Social Responses and Change Management Strategies in Smart City Transitions: A Socio-Demographic Perspective

Highlights

What are the main findings?

• Individuals in the low-income bracket (below AUD 90,000) exhibited emotional distress—including shock, frustration, and depression—primarily driven by fears of job displacement amid smart city transformations. A weak positive correlation was observed between higher educational attainment and openness to digital environments.
• Elderly individuals and females reported significantly higher levels of anxiety and depression compared to other socio-demographic groups in relation to the adoption of smart technologies and transformed urban systems.

What is the implication of the main finding?

• Free local government-led digital literacy programs and consistent technical support were identified as the most effective change management strategies across socio-demographic groups to reinforce the value of transformed urban environments.
• To ensure a successful smart city transition, local communities and governments must prioritise knowledge enhancement and address the digital divide, particularly in supporting elderly populations and women.

Abstract

Technological advancements alone, without addressing public responses to social changes cannot ensure inclusive and sustainable smart city transitions as cities and societies comprise diverse individuals and communities with varied socio-demographic backgrounds. Thus, this research investigates social responses to smart city transitions aiming to understand individuals’ social reactions to the changes across diverse socio-demographic profiles, and identify socio-demographic group-specific change management strategies to enhance public engagement and minimise resistance during the transition. Through a questionnaire survey using multivariate analysis, correlations between socio-demographic profiles and social reactions are identified. Age and frustration showed a positive correlation indicating that elderly individuals express greater concerns about unfamiliar smart technologies. Weak negative correlations emerged between income levels and transition-related stress including shock, frustration and depression. Significant differences were revealed between income groups (AUD 126,000+ and below AUD 90,000) associated with job security due to smart technologies and digital automation. Improving digital proficiency through free local government-led training, and reinforcing the benefits of digitally transformed urban environments through timely technical support were identified as the most essential change management strategies. Thus, this research will contribute to enabling local governments and policymakers to have practical insights in developing socially inclusive and community-centric transition plans with minimised social resistance.

1. Introduction

Smart city transitions leverage diverse smart technologies to create sustainable living environments in infrastructure, transportation, energy, waste, and healthcare, and ultimately aim to enhance the quality of life for residents [1]. Recently, an increasing number of developed countries—the UK, Singapore, and Spain—have adopted smart city transformation initiatives as a part of national development strategies [2]. The evolution of smart cities inevitably introduces significant social changes affecting social behaviours and norms in diverse contexts including workplace, education, civic engagement, and community dynamics [3]. A transformed city can perform as planned when the pubic willingly accepts and adapts to smart city environments enabled by digital transformations [4]. Since a city is a heterogeneous composition of individuals and communities, neglecting the diverse social responses toward the new urban environments may cause risks alienating the public from the transition and foster scepticism toward the new environment [5]. Indeed, there have been many cases of social resistance against smart city transitions as seen in various countries such as Australia, Hong Kong, Brazil, and Germany due to insufficient public acceptance [6]. Therefore, gaining a nuanced understanding of public attitudes and emotional reactions toward smart city initiatives is essential for developing inclusive transition strategies that foster and sustain long-term sustainability of smart city [7,8]. Although the welfare of communities and society is the ultimate goal of smart cities, researchers have criticised the current imbalanced focus on overemphasised technological transition compared to the social dimensions of smart city transitions including the insufficient in-depth exploration of individual and community responses during the transition [5,9].

Building upon current studies, this research formulates the following research questions.

• Q1. How do socio-demographic factors influence individuals’ emotional and behavioural reactions to smart city transitions?
• Q2. How should social change management strategies be tailored to diverse socio-demographic groups to foster acceptance of smart city transitions?

To explore the research questions, this research utilises the two well-established frameworks as theoretical lenses—the Kübler–Ross change model and the ADKAR change management framework. The Kübler–Ross change model provides a structured way to understand individuals’ emotional progression and behavioural adaptation including resistance and acceptance in response to abrupt changes and disruptions such as smart city initiatives [10,11,12]. Additionally, the ADAKAR framework is widely adopted in developing change management strategies for large-scale urban development programmes including smart city development since it offers practical guidance for facilitating public adaptation to digitally transformed environments [13]. Through the integrated application of these lenses, the research aims to investigate social reactions and responses toward smart city transitions across diverse socio-demographic profiles, and identify effective change management strategies that enable socially inclusive smart city transitions. The findings will support local governments and policymakers in developing equitable transition plans that accommodate and address socio-demographic-specific needs and concerns. references.

2. Literature Review

2.1. Social Reactions and Attitudes Toward Smart City Transition

Managing social changes is equally vital to the integration of technological innovation in urban environments during smart city transitions since these changes profoundly affect individuals’ daily lives and community cultures [14,15]. A limited understanding of how digitally transformed environments reshape individual lifestyles can cause public anxiety and hesitation in embracing the transition. The technological transition demands digital literacy from the public to adapt to the digitally transformed societies [16,17]. Unlike conventional urban development projects, smart city transitions evoke complex emotional responses. Individuals are required to step outside their comfort zones [18], since smart technologies dramatically alter familiar urban settings and daily routines. Resistance is often observed among those who are less enthusiastic about smart cities and digital technologies [19].

