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American Journal of  Smart 
Technology and Solutions (AJSTS)

Optimising Customer Service Delivery and Response Time through AI-Enhanced
Chatbots in Facilities Management-A Mixed-Methods Research

Mai Alhammadi1*

Volume 2 Issue 2, Year 2023
ISSN: 2837-0295 (Online)

DOI: https://doi.org/10.54536/ajsts.v2i2.2206
https://journals.e-palli.com/home/index.php/ajsts

Article Information ABSTRACT

Received: October 17, 2023

Accepted: November 19, 2023

Published: November 22, 2023

The present study aimed to assess the effect of  AI-enhanced chatbots that optimize 
customer service delivery and response times on facility management. It utilised the 
Technology Acceptance Model (TAM) and Social Response Theory (SRT) for this 
purpose. The research adopted a mixed-methods methodology aimed to explore the 
multiple perspectives of  10 facility managers and facility service providers affiliated with 
facilities management departments in the UAE, Qatar and Saudi Arabia regarding the 
benefits and challenges of  AI-enhanced chatbots. This research used correlation analysis 
and regression to examine the relationships between variables. Correlation analysis, 
using SPSS 24.0, showed strong positive correlations between five AI-enhanced chatbot 
factors: Perceived Usefulness (PU), Perceived Ease of  Use (PEoU), Behavioural Intention 
to Use (BIoU), Responsiveness (RP), and User Satisfaction (US) (Pearson Correlation 
Coefficient>0.7). Regression analysis indicated a significant impact of  all these variables 
on facilities management (p<0.05). The study found that AI-enhanced chatbots in facilities 
management improve communication, responsiveness, and operational efficiency. They 
automate workflows, handle manual tasks, predict failures, and respond to customer 
queries. However, challenges include technical issues, limited human-human interaction, 
system quality and security, and user adoption. Chatbots deliver productivity gains and 
are used for automated reporting, identifying hazards, conducting briefings, managing 
meetings, providing training, supporting teamwork, ensuring well-being, and enhancing 
customer service.

Keywords
Artificial Intelligence, Facilities 
Management, Chatbot, Natural 
Language Processing, Service 
Delivery, Response Time

1 MEEM 48 Engineering Consultancy, Abu Dhabi, UAE
* Corresponding author’s e-mail: eng.maialhammadi@hotmail.com

INTRODUCTION
Text-based conversational systems or conversational 
Artificial Intelligence (AI) referred to commonly 
as chatbots are the designed software systems for 
human interaction using natural language processing 
(NLP) (Gnewuch et al., 2022; Lin, 2023). Chatbots are 
categorised based on two objectives including task 
oriented and non-task oriented; task-oriented chatbots 
are those compatible highly with the information retrieval 
requirement for effective decision making. Thereby, 
chatbots have gained widespread attention in various 
industries like finance, e-commerce and healthcare due to 
growing demand for convenient and efficient customer 
service (Gnewuch et al., 2022). Notwithstanding, using 
live chat interfaces to communicate with customers 
in e-commerce settings improves real-time customer 
service to obtain information for product details or 
assistance in solving technical problems. Chatbots have 
thus enhanced the two-way communication significantly 
affecting customer satisfaction, trust, word-of-mouth 
(WOM) intentions and repurchase (Adam et al., 2021). 
Over 100,000 chatbots have already been created as of  
2017 on Facebook Messenger only for customer service 
delivery through instant messaging apps and social media 
(Meyer-Waarden et al., 2020). 
Facilities management or facility management has been 
called multiple things, including asset management, 
business infrastructure management and invisible service 
for building. It has evolved by merging as a business 

support service and building maintenance management 
business (Atkin & Bildsten, 2017). Interoperability 
capabilities of  Building Information Modeling (BIM) 
are effective in the application of  facility management, 
construction and building maintenance stages 
referring to technology-based solutions for improving 
inter-organisational productivity and collaboration 
(Ghaffarianhoseini et al., 2017). A chatbot is developed 
as a friendly user interface to improve the user experience 
and efficiency in facility management, integrating BIM, 
NLP and ontological techniques to generate immediate 
responses (Lin, 2023). A delayed response time negatively 
impacts usage intentions and the social presence of  
users, affecting customer service in facility management 
(Gnewuch et al., 2022). 
Nonetheless, it is worth noting here that a key challenge 
faced in designing conversational user interfaces is to 
make sure that the conversations feel human-like and 
natural. Thus, to increase perceived humanness, chatbots 
may use response delays; however, this can affect user 
satisfaction, especially in situations where fast response 
times are expected, i.e., customer service. Service delivery 
and system response time are correlated, being critical 
factors for productivity and user satisfaction among 
chatbots. For example, when the response time is slow 
in customer service live chats, it creates negative website 
quality perceptions among users (Gnewuch et al., 2018). 
Additionally, the chatbot provides improved functionality 
in real-time scenarios, emphasising its usefulness within 



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an organisation dealing with relevant challenges, including 
complex business domains, cost factors and limited 
responsiveness, etc. (Majumder & Mondal, 2021). 
Although previous research has widely discussed AI-
enhanced chatbots in various fields, implementing them 
in facilities management for optimising service delivery 
and response time has been discussed rarely. It has 
been noted that chatbots in facility information delivery 
solution promises three benefits, including handling 
large amounts of  complex data, containing tedious 
information and having high mobility, reducing time to 
solve users’ query with intuitive user interfaces (Chen & 
Tsai, 2021). However, with a great many benefits, there 
might be a few challenges associated with AI-enhanced 
chatbots as well. Therefore, the current research opted 
for a mixed-methods approach to examine the impact 
of  AI-enhanced chatbots optimising customer service 
delivery and response time on facility management using 
the Technology Acceptance Model (TAM) and Social 
Response Theory (SRT). It is also aimed at exploring 
the in-depth perspectives of  personnel linked with the 
facilities management department in the UAE, Qatar 
and Saudi Arabia on the benefits and challenges of  AI-
enhanced chatbots optimising customer service delivery 
and response time.

