results are shown in Table 2.

Table 2: Chi-Square Analysis of Year and NCH Grievance-Reporting Mode, 2021–2025 Statistic Result Number of observations (N) 38,193 Contingency table 5 x 5 Pearson chi-square (χ²) 8,572.95 Degrees of freedom (df) 16 p-value <.001 Cramer’s V 0.237 Minimum expected cell frequency 10.50 Expected cells below 5 0 Note: The primary analysis includes Web, Phone, NCH App, UMANG, and SMS. WhatsApp is excluded because it was introduced during the study period and therefore generated structural zero counts in 2021 and 2022. “Others” is excluded because the official source does not specify a consistent composition for this residual category. All expected cell frequencies exceeded 5. Source: Authors' calculations based on National Consumer Helpline data reported in Lok Sabha Unstarred Question No. 5415, Department of Consumer Affairs, Government of India, answered 25 March 2026. There was a statistically significant difference among years and grievance-reporting modes, χ² (16, N = 38,193) = 8,572.95, p < 0.001. In other words, the pattern of distribution of registered grievances over five fixed reporting modes was not constant through time in the 2021, 2025 period. The lowest expected frequency was, 10.50 and as none of the expected cell frequencies was below 5, the expected-frequency assumption of the Pearson chi-square was fulfilled. A Cramer's V value of 0.237 was reported, representing an estimate of the degree of association apart from the chi-square statistic. It should be a main criterion to report the effect size since the number of administrative records is quite a large data set. Because p-value is a function of sample size, alone it might indicate statistically significant findings regardless of the magnitude of the difference between the distributions of the reporting mode.

4.3. Adjusted Standardized Residual Analysis

In general, the chi-square statistic suggests association between the composition of reporting modes and year. Though, the statistic itself does not reveal which combination of year with particular mode was the main contributor to this association. That means, the study team focused on the 25 cells for which data were available and looked at the adjusted standardized residuals for each of those cells. In Table 3 the residuals for the modes of online/Web, Telephonic (Phone), NCH App, UMANG and SMS are presented across the years 2021-2025. For interpretation, an absolute adjusted standardized residual of around 1.96 or higher is seen as a major departure from the frequency that would be expected if no association existed; but in this report, the residuals are only considered as diagnostic indicators of an overall association rather than as separate hypothesis or significance tests.

Table 3: Adjusted Standardized Residuals for the Primary Year-by-Reporting-Mode Analysis Year Web Phone NCH App UMANG SMS 2021 3.36 -14.55 9.02 6.42 0.47 2022 14.99 -28.20 7.49 16.58 -6.18 2023 21.58 -47.63 26.56 0.43 -7.54 2024 -5.10 -10.74 19.06 -3.38 -3.22 2025 -29.81 83.61 -51.52 -13.54 14.69 Note: Positive residuals indicate the observed frequencies greater than the expected under independence, whereas negative residuals indicate observed frequencies lower than expected. Absolute residual values of approximately 1.96 or greater are interpreted as notable cell-level departures. Residuals are used diagnostically to identify the structure of the overall chi-square association. Source: Authors’ calculations based on National Consumer Helpline data reported in Lok Sabha Unstarred Question No. 5415, Department of Consumer Affairs, Government of India, answered 25 March 2026.

The residual pattern revealed that the biggest deviation was for Phone calls in 2025, and the standardized residual was positive at 83.61 which was very much higher. This means that a lot higher number of complaints were filed by Phone in 2025, compared to what one would expect if the year and reporting mode were not related. But, Phone had strongly negative residuals for 2022 (-28.20), and for 2023 even more so (-47.63), Because of this, its unexpectedly large positive contribution year for 2025 shows a major distribution change of observed complaints over time.

The NCH App was opposite. It only came out negative in 2025 after producing a row of positive residual numbers of its reporting mode between 2021-2024. Web too showed this pattern of change as well, as its positive number dropped from 21.58 (2023) to negative of (-5.10) (2024) then went down further (-29.81) (2025). The findings agree with the downward trend in relative shares of the NCH App and Web observed in Table 1.

Though smaller, some of the reporting methods also were contributing to this association. UMANG had a quite big positively residual at 2022 (16.58) but later on in 2025 (- 13.54) it turned negative whereas SMS shifted from being negatively residual in 2022-2024 to being positively one at 14.69 in 2025. Overall, it is the residual analysis showing that the correlation overall was not caused through an equivalent variation affecting all reporting ways of the data. Actually, it was a redistribution change, as that mainly large Phone count was together with Web and NCH App's Really lower-than-expected counts as well as a few small adjustments in other reporting modes.