Initial public scepticism at the outset of smart city transitions often stems from concerns about adopting new technologies, and fears related to data privacy and cybersecurity [20,21]. Furthermore, job security concerns triggered by digitalisation and automation may intensify negative emotional reactions such as hopelessness or anger [22]. Researchers have highlighted that the public willingness to embrace and adapt to the social changes is the most critical success factor in achieving sustainable smart city transformation [23,24]. Indeed, as tangible benefits including improved quality of life and new job opportunities become evident, individuals’ attitudes gradually shift from resistance to acceptance [25]. Thus, emotional responses should be strategically addressed in smart city transition planning to foster social trust and address individual and community concerns.

2.2. Kübler–Ross Model in Understanding Social Reactions

Cities and urban environments must be understood as dynamic and heterogeneous systems rather than uniform entities to fully grasp the nuanced emotional reactions elicited by smart city transformations across diverse socio-demographic groups [26]. Researchers highlighted a lack of detailed categorisation and comprehensive investigation into individual emotional and social reactions throughout the smart city transition process [27,28]. Despite the critiques, the current literature identifies two primary emotional stages associated with the smart city transition: (1) Resistance (including scepticism, concerns and frustrations), (2) Acceptance (including adaptation, exploration and trust). Interestingly, these findings correlate closely with the Kübler–Ross change model, which outlines the six emotional stages: Denial, Frustration, Depression, Experimentation, Decision, and Integration. This model provides a more structured and granular segmentation enabling researchers to capture the emotional evolutions and shifts over time. Consequently, the change model has been widely used to investigate rapid social changes such as urban reformation and development to address social concerns and mitigate resistance [11,29,30,31,32].

Given the relevance and practical utility of the Kübler–Ross change model, this study employs it as a conceptual framework lens to investigate the nuanced individual emotional responses to smart city transitions. The model will further support a deeper investigation of emotional evolution trajectories throughout the transition process. Based on a holistic understanding of emotional responses across diverse socio-demographic profiles, the research aims to offer insights to design inclusive and socially responsive smart city transition strategies.

2.3. Change Management Strategy for Smart City Transition

The concept of smart cities has emerged as a catalyst for societal transformation in urban areas driven by the integration of ICT technologies that profoundly reshape urban environments and lifestyles. These transformations occur rapidly across individual, community, and societal levels, and the complexity of smart city transitions poses significant risks of intensifying public resistance [33]. Researchers [34] identified that the benefits of smart city transition may be unevenly distributed, largely due to the gaps in individuals’ digital proficiency. Researchers [35] asserted that process-oriented guidance for urban transformation is instrumental for improving digital literacy, and empowering diverse individuals and communities to develop technological confidence. Pahuja [36] emphasised that transparent and inclusive change management strategies are critical for guiding public adaptation to the transformed environment effectively, and tackling potential inequities stemming from different levels of individual’s digital readiness. Consequently, there is a growing consensus among researchers that the success of smart city transitions requires proactive and inclusive change management approaches that support both individuals and communities [23,37,38,39].

In response to the necessity of structured and synchronous change management plans, the ADKAR change management framework comprising the five distinct sequential change processes—Awareness, Desire, Knowledge, Ability, and Reinforcement—has been widely adopted and recommended by local governments and researchers for collaborative ‘authority-led and community-driven’ transition plans [40,41,42]. Furthermore, researchers recognised the greater efficacy of the ADKAR model compared to other change management frameworks such as Kotter’s 8-Step model, the Duration, Integrity, and Commitment, and Effort framework, since the ADKAR model is grounded in a bi-directional communication structure that fosters synergy between the public and local governments for increasing awareness and desire for the changes [43,44]. In contrast, the other frameworks adopt top-down and asynchronous communication approaches. El Barachi et al. [45] emphasised the importance of managing people-side changes by establishing mutual trust and synchronous communication channels between the two parties. Recently, the ADKAR model was implemented in Romania to assess technological readiness in public institutions as a part of urban digitalisation projects, which highlights the ADKAR model’s practical utility in guiding urban transformation [13].

However, researchers recognised that monolithic change management plans often underestimate the complexity of diverse socio-demographic factors during the smart city transition processes [25,46]. Particularly, the elderly require distinct support mechanism compared to younger cohorts due to limited digital proficiency and lower technical adaptability [23]. To sustain established communities and public awareness of the transition, government-led initiatives such as free public training for smart technologies and continuous promotions highlighting the benefits of digital transformation are essential [47]. Although there are studies around how to technologically transform the existing city to smart city, researchers argued that there is no ‘one-fits-all’ transition plans. The socio-demographic-specific responses should be considered in the development of human-centric smart city transition strategies.

Thus, in alignment with the need for tailored transition strategies, this research adopted the ADKAR change management model as a structured framework and analytical lens to identify effective change management strategies suited to diverse socio-demographic groups. The ADKAR model will also serve as change management guide for supporting communities throughout the transition process, and ensure inclusive transition plans. Furthermore, these findings will complement a comprehensive understanding of social reactions captured through the lens of the Kübler–Ross change model, which will formulate holistic change management strategies for smart city transitions. The conceptual framework in Figure 1 presents an integrated lens combining the Kübler–Ross model and the ADKAR change management model for this research.