LITERATURE REVIEW 
AI-Enhanced Chatbots
The first chatbot ‘ELIZA’ was developed in the 1960s; 
however, broader organisational interest was not gained 
until the 2010s. On Facebook alone, 300,000 chatbots 
were developed at the beginning of  2016, having 
common applications in e-commerce, customer service, 
workplace employee support and healthcare (Gnewuch 
et al., 2022). It is estimated that conversational agents can 
reduce the costs of  current global business to around 
$1.3 trillion by solving 265 billion queries of  customers 
per year by reducing their response times, freeing up the 
human workload for different work and dealing with the 
80% of  routine questions (Adam et al., 2021). Siri was 
developed in 2010 by Apple, which makes conversations 
and resolves inquiries using voice commands through 
messengers integrating with video, audio, and image files. 
Later, in 2011, a Watson names chatbot was developed by 
IBM and Google Now was developed in 2012 by Google. 
Cortana, the Microsoft-designed personal assistant and 
Alexa, a human-automation chatbot, were designed 
in 2014. Notably, Chatbots are categorised into various 
categories; knowledge domain, service provided, response 
generation method, human-aid, communication channel 
and permissions (Adamopoulou & Moussiades, 2020). 
Siri and Alexa are task-based dialog agents which are 
known for creating short conversations, including 
making phone calls, describing routes, etc. However, 
conversational AI-based chatbots are non-task-oriented 
dialog systems used in customer service for various 
purposes. These are focused on imitating conversations 
like humans focused on certain tasks. Xioaice is developed 

as a non-task-oriented agent by Microsoft Pecking, which 
is like a friend (Akhtar et al., 2019). Additionally, OntBot 
was developed using NLP to ease user interactions, 
providing support that can process e-commerce queries. 
Ask Diana is a chatbot known for providing information 
relevant to disaster-related information delivery in 
facilities management (Chen & Tsai, 2021). Besides, 
some of  the Facebook Messenger-based chatbots 
that generate a response to users by interacting with 
them include DBpedia, SOGO, Arts-bot, SHIHbot, 
CISEC, E-Commerce Website Chatbot, Nombot, and 
CALMsystem (Maroengsit et al., 2019).

AI-Enhanced Chatbots in Facilities Management
AI components, including “pattern recognition” and 
“machine learning,” integrate a potential value in the 
AI-enhanced alternative workflow for humans. The 
continuous advances of  smart digital tools are effectively 
operating in improving customer services and solving 
problems (Burry, 2022). Furthermore, the access to 
AI-enhanced chatbots anywhere and anytime with the 
integration of  cloud-BIM and augmented reality (AR) 
offers extreme assistance for facilitating decision-making. 
It provides support to facility managers contributing 
towards customer service (Su et al., 2021). Similar to 
humans, chatbots offer customer service, integrating 
relationship management with consumers. These include 
relational-oriented behaviours and functional-oriented 
behaviours assisting consumers in buying decisions. 
Despite the fact that chatbot has no emotions, which is 
considered its dark side, it has attained multiple benefits 
in faster service delivery, satisfying immediate customer 
needs. Leveraging the fact that computers are social 
actors, traits of  chatbots are considered trustworthy, 
reducing the spread of  negative WOM (Su et al., 2021).
AI chatbots are used in organisations to help staff  
members access business information and documents 
online, offer translation services, gather data from 
various sources, and format gathered data to adhere to 
organisational guidelines. By facilitating easy access to, 
discovery of, and management of  work resources, AI 
chatbots are said to enhance employees’ experiences 
(Gkinko & Elbanna, 2023). In addition to being used 
increasingly often in working settings to help employees, 
AI chatbot systems are utilised to support customers 
in a range of  industrial sectors (including healthcare, 
banking, retail, and education). AI chatbots have shifted 
from emphasising the perspective of  employees to that 
of  either designers or customers (Gkinko & Elbanna, 
2023). The validity of  compliance and persuasion 
strategies in technology-based self-service contexts is 
being debated as chatbots replace human customer care 
representatives. Conversational Agents provide 24-hour 
electronic channels for consumers, offering quick, easy, 
and affordable communication. However, the nature 
and caliber of  these exchanges vary significantly. For 
example, consumers use more profanity and speak for 
longer periods, which could affect their cooperation in 



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response to chatbot suggestions and requests. Therefore, 
understanding the differences between humans and 
chatbots is crucial for effective customer service (Adam 
et al., 2021).

AI in Customer Service Delivery and Response Time 
in Facilities Management
According to Gkinko and Elbanna (2023), a major 
challenge of  balancing service quality and service efficacy 
is faced by customer service providers. Therefore, the 
potential benefits of  chatbots are considered significant 
for customer self-service, including reduced costs, time 
efficiency and enhanced customer experience. It impacts 
improving provider-customer encounters and service 
quality by being a cost-saving as well as time-saving 
approach (Gkinko & Elbanna, 2023).
Using AI-based chatbots is all about easing human life by 
knowing information or news to make recommendations, 
suggestions, shopping services, etc. AI-based 
communication agents support facility management in 
a wide range of  services that improve customer service, 
including providing customer support, scheduling 
meetings, giving financial assistance, suggesting policies, 
advising insurance policies, offering administration-based 
services, etc. (Nirala et al., 2022). Big data simplifies the 
role of  CRM staff  by providing them with advanced 
insights into client behaviour patterns, enabling them 
to manage each customer effectively. This data also 
allows for better engagement across channels, enabling 
manufacturers to assess customer reactions to new 
products on social networks and media. This allows them 
to pinpoint the optimal CRM approach for each client, 
ultimately resulting in cost-effectiveness for all CRM 
actions (Anshari et al., 2019).
Facility/asset owners and operators have accumulated 
enormous amounts of  data over the years but frequently 
lack the tools to utilise them fully. Humans have a 
very limited ability to interpret the given data, which 
is where AI comes into play since it can provide top 
management with well-reasoned, well-supported advice 
on which to base a decision. It is crucial to understand 
that intermediate managers, which include facility and 
asset managers, might be disregarded in this situation 
since mission-critical choices are made much more 
quickly than normally (Atkin & Bildsten, 2017). In the 
research by Chen & Tsai (2021), to implement the created 
information distribution strategy, a chatbot based on 
LINE, an instant messaging service with the greatest 
market share in Taiwan, was prototyped. The LINE 
chatbot offers customers two primary interfaces via 
which they can get or utilise rules to query the facility 
management data.
The efficiency is maximum, and speed is almost twice 
as quick when the participant chooses an item from the 
chatbot’s clickable menu to get information. Additionally, 
a user’s performance was the same while utilising the 
chatbot and the facility management platform to input 
natural language to get information.

