PV panel manufacturers and testing laboratories can confirm reliable and consistent measurements of module performance using STC, which allows for fair evaluations between different solar modules or systems, providing a common reference point for assessing and evaluating their electrical production.

**Fig. 2.** Guaranteed Power Performance by manufacturer *(Source:* www.jinkosolar.com*)*

Table 2 illustrates how well the specified solar panel system performed in generating energy over the year (E\_Gird) and reflects the performance ratio (PR), which is how efficiently the system converted the solar irradiation into energy in the grid. These PR values range from **77.5%** to **83.9%.** This indicates that the system reached the cutoff in 22 years which is earlier than anticipated. The manufacturers guarantee a power performance of up to **80.2%** until reaching **25** years (Fig. 2). Hence, the researcher can conclude that the panels reached the time for decommissioning and shall be removed from the power system. Furthermore, it is noticeable that the performance ratio was higher in winter rather than the summer, with less shade and more sunlight; this may be credited to the high temperatures observed and experienced during those months, which have an opposing impact on the efficiency of the solar cells. A drop below **78%** in June, July, & August, indicating that there might be issues with system components overheating or it could be due to extreme weather conditions such as a rise in temperature or sand storms and dust (Ahmad and Ahmad 2019, Agyekum et al. 2022, Conde at el. 2023). According to (Satya et al. 2021), there is a direct relationship between temperature and solar PV module cell efficiency, where the PV cell's performance tends to decrease when temperature rise (Conde et al.2023). This happens because of disrupted internal transmission of PV cells leading to problems with the energy movement within the panel which make it work and function less efficiently. Hence, the temperature is a crucial factor in the power-generating process affecting the overall performance. Figure 3 is an illustration of the PR of the given PV system, which is equal to = 80% in a Uniform shape.

**Table 2.** Energy and Performance Ratio (UAE-Dubai)

**Fig. 3.** Months Vs Performance Ratio PR (Dubai-UAE)

As shown, Table 3 represents the normalised performance coefficients which are equal to the daily energy of the system (Yf) over the daily output produced energy of the array plane (Yr) = **4.81/6.01 = 80%.**

**Table 3.** Normalized Performance Coefficients (Dubai-UAE)

#### **4.2 Performance Analysis and Energy Losses in (Dubai-UAE) PV System Simulation**

The loss diagram shown in Fig. 4 is a key output of all simulations performed by PVsyst software. The figure provides a thorough overview of the energy flow during which the energy is produced and lost at different stages until it is connected to the grid (Ahmad and Ahmad 2019 and Agyekum et al. 2022). The energy flow starts at the stage of global horizontal irradiation; in this stage, it is equal to = 2042 kWh/m2 for the region of Dubai (UAE). At the beginning of the energy flow, there is an increase in the energy equal to (+7.4%), which can be caused by the PV array being titled southward and thus collecting a more significant amount of energy than being horizontal. An important point to make regarding panel orientation is that according to (Tsoukp et al. 2022), the best and most efficient angle to position a solar panel is to rotate it in accordance with the latitude angle of the specific site location to collect the most sunlight and produce the most energy throughout the entire year. For locations with north latitude, the solar panel plate shall be facing south, while for locations with south latitude, it shall be facing south, toward and away from the equator, respectively.

After this rise, the (−1.8%) arrow shows a loss concerning the energy that remained present and active at the previous stage (Agyekum et al. 2022). Following that, the effective irradiation on the collector is determined and calculated by multiplying the final irradiation on the collector by the total collector area = 2145 kWh/m2\* 18 m2 coll. This tells the total luminous energy available on the collector. Multiplying this value by the nominal efficiency of the PV module at STC = 20.06% to attain the nominal PV electrical energy = 7759.37 kWh, rounding it up to the nearest whole number will give 7960 kWh. This value will be developed every time the PV modules function at their nominal efficiency.

The following losses (−0.4% & −12.0%) are due to the fact that PV modules don't operate under STC at their maximum efficiency. In sunny weather conditions, the PV loss due to irradiance level may be lower, whereas, in very hot environments with poor airflow (like Dubai-UAE), the PV loss due to high temperature might be increased. Subsequently, there are losses on the inverter level ( a device that converts DC to AC to be injected in the grid), as shown in the diagram; the inverter has operational losses, which are indicated as efficiency loss = − 49%. Other losses are null. Since there are no further losses considered, the output of the inverter is equal to the generated power, which is shown at the bottom of the diagram. The output energy is equivalent to = 6476 kWh, which is the electrical energy injected into the grid.