3. Research Methods

To explore individuals’ social reactions and effective change management strategies during smart city transitions across socio-demographic profiles, this research adopted a structured questionnaire survey comprising 13 questions categorised into three thematic sections.

Section 1. Socio-Demographic Information (Two Questions): Collecting data on participants’ Age, Gender, Academic Qualifications, and Income Level to conduct multivariate analysis to identify and understand relationships among socio-demographic profiles, social responses and priorities among change management strategies.

Section 2. Social Responses to Smart City Transition (Six Questions): Assessing participants’ social reactions for each emotional stage based on the Kübler–Ross model (i.e., Denial, Frustration, Depression, Experimentation, Decision, and Integration stages). A 5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree) was utilised to measure social reactions quantitatively. Participants were asked to indicate levels of emotional reactions toward smart city transitions.

Section 3. Change Management Priorities (Five Questions): Investigating the relative importance among the five change management strategies of the ADKAR framework (i.e., Awareness, Desire, Knowledge, Ability, Reinforcement strategies) based on a ranking survey. Participants were asked to rank the five strategies in order of perceived importance for facilitating adaptation to smart city transitions. To ensure the validity of the ranking survey, the questionnaire was developed based on the five established stages of the ADKAR framework. To minimise confusion and confirm that participants clearly understood the ranking questions, a pilot test with a small sample (n = 4) was conducted. Consequently, each strategy was clearly defined and presented in a randomised order to minimise response bias.

The questionnaire survey was disseminated through both digital and physical mediums to maximise diversity of participants’ socio-demographic profiles. Online survey was made available through special interest groups (SIGs) on social media platforms such as the Smart City Council and direct email contacts through local governments in Australia. In parallel, printed survey were distributed through local community centres, public libraries, and local government offices to obtain the elderly people, who are not actively engaged with social medias and prefer a paper-based survey. Ethical approval for this research was granted by the Human Research Ethics Committee at the University of South Australia. Prior to participation, all individuals received a comprehensive research information sheet outlining the research objectives, methodology, and confidentiality protocols. Informed consent was obtained from every participant. Participation was entirely voluntary, and anonymity was rigorously maintained throughout both the data collection and analysis phases.

The sample size was determined based on followed the definitive population correction formula as shown in Equation (1). The recommended sample size is 100 for a population of 100,000+ to achieve a precision level of ±10% with a 95% confidence interval with a margin of error ranging from 5% to 10% [48]. The population size of South Australia was 1,873,819 in 2024 [49], and the recommended sufficient sample size for statistical analysis is between 96 and 385. The research successfully collected 203 responses, which falls within the statistically sufficient range.

$$SampleSize={\displaystyle \frac{\frac{Z^2\times \rho (1-\rho )}{e^2}}{1+\left(\frac{Z^2\times \rho \left(1-\rho \right)}{e^{2}N}\right)}}$$

(1)

where N is population size, e: is margin of error, Z is Z-score.

To investigate the correlation between ordinal socio-demographic variables (Age, Academic Degree, and Income) and the Reaction and Change Management Strategies’ Priority variables, Spearman’s correlation tests were performed. Since Gender is a dichotomous variable, Rank-biserial correlation coefficient tests were conducted. In addition, to further investigate the presence of statistically significant relationships among the variables, a crosstabulation analysis using the Chi-square test was conducted. Furthermore, to examine whether significant differences exist in social reactions and change management strategy priorities among each group of Age, Academic Degree, and Income towards the smart city transition, Kruskal–Wallis’s tests were conducted. Subsequent Dunn–Bonferroni’s post hoc tests were conducted to further investigate whether the mean rank of one group is significantly different from another group (e.g., between Age 20s and 60s). To assess differences and compare reactions between Genders (male and female), the Mann–Whitney test was applied. All statistical analyses were performed using appropriate settings for ordinal and non-normally distributed data, ensuring robustness in the interpretation of collected data.

4. Results

4.1. Socio-Demographic Profiles

A total of 203 valid responses were collected (112 online and 91 paper-based) from 400 targeted respondents (51% response rate). As shown in

Table 1. Respondents’ socio-demographic profile.

Socio-Demographic Profile		Frequency	Percentage (%)
Age	18–24	18	9
	25–34	50	25
	35–44	76	37
	45–54	24	12
	55–64	18	9
	65+	17	8
Academic Degree	School (High School)	20	10
	Certificate	20	10
	Diploma	20	10
	Bachelor	80	39
	Master/Doctoral	63	31
Annual Income ($, AUD)	Below 48,000	91	45
	48,000 to 90,000	74	36
	90,000 to 126,000	20	10
	Above 126,000	18	9
Gender	Male	121	59
	Female	82	41
Total		203	100

, most respondents hold a bachelor’s degree. Particularly, the collected data presents a well-balanced distribution in the gender ratio (59% male and 41% female) as well as in the income levels between lower-income (45% of participants) and mid/higher-income (55%). The balance enables the research to avoid analysing biassed datasets based on a one-sided perspective.