Theorisation of  Constructs and Hypothesis 
Development 
Technology Acceptance Model (TAM)
The utilisation of  emerging technologies, such as AI 
and service robots, and their acceptability by users are 
predicted using TAM. The service robot acceptance 
model (sRAM), which incorporates relational and social-
emotional components, adjusts and improves the TAM. 
TAM attempts to study how external factors affect a 
person’s internal beliefs, attitudes, and intentions by 
drawing on the Theory of  Reasoned Action. TAM 
analyses two crucial factors-the Perceived Usefulness 
(PU) and the Perceived Ease of  Use (PEoU)-to study the 
behaviours associated with technology adoption (Meyer-
Waarden et al., 2020). These two aspects are related to 
the motivational factors which create an influence on 
behavioural intentions and user satisfaction. PU reflects 
upon the beliefs of  users about their experiences of  using 
technology, whereas PEoU is based on the perceived 
system quality of  chatbot for the user with limited 
response time and easy availability of  chatbot systems. 
The motive is to provide reliable information for user 
support needs, increasing levels of  trust and satisfaction 
(Nguyen et al., 2021). 
PU is known as the degree to which it is believed that 
a particular system would improve an individual’s job 
performance. PEoU refers to a person who perceives 
that using a specific system would be free of  effort. 
Therefore, AI-enhanced chatbots are perceived as 
easy to use by users for acquiring quick knowledge and 
system-wide optimal solutions free from human fatigue 
and error. Chatbots enhance service delivery within four 
dimensions, including reliability, empathy, responsiveness 
and tangibles. It is the distinction that increases the 
intention to reuse the chatbot (Meyer-Waarden et al., 
2020). TAM allows for meeting the requirements of  
social influence, complexity and ease of  use, which affects 
the user’s choice of  technology selection (Humairoh & 
Susilo, 2023). 

Social Response Theory (SRT)
A set of  social cues are posited from computers in social 
response theory (SRT), such as using natural language, 
interacting with others, triggering mindless responses 
from humans, and playing social roles irrespective of  
whether the cues are rudimentary or not. Thereby, it is 
noteworthy here that a chatbot’s response time may trigger 
social responses that are shaped by the social expectations 
of  users. The persuasiveness of  the chatbot’s messages 
is influenced by response time (Gnewuch et al., 2018). 
In digital contexts, anthropomorphism is the attribution 
of  human-like behaviours, characteristics and emotions 
to non-human agents (Adam et al., 2021). Chatbots 
mainly interact with customers through messaging-based 
interfaces in a real-time dialogue via dynamic and physical 
representations. However, some believe that due to the 
immediate response of  chatbots, they may appear as 
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increase social presence, user satisfaction and perceived 
humanness (Gnewuch et al., 2022). It creates an impact 
on the behavioural intention to use in customer service 
delivery and dynamic response times (Adam et al., 2021). 
Based on the theoretical foundation of  TAM and SRT, 
the following hypotheses have been formulated to test 
the effectiveness of  AI-enhanced chatbots on facilities 
management. Figure 1 depicts the conceptual framework 
of  the research in which the variables on the left-hand side 
are independent, i.e., factors of  AI-enhanced chatbots 
optimising customer delivery service and response time 
tested to examine their impact on facilities management.

H1: The impact of  the perceived usefulness of  AI-
enhanced chatbots optimising customer service delivery 

and response time is significant on the dependent variable, 
i.e., facilities management.

H2: The impact of  perceived ease-of-use of  AI-
enhanced chatbots optimising customer service delivery 
and response time is significant on facilities management.

H3: The impact of  behavioural intention to use AI-
enhanced chatbots optimising customer service delivery 
and response time is significant on facilities management.

H4: The impact of  responsiveness of  AI-enhanced 
chatbots optimising customer service delivery and 
response time is significant on facilities management.

H5: The impact of  user satisfaction of  AI-enhanced 
chatbots optimising customer service delivery and 
response time is significant on facilities management.

Figure 1: Conceptual Framework 
Source: Author

METHODOLOGY
Research Design
A mixed-method approach comprising quantitative data 
collection through surveys and qualitative interviews 
was employed in the current research. A pragmatic 
philosophical approach was opted to support the 
subjective findings with objective conclusions gathering 
both qualitative and quantitative data. The purpose of  
the research was to examine the perceptions of  facility 
service providers who have implemented chatbots to 
explore their significance in customer service delivery 
and response time in facilities management. Using mixed 
methods, the research offers empirical and theoretical 
insights into the practical implementation of  chatbots in 
this industry and gauges their effectiveness.