4.4. Restricted-Period Analysis, 2024-2025 The study of restricted periods revealed whether the redistribution of grievance registrations was still visible if the comparison was restricted to the years 2024 and 2025. Both these years were selected as they had the availability of all the six identified reporting channels which comprised Web Phone NCH App WhatsApp UMANG, SMS. The 'Others' category was omitted from the analysis. The contingency output with 2 rows and 6 columns was based on 24,820 complaints lodged through various platforms.

Table 4: Restricted Analysis of NCH Grievance-Reporting Modes, 2024-2025 Reporting Mode 2024, n (%) 2025, n (%) Change (percentage points) Adjusted Standardized Residual, 2025 Web 3,030 (33.09%) 3,993 (25.49%) -7.60 -12.83 Phone 1,749 (19.10%) 6,969 (44.49%) +25.39 +40.43 NCH App 2,429 (26.53%) 961 (6.14%) -20.39 -45.14 WhatsApp 1,773 (19.36%) 3,437 (21.94%) +2.58 +4.81 UMANG 147 (1.61%) 117 (0.75%) -0.86 -6.36 SMS 28 (0.31%) 187 (1.19%) +0.89 +7.28 Total 9,156 (100.00%) 15,664 (100.00%) - - Note: Percentages in this table are calculated using only the six named reporting modes included in the restricted analysis and therefore differ slightly from corresponding percentages in Table 1, which includes the “Others” category. Positive adjusted standardized residuals indicate frequencies greater than expected under independence; negative residuals indicate frequencies lower than expected. Source: Authors’ calculations based on National Consumer Helpline data reported in Lok Sabha Unstarred Question No. 5415, Department of Consumer Affairs, Government of India, answered 25 March 2026. Pearson's chi-square test confirmed the reporting-mode distributions were quite a bit different across 2024 and 2025, χ² (5, N = 24,820) = 3,048.91, p <0.001. The lowest expected cell count came to 79.31, which satisfies the expected-frequency condition for this test. Cramer’s V sat at 0.350. The yearly shift qualifies as a meaningful difference within the observed administrative records.

The restricted comparison points to Phone as the biggest mover. Out of the six named modes, Phone climbed from 19.10% in 2024 to 44.49% in 2025. That is a jump of 25.39 percentage points. The adjusted standardized residual for 2025 hit 40.43, strongly positive. The NCH App went the other direction, falling from 26.53% to 6.14%, a drop of 20.39 percentage points. It produced the largest negative residual for 2025 at -45.14. Web lost 7.60 percentage points too, with a negative residual of -12.83. WhatsApp grew more moderately, from 19.36% of the six-mode total in 2024 to 21.94% in 2025, while SMS inched up from 0.31% to 1.19%. UMANG slipped from 1.61% to 0.75%. What this tells us is the 2024 to 2025 change wasn't simply a side effect of WhatsApp entering the picture during the broader 2021, 2025 window. Even when analysis stayed limited to years where WhatsApp and the other named modes were all available at once, a large redistribution stayed visible. Like the primary analysis, these findings track shifts in the composition of registered grievances, not the personal reasons behind how anyone chose a given mode.

5. Discussion

The results have displayed very marked variations in the selection of various e-commerce complaint registration modes through the India National Consumer Helpline (NCH) system reporting channels, over the period 2021-2025. We find that at the very start Web, with the NCH App, constituted significant portions of the reporting modes; yet, during the time, phone has become the main reporting channel by 2025. Besides, WhatsApp also proved to be a significant means of reporting after it was introduced during the investigation. The results from inferential techniques showed that change in distribution about year was linked with modes of reporting whereas, Cramer’s V measure indicated that this change was not solely due to the number of administrative records but also of substantial strength.

The adjusted standardized residuals give a deeper perspective of how the redistribution was done. The huge positive residual of the Phone category in 2025 in association with greatly negative residuals of the NCH App and the Web show the overall chi-square relationship was mainly due to a few very different changes rather than a set of very similar ones across various modes. For a large part of earlier period the NCH App showed positive residuals and in 2025 it became strongly negative. Same here for the Web from positive residuals at the beginning to highly negative residual in 2025. These trends corroborate the observational fact that the relative position of different access modes within the NCH system can fluctuate over the years.

Such results can be linked with the broader literature dealing with complaint-channel use. Actually, previous evidence points to that complaining through different channels can be of differing nature, and what channels one chooses for complaining could reflect not only the nature of the complaint but also one's personality and the particular situation (Robertson, 2012; Frasquet, Ieva, & Ziliani, 2019, 2021; Miquel-Romero, Frasquet, & Molla-Descals, 2020; Wolf & Steul-Fischer, 2023) [27, 12, 13, 22]. Works on digital complaint giving too have proved that various channels provide different settings for consumers to express complains and the complaints handling processes. The results we have found here so complement and develop this line to show that differences in mode of use are also very well visible when studying over a period a nation-wide public consumer-grievance system administrative record.