4.2. Social Reactions and Change Management Strategies

Participants were asked to indicate their reactions and responses to the smart city transition, using the Kübler–Ross change model as a framework. In Figure 2, the x-axis represents the seven stages of social reactions, while the y-axis indicates the weighted means of the emotional response levels to the smart city transition. The Decision stage (stage of fully adopting new technologies into daily life and adapting to the transformed environment), which had the highest score of 4.2, reflecting acceptance and readiness to embrace the smart city transition and its transformed environments, was the most prevalent reaction, with 88% of participants selecting Agree and Strongly Agree. The Integration stage (stage of no longer perceiving the transition as a burden, rather feeling comfortable with the changes) and Experiment stage (stage of gradually embracing new technologies and becoming familiar with the transformed environments) were the second and third most prevalent reactions, indicating that people have a strong inclination to integrate the transformed smart city environments with their daily lives by exploring digital technologies and smart systems. In contrast, the early emotional stages including Shock, Denial, Frustration, and Depression were the least common reactions, indicating low scores between 2.1 and 2.4. Thus, predominantly positive attitudes towards the transition were exhibited, and a minimal portion of respondents’ resistance or disbelief was presented. These findings suggest that a high level of agreement and acceptance in embracing the changes are currently established.

Participants were asked to rank the most effective change management strategies for helping people adapt to the changes introduced by the smart city transition. As shown in Figure 3, the x-axis represents the five change management strategies based on the ADKAR framework, while the y-axis indicates the levels of effectiveness of these strategies for transition adaptation based on the weighted means.

Providing proper technical support to reinforce the changes embraced (S5 with weighted mean 3.43) was identified as the first priority strategy, with 50% of the respondents ranking it as either the first or second most effective strategy.

The three strategies—Increasing Desire (S2), Improving Knowledge (S3), and Enhancing Ability (S4)—indicated similar outcomes, ranking as the second or third most effective strategies, with weighted mean values ranging from 2.96 to 3.17. In contrast, Increasing Awareness of the smart city transition (S1) was regarded as the lowest priority strategy. These findings align with the previous findings regarding the social reactions indicating the high level of acceptance towards the changes, since respondents expressed a desire to improve their digital skills and maintain confidence levels. Furthermore, the results suggest that individuals experiencing frustration or depression may actively attempt to adapt to the transition by learning and acquiring knowledge and skills in smart technologies when they have access to free training opportunities.

4.3. Correlations Between Socio-Demographic Profiles and Social Reactions and Change Management Strategies

Correlation coefficient tests (for Gender) were conducted to investigate the correlation between social reactions, change management strategies, and socio-demographic variables. As shown in

Table 2. Correlations between socio-demographic profiles and social reactions (R1 to R7).

	Age		Academic Degree		Annual Income		Gender
Correlation Coefficient/Significance	${\mathit{r}}_{\mathit{s}}$	$\mathit{\rho }$	${\mathit{r}}_{\mathit{s}}$	$\mathit{\rho }$	${\mathit{r}}_{\mathit{s}}$	$\mathit{\rho }$	${\mathit{r}}_{\mathit{r}\mathit{b}}$	$\mathit{\rho }$
R1	0.16	0.04	−0.17	0.23	−0.13	0.09	0.12	0.11
R2	0.17	0.02	−0.02	0.76	−0.13	0.09	0.12	0.11
R3	0.22	0.00	−0.06	0.43	−0.15	0.05	0.10	0.19
R4	0.16	0.04	−0.04	0.63	−0.19	0.01	0.15	0.06
R5	−0.20	0.01	0.12	0.11	−0.03	0.74	−0.06	0.47
R6	−0.14	0.05	0.17	0.03	0.01	0.87	−0.04	0.63
R7	−0.17	0.02	0.13	0.08	−0.05	0.52	−0.02	0.81

, Age exhibits a weak positive correlation with Shock (R1), Denial (R2), Frustration (R3), and Depression (R4), while showing a weak negative correlation with Experiment (R5), Decision (R6), and Integration (R7).

In particular, the positive correlation between Age and Frustration suggests that as participants’ age increases, the transition process requires effort, trial and error in adopting unfamiliar environments, which tends to amplify frustration. This finding is consistent with previous research indicating that as age increases, the level of frustration an individual experience regarding technology also increases [50]. Furthermore, the negative correlation between Age and Experiment (R5) indicates that as participants’ age increases, their tendency to explore new systems and technologies in smart city environments declines. Consequently, a weak negative correlation between Age and Improving Knowledge (S3) indicates that as participants’ age increases, the perceived importance of improving knowledge tends to diminish, as shown in

Table 3. Correlations between socio-demographic profiles and change management strategies (S1 to S5).

	Age		Academic Degree		Annual Income		Gender
Correlation Coefficient/Significance	${\mathit{r}}_{\mathit{s}}$	$\mathit{\rho }$	${\mathit{r}}_{\mathit{s}}$	$\mathit{\rho }$	${\mathit{r}}_{\mathit{s}}$	$\mathit{\rho }$	${\mathit{r}}_{\mathit{r}\mathit{b}}$	$\mathit{\rho }$
S1	0.13	0.11	0.01	0.93	0.12	0.13	−0.02	0.86
S2	−0.06	0.46	−0.05	0.56	−0.11	0.17	0.13	0.10
S3	−0.13	0.05	0.26	0.00	−0.12	0.13	−0.11	0.17
S4	0.10	0.21	−0.06	0.44	0.11	0.17	0.13	0.10
S5	−0.02	0.82	−0.12	0.14	0.02	0.78	0.07	0.39

. Thus, ageing populations tend to remain discouraged and frustrated, instead of adapting to new technologies and moving forward to the ‘Experiment’ and ‘Integration’ stages, compared with younger generations.