Data Collection
The researcher gathered qualitative and quantitative 
data, both through primary sources. The quantitative 
data was collected by distributing a close-ended survey 
questionnaire among the target population. The items 

of  the questionnaire were adapted from the theorisation 
of  constructs using TAM and SRT and relevant existing 
literature. Perceived usefulness, perceived ease-of-use, 
behavioural intention to use, responsiveness and user 
satisfaction were the selected five constructs with three 
items each. Each item was examined based on a five-
point Likert scale ranging from 0 to 4, in which 0 refers to 
strongly agree, whereas 4 refers to strongly disagree. The 
interview questions were centred on specific research 
objectives to identify the key benefits and challenges of  
using AI-enhanced chatbots optimising service delivery 
and response time in facilities management. However, 
the survey and interview questions were both modified 
to fit the current and recent research, following the 
instructions of  some experts in facility management to 
ensure comprehensiveness, consistency and readability. 
Also, convergent validity (AVE) and reliability of  items 
were tested. 

Sampling
The targeted population of  the current research was 



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the personnel working in facilities management. The 
targeted population was approached to fill out survey 
questionnaires through LinkedIn and other social 
media platforms. Therefore, these respondents were 
sampled through a random sampling approach, and 270 
respondents who finished the complete survey have 
experience working with AI-enhanced chatbots for 
improved service delivery and response time in facilities 
management. Furthermore, 9 respondents for interviews 
were sampled using purposive sampling as they were the 
experts in their field. Interview respondents were the 
facility managers and facility service providers who have 
implemented chatbots within the Middle East. For both 
interviews and surveys, equal respondents, i.e., (90 each 
for surveys and 3 each for interviews) were approached 
from UAE, Qatar, and Saudi Arabia, as these could be 
approached easily by bearing limited costs. 

Analysing Sample’s Profile
Figures 2,3,4 and 5 below depict the demographics of  the 
targeted respondents. 
The majority of  the respondents were aged between 
30 and 39 years, i.e., 69.3%, whereas 37, i.e., 13.7% 
respondents were aged between 20 and 29, 26, i.e., 9.6% 
were within the age group of  40-49 years and only 20, 
i.e., 7.4% respondents were aged 50 years and above as 
shown in Figure 2.
Figure 3 shows the gender demographics, such that 158 
(58.5%) were male, whereas 102 (37.8%) were females 
who participated in this research. 10 (3.7%) respondents 
preferred not to mention their gender.
Figure 4 below depicts the designation of  respondents. 
It shows that 106 (39.3%) of  respondents were Facility 
Service Providers, followed by 95 (35.2%) by Facility 
Managers. 53 (19.6%) were End Users, and 9 (2.6%) 

Figure 2: Age Demographics
Source: Author

Figure 3: Gender Demographics
Source: Author

Figure 4: Job Designation of  Respondents
Source: Author

Figure 5: Job Experience of  Respondents
Source: Author

were IT Specialists. The remaining 7 (3.3%) were Other 
respondents, suggesting that these categories cover most 
respondents.

Data Analysis
SPSS 24.0 was used for carrying out numerical analysis. 
Using this statistical tool, reliability and convergent 
validity were tested, and correlation, regression and 
exploratory factor analysis were performed to test the 
association of  independent and dependent variables of  
the research. Besides, thematic analysis was conducted 
to analyse the interview responses following the stages 

of  coding transcripts, identifying keywords, formulating 
themes and analysing them.

RESULTS
Quantitative Analysis 
This section of  the research contains results for the 
survey questionnaires examined using SPSS.

Reliability Analysis
The reliability of  a scale was tested in this research 
to assess the internal consistency of  the variables. In 
reliability analysis, the value of  Cronbach’s alpha is 



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obtained, and values are tested to range between 0 and 1, 
with 0.7 being the lowest accepted value (Hajjar, 2018). 
As shown in Table 1 below, the value of  Cronbach’s 
alpha for each construct is obtained greater than 0.9, 

showing high internal consistency of  all the statements, 
showing that these constructs are measuring a similar 
concept underlying AI-based chatbots in facilities 
management.

Table 1: Cronbach’s Reliability Test
Dimension Name Number of  Statements Cronbach’s Alpha (ą) (Standardised) N=270
Perceived Usefulness (PU) 3 0.931
Perceived Ease of  Use (PEoU) 3 0.960
Behavioural Intention to Use (BIoU) 3 0.963
Responsiveness (RP) 3 0.971
User Satisfaction (US) 3 0.989

Source: Author

Convergent Validity and Exploratory Factor Analysis
The measurement model must meet three key conditions 
to achieve convergent validity: the factor loadings of  
all the variables must be higher than 0.5, the Average 
Variance Extracted (AVE) must be greater than 0.5, 
and the composite reliability for each construct must be 
higher than 0.7 (Nguyen et al., 2021). 
The criterion followed in this research is Fornell and 
Larcker’s method, which was used to analyse the AVE 
values for each construct shown in Table 2 below. The 
current measurement model confirmed validity as it met 

all three conditions for all the latent constructs since the 
AVE value for all constructs, including PU, PEoU, BIoU, 
RP and US, is greater than 0.5, lying within the range of  
0.7 and 0.9.
Factor loadings were examined using the Kaiser-Meyer-
Olkin (KMO) method. As shown in Table 2 below, all the 
values of  the factor loadings are greater than 0.5; therefore, 
all variables are acceptable, fit and unidimensional in the 
current research. Explained Variance (%) depicts that the 
factors capture a large data portion in Variance of  data if  
percentages are higher, as shown in Table 2 below.

Table 2: Exploratory Factor Analysis 
Dimension Name Items Factor 

Loadings
Kaiser- Meyer-
Olkin (KMO) 
Values

Explained 
Variance 
(%)

Mean (Std. 
Deviation)

AVE

Perceived Usefulness (PU)
AI-enhanced chatbots increase the 
effectiveness and quality of  facility 
management services.