WhatsApp's sudden appearance demands careful examination. Really from 2023 it was the second largest reporting channel, only after the Website which never left the top, shows that through this communication channel complaints actually reached the NCH in considerable numbers. Though, Truth is WhatsApp was not available in 2021 and 2022 is a matter of technical capability, not a consumer rejection of the channel. That is why it would be wrong to say that the rise from 0 to over half a million messages indicates a change in customers' preferences. A lot, our 2024-2025 study showed that a lot of changes took place even when the comparison was made only of those two years in which WhatsApp and other named channels were available together. A dramatic increase in phone and drop in NCH App, accompanied by a general decrease in Web, point out that the overall change in reporting-mode composition is not merely a consequence of the launch of WhatsApp. The results here are not connected with the effectiveness or quality of individual reporting avenues. A Web or NCH App share decrease does not imply that these channels became less effective; also, increase in the share of Phone or WhatsApp channels does not prove that those channels brought greater satisfaction, convenience or resolution to consumers. The channels that one uses to present their grievances are influenced by several factors like convenience familiarity characteristics of the channels, and situational factors (Robertson in 2012; Sun, Gao, & Rui in 2021; Wolf & Steul-Fischer in 2023) [27]. These channels, when taken as a summary, can be influenced by what I just said mentioned factors but it is impossible for us, using the NCH data for this paper, to actually test these mechanisms (Robertson, 2012; Sun, Gao, & Rui, 2021; Wolf & Steul-Fischer, 2023) [27]. It is better than for one to treat such data as signs that a change has occurred without trying to point out what has caused the change.

We need to realize finally that grievance registrations should not be looked upon as a straightforward measure of consumer harm. Not all consumers who face problems will automatically make use of official complaints and if a grievance is actually filed then it itself can be an outcome of the complaint being in such a form and place that allows the person to understand available remedies, have procedural knowledge, and be accessible with being willing to follow through the redress process. Balasubramaniam, Sane, & Sharma (2023) [4] further prove the importance of including consumer problems identified from the data that never went through the administrative system. Because of this, the role of this paper is not about quantifying the extent of e-commerce related problems in India but rather explaining the nature of and the changes to the ways through which a set of grievances that reached the NCH were reported.

6. Policy Implications and Limitations

6.1. Policy Implications

The research results give rise to several points which will guide policy-makers and service delivery teams on consumer grievance redressal via multiple-channels. The shift in the manner of complaint registration by way of different modes - Web Phone App, etc. shows that based on only one mode to get the complaint might miss the real grievance pattern. To provide a channel that can help a grievance reach the NCH, the consumer service team has different types of reporting modes. This concept matches with other works showing that effective complaint and redress mechanism must be accessible and digitally redressal friendly (Rana et al., 2016; Ong & Teh, 2016; Kumar et al., 2023) [26, 23, 18]. Then again, in reality Phone complaints went up drastically the year 2025 illustrates the point for regularly evaluating complaint volumes at different modes to plan for better operationalization. Such fluctuations in modes can help administrators decide whether staffing level, call-handling capacity, digital infrastructure, or other operational resources still match with the grievance-receiving channels. Continuous monitoring is necessary because of the changes in importance which different modes undergo for example as revealed by the contrasting development over the period, between the Phone, Web, and the NCH App. In parallel, a drop in the percentage share of a reporting channel should not justifiably result in removal or drastic reduction of the channel. Because utilization level is not always a good indicator of effectiveness of the channel, or dissatisfaction, or even lack of the need. Different types of channels may cater to different types of groups as in the case with groups having different access circumstances. Also, greater utilization may not mean that the channel is the best. Better information on the expansion, modification, or discontinuation of different channels will come if the mode-level registration data is compared with measures of processing time, resolution outcomes, service quality accessibility user satisfaction, and characteristics of the complainer.

A good illustration about the necessity to analyze the performance of the grievance channels under the specific organizational structure came forth with the introduction of the NCH over the WhatsApp communication platform during the study period. The launch of this new mode and the upgrade of the existing NCH system resulted in the fluctuation in the complaint registration ratio across different channels. It is quite clear that this fluctuation was due to the introduction of the new mode rather than consumer preference changes. So, to get a comprehensive view on consumer-grievance redress performance, besides general evaluations, the Department of Consumer Affairs shall consider the periodic analysis of the channel-level administrative data. This way, the department will be able to pinpoint fluctuations in the consumer demand between the reporting channels while maintaining a wide and easy-access NCH reporting architecture.