Weak positive correlations between Academic Degree and Decision (R6) and Improving Knowledge (S3) were observed. Individuals with higher levels of education tend to indicate more positive responses toward the smart city transition. Similarly, weak negative correlations between Annual Income and Frustration (R3) and Depression (R4) were identified. No other substantial correlations were observed between the remaining socio-demographic factors and social reactions as well as the mitigation strategies. Although Shock (R1) and Denial (R2) did not present statistically significant results (p-values > 0.05), the presence of negative correlations needs to be acknowledged. Rank-biserial correlation coefficient tests were conducted for Gender, as it is a dichotomous variable. Although there were no substantial correlations identified, it is noteworthy that Depression (R4) exhibited a weak positive correlation. This finding suggests that Depression may be a common emotional response to the transition regardless of gender.

To further investigate the presence of statistically significant relationships among the variables, a crosstabulation analysis using the Chi-square test was conducted. As shown in

Table 4. Crosstabulation analysis between socio-demographic profiles and social reactions and change management strategies. P.C.: Pearson’s Chi-square, L.R.: likelihood ratio.

	Age		Academic Degree		Annual Income		Gender
	P.C.	L.R.	P.C.	L.R.	P.C.	L.R.	P.C.	L.R.
Relationships with Social Reactions
Value	57.02	58.96	15.77	15.95	22.71	21.80	12.39	12.69
df (Degree of Freedom)	20	20	16	16	12	12	4	4
Asymptotic Significance	<0.001	<0.001	0.47	0.46	0.03	0.04	0.02	0.03
Relationships with Change Management Strategies
Value	88.34	90.18	21.13	22.15	41.30	36.48	20.85	21.03
df	20	20	16	16	12	12	4	4
Asymptotic Significance	<0.001	<0.001	0.17	0.14	0.02	0.01	<0.001	<0.001

, significant relationships were identified between social reactions and change management strategies with Age, Annual Income, and Gender. Interestingly, Academic Degree did not present a statistically significant relationship indicating that educational attainment may not be a decisive factor in social responses to smart city transitions or change management approaches for the transitions.

4.4. Socio-Demographic Influences on Social Reactions and Change Management Strategies

Kruskal–Wallis’s tests were conducted to examine whether there were significant differences among different Age, Academic degree, and Income groups regarding social reactions and the priorities of change management strategies for the transition to smart cities. To assess differences between Genders, the Mann–Whitney test was conducted as shown in

Table 5. Socio-demographic influences on social reactions (R1 to R7) and change management strategies (S1 to S5), KW: Kruskal–Walis, MW: Mann–Whitney.

		R1	R2	R3	R4	R5	R6	R7	S1	S2	S3	S4	S5
Age	KW H $X^2\left(5\right)$	11.82	14.37	24.07	12.25	28.60	26.78	17.02	9.82	1.37	6.43	4.44	0.54
	df	5.00	5.00	5.00	5.00	5.00	5.00	5.00	5.00	5.00	5.00	5.00	5.00
	$\rho$	0.04	0.01	0.00	0.03	0.00	0.00	0.00	0.08	0.93	0.27	0.49	0.99
Degree	KW H $X^2\left(4\right)$	8.33	6.35	7.41	5.46	7.34	9.05	5.04	2.35	2.58	14.62	1.12	2.46
	df	4.00	4.00	4.00	4.00	4.00	4.00	4.00	4.00	4.00	4.00	4.00	4.00
	$\rho$	0.08	0.17	0.12	0.24	0.12	0.06	0.28	0.67	0.63	0.01	0.89	0.65
Annual Income	KW H $X^2\left(3\right)$	8.78	4.93	8.83	9.70	0.91	3.42	1.61	2.53	3.50	2.76	2.05	1.35
	df	3.00	3.00	3.00	3.00	3.00	3.00	3.00	3.00	3.00	3.00	3.00	3.00
	$\rho$	0.03	0.18	0.03	0.02	0.82	0.33	0.66	0.47	0.32	0.43	0.56	0.72
Gender	MW U	2998.5	3013.5	3045.5	2833.5	3093.5	3318.5	3343.5	2974.0	2538.5	2695.5	2567.5	2770.5
	$\rho$	0.08	0.08	0.10	0.03	0.11	0.41	0.49	0.99	0.10	0.17	0.09	0.37
	Mean Rank (Male)	81.79	81.93	82.23	80.23	91.32	89.19	88.96	96.00	95.00	98.00	98.00	99.00
	Mean Rank (Female)	95.25	95.02	94.54	96.57	80.17	83.53	83.90	62.00	63.00	63.00	62.00	61.00

. Noticeable differences were observed across different age groups concerning all social reaction variables. Subsequent Dunn–Bonferroni’s post hoc tests were performed to pinpoint which specific groups differed significantly from the other five age groups (see Table 1). The primary source of these differences stemmed from the 65+ age group compared to the other age groups. This finding suggests that the 65+ age group exhibits distinctly concerned and negative reactions toward new and unfamiliar smart city environments and the transition process, in alignment with the findings of Tupasela et al. [51] indicating that ageing populations are more vulnerable than younger generations during the smart city transition and transformation. In addition, Vojinovic et al. [52] support this finding noting that the elderly individuals tend to actively engage with smart technologies when smart city services and environments are designed for ease of use. There were no significant differences observed among different age groups regarding change management strategies.