PU1 0.925 0.732 88.077% 11.26 (0.912) 0.849

AI-enhanced chatbots can speed 
up the response time to service 
requests in facilities management.

PU2 0.866 10.98 (0.971)

AI-enhanced chatbots can assist 
in automating the facilities 
management process, making it 
more efficient and beneficial.

PU3 0.851 11.35 (0.899)

Perceived Ease of  Use (PEoU)
I find AI-enhanced chatbots in 
facilities management easy to use.

PEoU1 0.940 0.770 92.747% 11.27 (0.971) 0.832

I often use AI-enhanced chatbots 
to navigate and communicate when 
making service requests or queries 
about facilities management.

PEoU2 0.906 10.97 (0.952)

AI-enhanced chatbots make facility 
management service’s access and 
requests easier.

PEoU3 0.937 11.26 (0.912)

Behavioural Intention to Use (BIoU)



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In the future, I intend to use AI-
enhanced chatbots for facility 
management service requests and 
queries.

BIoU1 0.955 0.749 93.113% 10.93 (0.963) 0.871

AI-enhanced chatbots are 
equally appropriate in these new 
technology-based self-service 
situations and are designed to 
convince consumers to comply 
with or adapt to a specific request.

BIoU2 0.946 10.98 (0.971)

I will use AI-enhanced chatbots 
because they create the impression 
of  intelligence in a non-human 
technology agent and make 
conversations feel more natural in 
a customer service setting.

BIoU3 0.892 11.26 (0.912)

Responsiveness (RP)
When employing AI-enhanced 
chatbots for facility management 
services, I frequently get a response 
or solution immediately.

RP1 0.941 0.756 91.714% 11.27 (0.971) 0.825

In contrast to their rule-based 
predecessors' somewhat static 
replies, AI-enhanced chatbots are 
adaptable and demonstrate empathy 
when responding to the user's input 
in facilities management.

RP2 0.884 10.97 (0.952)

Users' perceptions of  humanness 
and social presence are 
strengthened by AI-enhanced 
chatbots' responsiveness, which 
also increases their satisfaction with 
the chatbot engagement.

RP3 0.927 11.27 (0.971)

User Satisfaction (US)
Real-time AI-enhanced chatbots 
have made customer support a 
two-way conversation, which has 
a significant effect on customer 
satisfaction, repurchase intentions, 
and trust.

US1 0.912 0.536 67.946% 10.93 (0.963) 0.707

The response time of  an AI-
enhanced chatbot is an important 
factor that affects user satisfaction 
and other aspects of  perceived 
system quality.

US2 0.893 10.98 (0.971)

Customers are more satisfied 
interacting with support chatbots 
that give dynamically delayed 
replies than those that send near-
instant responses.

US3 0.233 10.98 (0.971)

Source: Author

Correlation Analysis
Correlation analysis is used test the relationship between 
multiple variables of  the research. When two variables 
are tested to be correlated their covariance is divided by 

the standard deviations known as Pearson Correlation 
Coefficient ‘r’ (Kafle, 2019). The value of  ‘r’ lie between 
+1 and -1 such that values greater than 0.7 depict strong 
correlation. As shown in the Table 3 below, all the five 



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factors of  AI-enhanced chatbots optimising service 
delivery and response time including PU (0.979**), PEoU 
(0.978**), BIoU (0.991**), RP (0.989) and UP (0.952**) 
are strongly correlated with facilities management since 
the values are greater than 0.7.

Regression Analysis
The relationship tested between two or more independent 
and dependent variables of  interest is evaluated using 

regression. The sig value or p-value is tested to determine 
the impact of  independent variables on the dependent 
variable, which should be less than the threshold value 
of  0.05 (Kafle, 2019). As shown in Table 4 below, the sig 
values of  all the independent variables of  the research are 
0.000, which depicts that the impact of  all the five factors 
of  AI-enhanced chatbots optimising service delivery and 
response time, including PU, PEoU, BIoU, RP, UP is 
significant on facilities management.

Table 3: Correlations 
Perceived 
Usefulness 
(PU)

Perceived 
Ease of  Use 
(PEOU)

Behavioural 
Intention to 
Use (BIoU)

Responsiveness 
(RP)

User 
Satisfaction 
(US)

Facilities 
Management 
(FM)

Perceived 
Usefulness (PU)

1

Perceived Ease of  
Use (PEOU)

.971** 1

Behavioural 
Intention to Use 
(BIoU)

.970 .981 1

Responsiveness 
(RP)

.945 .961** .991** 1

User Satisfaction 
(US)

.939** .985** .945** .931 1

Facilities 
Management (FM)

.979** .978** .991** .989 .952** 1

** Pearson Correlation is significant at p< 0.05 (2-tailed); N=270.
Source: Author

Table 4: Table of  Coefficients using Regression 
Unstandardised Coefficients Standardised Coefficients t Sig.
B Std. Error Beta

(Constant) .000 .001 -.121 .904
Perceived Usefulness (PU) .575 .004 .558 160.069 .000
Perceived Ease of  Use (PEOU) 1.041 .019 1.062 54.758 .000
Behavioural Intention to Use 
(BIoU)

-1.610 .022 -1.633 -74.050 .000

Responsiveness (RP) 1.426 .012 1.490 122.925 .000
User Satisfaction (US) -.433 .010 -.462 -44.863 .000

Source: Author

Thematic Analysis
Chatbots are increasingly becoming significant for 
opening important gateways to digital information and 
services within the domain of  facilities management (Lin, 
2023). Thematic analysis was conducted to identify the 
key benefits, challenges and applications of  AI-enhanced 
chatbots in facilities management from the in-depth 
perspectives and views of  personnel working first-hand 
with them. These are the conversational agents which are 
used to gather insights on interactive customer service 
and collaborative work support systems. However, the 
challenges of  implementation and maintenance might be 
crucial at the initial stage (Følstad et al., 2021). Chatbot’s 

impact on facility management has not been discussed 
widely. Therefore, a few questions were asked from 
the interviewees of  the current research to gather their 
diverse opinions.