6.2. Limitations of the Study

A number of constraints must be recognized if one is to correctly understand the results of this work. At the level of administrative data, the scope is limited to a narrow category of e-commerce grievances about misleading advertisement and sale of fake or low-quality products. As a result, the findings cannot be extrapolated to encompass all sorts of e-commerce-related complaints submitted to the National Consumer Helpline or to describe the general situation of consumer issues in the online economy of India. On top of that, administrative reports reflect only the number of cases lodged in the official complaint system; they do not register consumer problems faced by those who never filed a complaint through this system.

Next, the datasets are grouped year and complaint mode-wise and so no information is provided about the complainants individually. It is not possible to assess how the age gender economic status, education level region knowledge of e-consumer rights, use of digital technology, etc. of the complainants may have affected the type of complaints or the way they came in to be addressed. Same here, the reason for choosing one mode of reporting over another is unknown. It would be a stretch to interpret an increase or decrease as reflecting the consumer's personal interest, convenience preference, level of information, or motivation level, etc. Third, throughout 2021, 2025, the way reporting was offered was more or less changing constantly. WhatsApp was made a reporting channel mid-observation, limiting the ability to compare directly between it and other modes of reporting which were consistently available over all five years. The solution put forward is to exclude WhatsApp from the chi-square test generally five years' data and to do a confined comparison solely for 2024, 2025 as an example. Still, really channel availability and the general technical development of the NCH were ongoing changes in the context in which users filed their complaints remained. With the limitation of the data being only yearly figures, this study cannot figure out the effects of technological or administrative changes that could result, for example, from introducing reporting channels or improving the NCH system.

Finally, the official records show an "others" category under the reporting category without specifying its detailed or necessarily consistent composition throughout different years. Because of that, the category was included in showing statistics but it was omitted when it came to hypothesis testing. Besides that, there are no data provided about grievance-processing length resolution quality measures of redresses, consumer satisfaction, and performance of individual modes of reporting. Because of this, neither an increase of share nor a decrease in the case registrations via a certain mode can be taken as evidence for the mode to perform better or worse respectively.

Future studies might mitigate those limitations by merging more detailed data on complaints with survey data from those who make complaints. Data collected monthly or quarterly at the level of complaint modes would allow a more detailed study of the impact, or otherwise, of the implementation or modernization of reporting channels, while information from the individual level might shed some light on why certain complaint modes are preferred. Connecting reporting-mode data with resolution time, outcomes, and consumer satisfaction would also give upcoming studies a chance to go further than utilization patterns and provide a comprehensive evaluation of how far various channels go in redressing grievance.

7. Conclusion

This paper investigated shifts in the composition of reporting mode in selected e-commerce complaints lodged through the National Consumer Helpline (NCH) in India over the period 2021 to 2025. Based on official administrative data on misleading advertisements and the sale of fake or substandard products, the analysis highlighted a definite reallocation of between the NCH reporting modes. Web and the NCH App constituted a significant proportion of registrations in the earlier years while Phone had emerged as the dominant reporting mode by 2025. WhatsApp emerged as a significant part of the reporting mix after its advent during the study period.

Statistical analysis established that reporting-mode distribution was related to year among the five reliable, stable named mode categories, χ² (16, N = 38,193) = 8,572.95, p <.001, Cramer’s V = 0.237. The adjusted standardized residuals revealed that this association was focused predominantly in the sharp increase in Phone registrations seen in 2025, and the proportionally lower-than-expected registrations through Web and the NCH App. Fortunately, the restricted 20242025 analysis, controlling for WhatsApp's presence throughout the general study period, also revealed a sizeable difference in reporting-mode composition, χ² (5, N = 24,820) = 3,048.91, p <.001, Cramer’s V = 0.350. This investigation extends the literature on consumer grievance redress by redirecting focus from channels of complaint volume, geographic clustering, and the composition of grievances to channel of entry into a civil public redress system. The results underscore the usefulness of analyzing reporting-mode composition across time and of recognizing shifts in channel availability in the interpretation of administrative grievance data. From a policy standpoint, the shifting distribution of channel types reinforces the importance of ongoing tracking of channel-specific demands and of supporting an adequately flexible multichannel approach to redress that is not solely reliant on a single mode of access.

Second, the results characterize observed administrative reporting behavior, not consumer propensity or total ecommerce consumer harm, and do not explain what causes shifts in mode usage or relative performance of specific channels. Future work with more detailed administrative data and complainant-panel data could investigate what drives reporting mode choice and relate modes to resolution outcomes and timeliness, access and user satisfaction.