The different Academic Degree groups and social reactions did not exhibit significant differences. However, a significant difference between Academic Degree groups and Improving Knowledge (S3) as a change management strategy reveals that the difference primarily exists between individuals with bachelor’s degrees and those with lower educational qualifications (e.g., high school, diploma, and certificate). Mean rank comparisons suggest that the higher the educational degree an individual attains, the greater their interest and intent in improving knowledge of transformed environments and digital technologies. This finding suggests that knowledge improvement may play an important role in mitigating social risks associated with the smart city transition.

Significant differences among Income groups are identified in the Shock (R1), Frustration (R3) and Depression (R4), which emphasises that income can be an influential factor in effective smart city transitions. Subsequent Dunn–Bonferroni’s post hoc tests clarified that these differences primarily arose from variations between the AUD 126,000+ income group and the income groups earning below AUD 90,000. Individuals with higher incomes tend to feel less frustration and depression than those in the lower-income groups. No significant differences were found between Income groups and change management strategies.

The Mann–Whitney results indicated that males and females exhibited differences solely in the Depression (R4) reaction variable, indicating that females feel more depressed during the transition process. Furthermore, mean rank analysis suggests that females tend to feel more negative emotions while males tend to indicate more positive reactions toward the smart city transition as the transformed environment pushes individuals out of their comfort zone and daily routine. There were no significant differences found between gender and change management strategies. Although the change management strategy of ‘Enhancing the ability through free training (S4)’ exhibited a slightly higher p-value (0.09), this finding should not be ignored since increasing digital literacy and abilities through the free digital training can support individuals and communities to adapt to the transformed environment regardless of gender, which aligns with the OECD’s inclusive digitalisation strategy [53,54].

5. Discussion

The research findings underpin that a city must be understood as a living and growing entity consisting of people, communities, and cultures rather than a homogeneous society. More importantly, as there is no ‘one-size-fits-all’ solution for transforming an existing city and satisfying every member of society simultaneously [53,55], it is instrumental that local governments address the specific needs and concerns of different segments in their smart city transition plans based on various socio-demographic profiles.

5.1. Age

Elderly citizens tend to feel more shocked and less ready to integrate into the smart city compared to other age groups, especially younger cohorts. These differences may be rooted in a generational divide, as older generations often grew up in a world without advanced digital technologies. Another contributing factor is the lifestyle preferences of senior citizens, who typically prefer face-to-face interactions, traditional services, and familiar routines over technology-driven solutions. This preference reflects a desire to preserve community bonds and maintain the status quo [56]. Self-service kiosks and automated technologies have been increasingly implemented in various places including restaurants and public services. However, elderly individuals with limited digital literacy are currently experiencing social exclusion in their daily lives [57].

In response to this problem, UN-Habitat created a global sustainable urban development framework “The New Urban Agenda”, aiming for an equitable distribution of benefits from urban development and digitalization. It promotes a user-friendly and participatory data platform where all residents have access to basic social services and opportunities, since ageing people are particularly vulnerable to digital transformation. Local governments should implement comprehensive sets of strategies to improve older residents’ digital literacy, enhancing their confidence in using technology, and addressing their unique needs and concerns to ensure inclusive engagement in smart city initiatives [5]. Thus, these findings highlight the importance of preparing elderly individuals for smart city transitions well in advance through well-structured change plans including a digital platform designed for ease of use, where people can easily navigate and find relevant support.

Furthermore, local governments should consider both long-term transition plans and immediate support for ageing people as short-term transition plans such as digital literacy improvement. The ageing population is projected to increase to 1.6 billion by 2050, which will be 20% of the global population, and the number of people aged 80 and older is expected to grow faster [58]. The simultaneous approach is vital since increasing demands on urban infrastructure such as healthcare, housing, transport for ageing people cannot be met by a simple short-term expansion of housing and infrastructure supply. As life expectancy continues to increase, more maintenance and retrofitting are required to alter housing conditions more suitable and sustainable for ageing populations. Particularly, designing roads considering mobility and accessibility, and building or refurbishing homes for assisted living environments should be considered [59]. Moreover, the growing number of ageing populations adds complexity to urban economies as employed populations will continuously decrease impacting tax revenues, and this may potentially render social funding insufficient for smart city transition.

Thus, local governments should establish a long-term financial planning strategies to meet future demands through a systematic approach to identify and address the needs of ageing people and communities in smart cities. In addition, encouraging post-retirement citizens to re-enter the local labour market through flexible work arrangements such as various working hours and locations [60] is instrumental to sustain the transition and boost social engagement and financial independence among ageing individuals. This effort aligns with the core objectives of smart city transitions, and the UN Sustainable Development Goals [61,62].