Benefits
In your opinion, what are the key benefits of  AI-enhanced 
chatbots in facilities management?

Participant 1 Stated That
“We deploy chatbots to make communication easy with 
the facilities management system. The benefit is that we 
are shifting towards messages from audio calls, which 



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are preferred by users very often as response times are 
limited.”

Participant 3 Stated That
“We all know that AI is the game changer in responsiveness 
being more user-friendly. I think in service delivery, 
chatbots are making significant improvements in terms 
of  handling daily inquiries and routine tasks, making our 
team stay focused on the complex ones. So, I believe 
that the key benefit of  AI-based chatbots in building 
management systems is streamlining of  operations 
appreciated by clients in reduced response times.”

Participant 7 Stated That
“The best thing we have done using AI-enhanced 
chatbots is to cut resources in the era of  Smart Facilities 
Management when we are working on smart buildings 
and cities. In my experience, the fully automated 
workflows managed by chatbots excel some manual tasks 
which save resources in service delivery like predicting 
and maintaining failures, responding to customer queries 
and minimising downtime.”

Challenges
How do you think AI-enhanced chatbots pose challenges 
in facilities management?

Participant 1 Stated That
“AI-based chatbots have many benefits, but technical 
issues and limited human-human interaction pose few 
challenges since sometimes consumers expect and we 
also need chatbots to mimic human behaviour.”

Participant 2 Stated That
“A natural conversation’s design can increase user 
satisfaction; therefore, poor system quality might pose 
a challenge with respect to the security and reliability 
of  chatbot systems. It is a major concern for us to 
implement compliance and robust security measures 
to secure the sensitive information of  clients and our 
facilities provided.”

Participant 4 Stated That
“Sometimes employees and clients both feel resistant to 
using chatbots for the requirements of  customer service 
delivery due to lack of  training on chatbots reliability and 
user adoption. The key is to know about what are aspects 
of  facility management where chatbots are considered an 
authentic and reliable source.”

Practical Applications
What is the practical implementation of  AI-enhanced 
chatbots in facilities management?

Participant 2 Stated That
“Implementing a chatbot for facilities management 
is a viable and cost-effective source, increasing user 
preference towards messaging due to the sophistication of  

NLP. The chatbot’s software implementation means there 
are no significant equipment expenditures or installation 
expenses. It does not require long to notice productivity 
gains for building management and convenience gains for 
building users.”

Participant 4 Stated That
“We employ a chatbot-assisted facility management 
technology to automatically produce daily reports for 
building contractors by collecting conversations between 
subcontractors on instant messaging platforms. Also, 
it helps with facility management, identifying hazards, 
briefings, meetings, training, teamwork, well-being, and 
customer service.”

DISCUSSION 
This study focused mainly on the integration of  
TAM and SRT to shed light on the use of  chatbots 
optimising service delivery and response time in facilities 
management. Several key findings derived from the 
analysis are discussed as follows.
First, the relationship between PU of  chatbots 
optimising service delivery and response time and 
facilities management is supported (H1: The impact of  
the perceived usefulness (PU) of  AI-enhanced chatbots 
optimising customer service delivery and response time 
is significant on the dependent variable, i.e., facilities 
management). TAM has been advanced in analysing 
how new technologies are perceived and received for 
the outcomes of  ease of  use, social influence and 
complexity (Humairoh & Susilo, 2023; Tawafak et 
al., 2023). Consequently, each time a user facilitates 
automated conversations using a chatbot, the parameters 
of  user satisfaction are increased after getting well-
timed, correct and relevant data. It directly influences the 
perceived usefulness of  chatbots in facilities management 
(Humairoh & Susilo, 2023; Le, 2023). Supporting the 
stated fact, another research claimed that chatbots are easy 
to use (PEoU) due to the use of  NLP technology, which is 
becoming useful (PU) for both companies and customers 
as they are perceived to deploy human resources in other 
tasks within the business and save time (Lubbe & Ngoma, 
2021; Selamat & Windasari, 2021). Efficient use of  
technology such as chatbot strengthen overall customer 
experience decreases the number of  complaints, and 
encourages repurchase intention (Chen et al., 2021; Lubbe 
& Ngoma, 2021). Nonetheless, existing literature studies 
also highlighted that PU and PEoU are associated with 
perceived enjoyment, perceived risk, price consciousness, 
compatibility, and personal innovativeness (Kasilingam, 
2020). Similarly, the research supported the relationship 
between PEoU of  chatbots optimising service delivery 
and response time and facilities management is supported 
(H2: The impact of  the perceived ease-of-use (PEoU) 
of  AI-enhanced chatbots optimising customer service 
delivery and response time is significant on the dependent 
variable, i.e., facilities management).
Second, the advantages of  chatbots are various 