5.2. Academic Degree

The similarity in social reactions related to academic degree may be attributed to the relatively standardised nature of formal educational systems, which tend to establish a common foundation of knowledge [63]. Interestingly, this insight can also explain the high levels of acceptance and readiness to embrace smart city transition among participants (See Section 4.3). However, Beştepe and Yildirim [64] emphasised that a higher level of education does not necessarily guarantee digital literacy or relevant skill levels. Some professions are more directly exposed to digitalisation and ICT-led changes. Subsequently, education level can be supplemented by post-education experience related to professions, work environments, and fields of expertise.

Thus, local governments and authorities should promote and support lifelong learning for diverse socio-demographic individuals through inclusive policies such as the ‘Digital Inclusion of All’ initiative by the UN International Telecommunication Union, ‘Making lifelong learning a reality’ by UNESCO [65], the ‘Learning Cities’ policy in Romania, and Future Melbourne 2026—Priority Area: Learning Cities as an intercultural cities programme in Australia [66]. Consequently, this inclusive policy can bridge the divide between technological advancements and social acceptance of the changes, and revitalise disengaged residents and communities.

More importantly, learning cities initiatives will eventually prevent further digital divide within communities by establishing inclusive social cultures [67]. To further support lifelong learning and smart city adaptation, European countries including France, Germany, Ireland, Italy, Spain and Poland suggest providing micro-credential courses to support vulnerable communities and individuals to obtain new skills and knowledge, which can increase employment potential in smart cities [68]. Hence, policies and strategies should be developed based on a long-term and inclusive strategic vision ensuring that individuals and communities have opportunities to continuously learn new skills and adapt to the transformed environment before, during, and after the transition.

5.3. Income Level

Variations in reaction among income groups can be explained by the financial security felt by those in the highest income bracket, which makes them less vulnerable to potential disruptions caused by smart city transitions [69]. Lower-income groups may feel more vulnerable due to concerns about job security as smart technologies have the potential to displace their roles [22]. In contrast, higher-income groups may perceive smart city initiatives as opportunities for economic advancement and financial gain rather than as disruptive forces.

The Department of Education [70] identified a positive correlation between academic degrees and income levels, revealing that individuals with a bachelor’s or higher degree earn about 75% more than those with a high school (or year 12) education or below. This finding reveals that income level can outweigh the academic degree, indicating that individuals with more financial resources tend to feel more comfortable with the changes regardless of individuals’ educational background. As a good example of policy responding to these issues, Australian Housing and Urban Research Institute [71] released a policy recommendation “Supporting employment in smart cities through affordable housing” to support low or no-income earners during the transition by providing employment opportunities for smart city development.

Therefore, local governments and policymakers should promote both the immediate and long-term financial benefits of the smart city transition while reducing resistance stemming from job insecurity. Through inclusive and strategically organised smart city transition plans including job creation through smart city transition projects, as a colloquial term, ‘One Stone, Two Birds’ can be realised, since the growing demands for smart infrastructure to support ageing populations can be achieved along with job security simultaneously. The increasing demand for social infrastructure, which is commonly known as the Silver Economy, is currently generating new employment opportunities in smart cities benefiting a wide range of socio-demographic groups [53,72].

5.4. Gender

Females were found to experience elevated levels of depression and anxiety compared to males in this context. The finding aligns with the research highlighting that women have greater safety concerns when engaging with digital technologies as they are more frequently targeted by cyberbullying, harassment, and privacy breaches compared to men [50,73]. Consequently, potential risks and uncertainties contribute to heightened anxiety and hesitation among females.

Thus, local governments need to account for gender-specific differences when they develop smart city plans to mitigate the negative social reactions (frustration and depression) experienced by females during the transition to smart cities. It is also vital to create an inclusive environment and policy that empowers females to participate in the transition process such as the Australian Digital Inclusion Strategy [74]. Furthermore, local governments and authorities should also consider physical spaces and their uses when they plan and design the transformed urban environments in conjunction with digital inclusion policies [75]. There is certain gender-specific needs, and these can be addressed through smart technologies and considerate urban spatial designs, e.g., ‘Breastfeeding Hub’ in Milton Keynes, UK, and ‘Women-friendly Zone’ in South Korea [76].

5.5. Change Management Strategy

Individuals with tertiary education place significant emphasis on the importance of knowledge enhancement and participation opportunities in smart city initiatives since they consider them as both effective change management and resistance mitigation strategies. Al-said and Zaidan [77] emphasised the role of local governments to foster opportunities for local communities to become involved in planning and governing from the outset of smart city transition. Higher education exposes individuals to complex systems and critical thinking skills, increasing their ability to understand the complexities and potential impacts of smart city technologies. In contrast, those with lower educational backgrounds may be more prone to resisting smart city transitions due to limited understanding or misinformation regarding the changes [27].