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associated with ease of  use, such as cost-effectiveness, 
availability, customer interaction, personal assistance and 
automation. Nevertheless, reduced flexibility can be a 
challenge for a few users, which influences their BIoU. 
However, the current research opposes the stated fact 
since it is supported (H3: The impact of  behavioural 
intention to use AI-enhanced chatbots optimising 
customer service delivery and response time is significant 
on facilities management). It is justified by Su et al. (2021) 
that chatbots, like humans, provide customer service 
by integrating relationship management and functional 
behaviours. Despite lacking emotions, they offer faster 
service delivery, satisfy immediate needs, and reduce 
negative word-of-mouth (Um et al., 2020). Consequently, 
Adam et al. (2021) analysed that AI-enhanced chatbots 
create an impact on the BIoU in customer service delivery 
and dynamic response times. Conversely, it was examined 
in the thematic analysis of  the current research that 
technical issues and limited human-human interaction 
may affect BIoU since sometimes consumers expect 
chatbots to mimic human behaviour.
In customer service delivery, user satisfaction is critical 
because if  service requests fail to meet a satisfactory 
response, it can cause crucial damage. Therefore, a 
chatbot is included for better responsiveness and fulfil 
customer satisfaction for service delivery, optimising 
response time for users (Hwang et al., 2019). Similarly, 
the results of  another past research examined that 
responsiveness and anthropomorphism directly influence 
customer engagement, service quality and customer 
satisfaction mediated by AI empathy and psychological 
safety whereas moderated by AI usability (Hui et al., 
2023). Consequently, the current research supported (H4: 
The impact of  responsiveness of  AI-enhanced chatbots 
optimising customer service delivery and response time 
is significant on facilities management and H5: The 
impact of  user satisfaction of  AI-enhanced chatbots 
optimising customer service delivery and response time is 
significant on facilities management). Meyer-Waarden et 
al. (2020) analysed that chatbots improve service delivery 
in four dimensions: reliability, empathy, responsiveness, 
and tangibles, increasing the intention to reuse them. 
Considerably, the thematic analysis also showed that 
the majority of  the interviewees agreed that AI-based 
chatbots in building management systems are streamlining 
operations appreciated by clients in reduced response 
times. In an online setting, businesses must be courteous 
when serving their customers and should provide them 
with an appropriate response. The operational efficiency 
of  chatbot systems may be greatly enhanced by their 
responsiveness, affecting user satisfaction (Yun & Park, 
2022).
Nonetheless, consumers desire customised 
communication despite the fact that it offers several 
alternatives for mobility and response. Their motto 
is “minimum time, best service.” Despite its many 
advantages, consumers and decision-makers who are 
unfamiliar with AI’s principles are greatly confused 

and misinterpreted (Khan & Iqbal, 2020). Similarly, 
interviewees in the current research claimed that 
implementing compliance and robust security measures is 
crucial for protecting client information and facilities, and 
understanding the aspects of  facility management where 
chatbots are considered authentic and reliable is essential.

LIMITATIONS 
The findings of  the current research are limited to 
facilities management, which might vary in any other 
context or industry. There will be a limited generalisability 
of  results to all industries of  TAM and SRT since, with 
rapid technological developments, the actual behaviour 
may change. The research limited data collection from 
respondents within a few countries of  the Middle East 
due to limited financial and time constraints. However, 
despite some of  these limitations, the research will be 
effective for the departments of  facilities management 
to use AI-enhanced chatbots for improving customer 
experiences and optimising service delivery and response 
time. Service quality will be a critical driver for enhancing 
customer satisfaction and trust for the facility managers 
when using chatbots for task management. However, 
researchers explore the role of  user training and 
education in the efficient adoption of  chatbots dealing 
with the ethical concerns of  privacy and data security. The 
study suggests future research with longitudinal studies, 
cross-industry research, qualitative research, controlled 
experiments, and ethical considerations.

CONCLUSION
AI-based communication agents enhance customer 
service in facility management by providing support, 
scheduling meetings, financial assistance, policy 
suggestions, and administration-based services. Delayed 
response time negatively impacts usage intentions and 
user social presence, affecting facility management. 
The study highlights the benefits of  AI-enhanced 
chatbots in facilities management, including improved 
communication, reduced response times, and automation 
of  routine tasks. These chatbots also contribute to 
resource savings and predictive maintenance. However, 
implementation challenges include technical issues, 
human-like interaction, and concerns about system 
quality, security, and reliability. The research emphasizes 
practical applications, such as hazard identification, 
meetings, teamwork, and customer service. The study 
supports the integration of  theoretical models and 
practical applications, finding a positive relationship 
between perceived usefulness, ease of  use, behavioural 
intention, responsiveness, and user satisfaction. 

REFERENCES
Adam, M., Wessel, M., & Benlian, A. (2021). AI-based 

chatbots in customer service and their effects on user 
compliance. Electronic Markets, 31(2), 427-445. 

Adamopoulou, E., & Moussiades, L. (2020). Chatbots: 
History, technology, and applications. Machine Learning 



Pa
ge

 
53

https://journals.e-palli.com/home/index.php/ajsts

Am. J. Smart. Technol. Solutions 2(2) 43-54, 2023

with Applications, 2, 100006. 
Akhtar, M., Neidhardt, J., & Werthner, H. (2019). 

The potential of  chatbots: analysis of  chatbot 
conversations. 2019 IEEE 21st conference on business 
informatics (CBI)

Anshari, M., Almunawar, M. N., Lim, S. A., & Al-
Mudimigh, A. (2019). Customer relationship 
management and big data enabled: Personalization 
& customization of  services. Applied Computing and 
Informatics, 15(2), 94-101. 

Atkin, B., & Bildsten, L. (2017). A future for facility 
management. Construction Innovation, 17(2), 116-124. 

Burry, M. (2022). A new agenda for AI-based urban 
design and planning. In Artificial Intelligence in 
Urban Planning and Design, 3-20. Elsevier. 

Chen, J.-S., Le, T.-T.-Y., & Florence, D. (2021). Usability 
and responsiveness of  artificial intelligence chatbot on 
online customer experience in e-retailing. International 
Journal of  Retail & Distribution Management, 49(11), 
1512-1531. 

Chen, K.-L., & Tsai, M.-H. (2021). Conversation-based 
information delivery method for facility management. 
Sensors, 21(14), 4771. 

Følstad, A., Araujo, T., Law, E. L.-C., Brandtzaeg, P. 
B., Papadopoulos, S., Reis, L., Baez, M., Laban, G., 
McAllister, P., & Ischen, C. (2021). Future directions 
for chatbot research: an interdisciplinary research 
agenda. Computing, 103(12), 2915-2942. 