Thus, local communities and governments need to prioritise knowledge enhancement to support the smart city transition programmes and dismiss misconceptions, therefore reducing resistance. Furthermore, education and training for knowledge development should introduce individuals to a broader spectrum of social, economic, and cultural perspectives, enhancing their awareness of how different groups might perceive and respond to the smart city transition [78]. Lv et al. [4] suggested that local governments and policymakers should avoid ‘one-size-fits-all’ approaches as societies are built upon diverse cultural and demographic backgrounds. With a strengthened understanding, individuals and communities can incorporate diverse perspectives into knowledge improvement. Indeed, Shayan and Kim [79] recognised that risk management and change management during the smart city transition process are closely interrelated since social and technological changes introduce uncertainties.

Accessible and timely support for technical issues is instrumental not only for reinforcing gained knowledge and maintaining operational stability of transformed environments but also for building user confidence in digital technologies post-transition [80]. Technical support centres can serve as local communication and continuous education hubs to foster a lifelong learning culture and ensure long-term success of smart city transition by promoting ongoing community engagement and support [81]. Currently, various technical support centres around the world such as the US Urban Transitions Mission Centre, the Centre for IT-Intelligent Energy Systems in Denmark, and academic centres established based on the UK National Cyber Strategy, are providing technical support for local communities [82]. Thus, both (1) knowledge development policies as short-term change management strategies, (2) timely technical support for those actively exploring smart technologies and engaging with the transformed environments as a long-term plan, should be implemented in a complementary manner.

Although smart technologies contribute to enhancing quality of life and promoting the sustainability of communities, the associated trade-offs and concerns associated with data privacy, cybersecurity, and use of various sensors and surveillance systems must be thoroughly considered and incorporated into smart city data management and data privacy policies. When these sensitive issues are not properly addressed, individuals and communities’ negative social responses and resistance toward the smart city transition will be amplified [34]. Particularly, transparency regarding the collection and use of personal data must be clearly communicated to establish public trust and enhance positive perceptions toward smart technologies such as smart devices, smart sensors, and smart home systems. To increase public participation and strengthen mutual trust in smart city digitalisation policies, citizens’ social reactions and concerns should be incorporated into data privacy protection policies—e.g., Data Privacy Policy in Manchester, UK [83]. Lim et al. [84] found that citizens’ positive perceptions toward smart technologies and urban digitalisation can be enhanced when policymakers address data privacy-related concerns. Therefore, policymakers and local governments must maintain a careful balance between digitalisation and data privacy by implementing change management policies and strategies that can effectively address concerns related to cybersecurity, the commercial use of personal data, and public surveillance.

Furthermore, digital inclusion and governance remain foundational goals across global smart city initiatives. For example, Barcelona (Spain), Manchester (UK), and Adelaide (Australia) share common commitments to citizen and community-centric inclusive digital with transparent and collaborative governance model. However, each city has its unique smart city policies based on socio-demographic profiles [85,86,87]. In Barcelona, digital inclusion is embedded within national strategies focusing on digital rights with particular emphasis on ageing populations and immigrant integration due to the current socio-demographic shifts and social cohesion challenges. In contrast, Manchester adopts a more localised digital strategy focusing on bridging digital divide between communities, and empowering young adults for joining the future digital workforce. Adelaide adopts a multi-layer strategy that integrates national, state, and local policies to foster social coordination across diverse regions and socio-demographic groups. These variations highlight the importance of understanding smart city transitions not only through a global lens. A context-sensitive approach is essential for interpreting and advancing smart city policies that are both inclusive and socially responsive.

6. Conclusions

Smart city programmes profoundly affect not only the operational aspects of cities but also their social dynamics, leading to major shifts in social structures, behaviours, interactions, and norms. People’s responses to these changes are diverse and influenced by their perceived advantages and drawbacks of smart city implementations, personal values, priorities, and individual experiences with technology. Understanding the range of social responses to the changes and developing strategies to minimise resistance is crucial, since these efforts directly affect the acceptance and success of the smart city transition. The most common reactions among people were identified as a positive attitude toward embracing and adapting to transformed environments, while ageing populations are recognised as more vulnerable to the transition than younger generations. Individuals in the highest income bracket tend to experience lower levels of Shock, Frustration, and Depression regarding smart city transition. Females were observed to experience comparatively higher levels of depression and anxiety than males in this context.

For a successful smart city transition, improving knowledge of digital technologies and reinforcing acquired knowledge through timely technical support were identified as the most effective change management strategies. Hence, the research recommends that local governments establish technical support centres to facilitate a smooth transition to smart cities. Finally, the research is expected to offer valuable insights into individuals’ and groups’ social reactions and attitudes toward the transition into smart cities, and suggest strategies for local governments and policymakers to effectively support people in adapting to the transformed environments while mitigating resistance to the changes through targeted group and gender-specific support and policies. While the sample size was limited and the research was primarily conducted in South Australia, the findings will contribute to a broader and deeper understanding of individuals’ social reactions and responses toward smart city transitions, as well as assist local governments and policymakers in developing more inclusive and socio-demographically tailored plans. Although caution should be exercised when generalising these results beyond the study population, the observed patterns suggest meaningful directions for further research. Future studies with larger and more diverse samples across other geographical regions such as North/South America, European Countries, and Asian Countries, are essential to explore how different populations respond to smart city transitions.