Ghaffarianhoseini, A., Tookey, J., Ghaffarianhoseini, A., 
Naismith, N., Azhar, S., Efimova, O., & Raahemifar, 
K. (2017). Building Information Modelling 
(BIM) uptake: Clear benefits, understanding its 
implementation, risks and challenges. Renewable and 
sustainable energy reviews, 75, 1046-1053. 

Gkinko, L., & Elbanna, A. (2023). The appropriation of  
conversational AI in the workplace: A taxonomy of  
AI chatbot users. International Journal of  Information 
Management, 69, 102568. 

Gnewuch, U., Morana, S., Adam, M., & Maedche, A. 
(2018). Faster is not always better: understanding the 
effect of  dynamic response delays in human-chatbot 
interaction. 

Gnewuch, U., Morana, S., Adam, M. T., & Maedche, 
A. (2022). Opposing Effects of  Response Time in 
Human–Chatbot Interaction: The Moderating Role 
of  Prior Experience. Business & Information Systems 
Engineering, 64(6), 773-791. 

Hajjar, S. (2018). Statistical analysis: internal-consistency 
reliability and construct validity. International Journal of  
Quantitative and Qualitative Research Methods, 6(1), 27-38. 

Hui, Z., Khan, A. N., Chenglong, Z., & Khan, N. 
A. (2023). When Service Quality is Enhanced by 
Human–Artificial Intelligence Interaction: An 
Examination of  Anthropomorphism, Responsiveness 
from the Perspectives of  Employees and Customers. 
International Journal of  Human–Computer Interaction, 
1-16. 

Humairoh, M. A., & Susilo, W. H. (2023). Integration 

of  TAM and UTAUT-ISS Model: How Customers’ 
Service Chatbot Drove Users’ Behavior Intentions. 
Asian Journal of  Social Science and Management Technology. 

Hwang, S., Kim, B., & Lee, K. (2019). A data-driven 
design framework for customer service chatbot. 
Design, User Experience, and Usability. Design 
Philosophy and Theory: 8th International Conference, 
DUXU 2019, Held as Part of  the 21st HCI International 
Conference, HCII 2019, Orlando, FL, USA, July 26–31, 
2019, Proceedings, Part I 21, 

Kafle, S. C. (2019). Correlation and regression analysis 
using SPSS. Management, Technology & Social 
Sciences, 126. 

Kasilingam, D. L. (2020). Understanding the attitude and 
intention to use smartphone chatbots for shopping. 
Technology in Society, 62, 101280. 

Khan, S., & Iqbal, M. (2020). AI-Powered Customer 
Service: Does it Optimize Customer Experience? 
2020 8th International Conference on Reliability, Infocom 
Technologies and Optimization (Trends and Future Directions)
(ICRITO), 

Le, X. C. (2023). Inducing AI-powered chatbot use for 
customer purchase: the role of  information value and 
innovative technology. Journal of  Systems and Information 
Technology. 

Lin, W. Y. (2023). Prototyping a Chatbot for Site Managers 
Using Building Information Modeling (BIM) and 
Natural Language Understanding (NLU) Techniques. 
Sensors, 23(6), 2942. 

Lubbe, I., & Ngoma, N. (2021). Useful chatbot experience 
provides technological satisfaction: An emerging 
market perspective. South African Journal of  Information 
Management, 23(1), 1-8. 

Majumder, S., & Mondal, A. (2021). Are chatbots really 
useful for human resource management? International 
Journal of  Speech Technology, 1-9. 

Maroengsit, W., Piyakulpinyo, T., Phonyiam, K., 
Pongnumkul, S., Chaovalit, P., & Theeramunkong, T. 
(2019). A survey on evaluation methods for chatbots. 
Proceedings of  the 2019 7th International conference on 
information and education technology, 

Meyer-Waarden, L., Pavone, G., Poocharoentou, T., 
Prayatsup, P., Ratinaud, M., Tison, A., & Torné, S. 
(2020). How service quality influences customer 
acceptance and usage of  chatbots? SMR-Journal of  
Service Management Research, 4(1), 35-51. 

Nguyen, D. M., Chiu, Y.-T. H., & Le, H. D. (2021). 
Determinants of  continuance intention towards 
banks’ chatbot services in Vietnam: A necessity for 
sustainable development. Sustainability, 13(14), 7625. 

Nirala, K. K., Singh, N. K., & Purani, V. S. (2022). A survey 
on providing customer and public administration 
based services using AI: chatbot. Multimedia Tools and 
Applications, 81(16), 22215-22246. 

Selamat, M. A., & Windasari, N. A. (2021). Chatbot for 
SMEs: Integrating customer and business owner 
perspectives. Technology in Society, 66, 101685. 

Su, T., Li, H., & An, Y. (2021). A BIM and machine 



Pa
ge

 
54

https://journals.e-palli.com/home/index.php/ajsts

Am. J. Smart. Technol. Solutions 2(2) 43-54, 2023

learning integration framework for automated 
property valuation. Journal of  Building Engineering, 44, 
102636. 

Tawafak, R. M., Al-Rahmi, W. M., Almogren, A. S., Al 
Adwan, M. N., Safori, A., Attar, R. W., & Habes, M. 
(2023). Analysis of  E-Learning System Use Using 
Combined TAM and ECT Factors. Sustainability, 
15(14), 11100. 

Um, T., Kim, T., & Chung, N. (2020). How does an 
intelligence chatbot affect customers compared with 
self-service technology for sustainable services? 
Sustainability, 12(12), 5119. 

Yun, J., & Park, J. (2022). The effects of  chatbot 
service recovery with emotion words on customer 
satisfaction, repurchase intention, and positive word-
of-mouth. Frontiers in psychology, 13, 922503. 