**Fig. 4.** Loss diagram (Dubai-UAE)

#### **4.3 Energy Production and System Output Assessment (Dubai-UAE)**

The above two figures are significant in assessing the performance of the PV module. Figure 5 indicates the normalised energy production and losses generated annually in the PV system and helps evaluate the system's efficiency. Normalised power, collection loss (PV-array losses), and System loss values = are equal to 4.81, 0.95, and 0.25 kWh/kWp/day, respectively. Figure 6 expresses the amount of sunlight reaching the collector, which is relatively stable over the year. Analysing and assessing the reference incident energy in the collector plane support the site engineers and operators in

**Fig. 5.** Months Vs Normalized Energy (Dubai-UAE)

**Fig. 6.** Months Vs Reference Incident Energy (Dubai-UAE)

optimising the PV system position and configuration and then maximising the system performance (Agyekum et al. 2022 Tsoukpoe, 2022).

The above two figures discuss the energy generation and injection into the grid by the PV system. The daily system produced energy injected into the grid from the PV system is displayed in Fig. 7. It is noticed that values are less fluctuating between April-August and virtually inconsistent in winter ( December- March), which proves that during summer, the grid is getting the greatest energy injection due to the extended time & greater intensity of solar radiation during summertime (Agyekum et al. 2022). However, there is a significant drop in March that requires further investigation. It might be due to inaccurate weather data values that affected the simulation or due to the overload on the inverter during this time (Agyekum et al. 2022). Figure 8 draw the distribution of the produced power injected into the grid concerning the energy injected into the grid over the year. It is negatively skewed to the left, which means most of the power output takes values between 1500–2500 (W). Figures highlight the seasonal variations and issues the grid exposed, and analysing them asses in improving system performance.

**Fig. 7.** Months Vs Energy injected into grid (Dubai-UAE)

**Fig. 8.** Power injected into grid Vs Energy injected into grid (Dubai-UAE)

**Fig. 9.** Effective Global corr. For IAM and shadings vs Average module temperature during running (Dubai-UAE)

Figure 9 shows how the array's temperature is changing with varying effective irradiance. The temperature of the collection fluctuated from as low as 11 °C throughout the winter to as high as near 79 °C in the summer, which is too far from STC values (25 °C at 1000 W/m2).

#### **4.4 Technical Evaluation Flowcharts:**

Based on the PVsyst results, the researcher propose utilizing Technical Evaluation Flowcharts as valuable tools for confirming that all important factors related to the panel performance is considered and evaluated in the decision-making process. Figures 10 and 11 illustrate a suggested decision-making process for PV panel waste, based on the obtained simulation results of panel efficiency and performance. If panel still functioning efficiently as guaranteed by the manufacturer, asset owner to decide if it shall stay in service, reused, resold, or repaired. Otherwise, if it reached the cut-off limit they may be decommissioned and recycled as per city regulations. This ensures that the best decision is selected based on complete evaluation of all key factors (Figs. 10 and 11).

**Fig. 10.** Proposed process flowchart for PV performance Evaluation at EOL

**Fig. 11.** Proposed process flowchart for PV Decision-Making at EOL

# **5 Conclusion**

This study examined the energy production efficiency, system performance, and losses of PV systems in a Solar Park in UAE. The researcher presented through the discussion the most common and effective methodologies used for evaluating PV panel performance to support decisions related to panels reaching the end-of-life (EOL). However, due to limitations, PVsyst software & technical flowcharts were utilised. The findings and analysis allowed for broader assumptions about the system conditions and variations observed and indicated by the researcher. The research objectives were achieved successfully as the critical parameters of the PV module were identified and utilised using the simulation software and were supported by the flowcharts Fig. 34 & Figure-35 for evaluating the performance and reinforcing the final decision.

In summary, the operating temperature plays a vital role in the energy production process using photovoltaic solar panels. High temperatures do affect the efficiency and physical conditions of PV panels. High-temperature weather leads to an increase in the cell's heat that ruin or burns the cell's components, reducing its expected lifetime and lowering its performance while operating. This assumption is proved by the result of the UAE solar park simulation that indicated the system reached the cut-off in 22 years with a performance of 80.0%, which is earlier than anticipated. The manufacturers guarantee a power performance of up to 80.2% until reaching 25 years. Accordingly, it is preferred that Solar panels that are less sensitive to temperature are installed in areas with hot climates, as considered in UAE. These factors are to be considered when deciding where to install the solar panels due to their impact on the efficiency and healthiness of the panels.

To summarise the findings:

• Efficiency of solar panels operating in hot environments is lower due to the heat effect on destroying or burning the internal PV cells components, making it function less efficiently.

After evaluating the PV system's efficiency in generating energy and power performance, and using the manufacturer's performance warranty as the benchmark, 80.2% PR after 25 years, for condition monitoring and evaluation, it is suggested to use the provided technical flowcharts (Fig. 34 & 35) to make an adequately evaluated decision for UAE solar park PV system, which might recommend decommissioning the panels since their validity or usefulness reached its end earlier than anticipated. These two tools are significant to support the site engineers, system operators, and manufacturers in optimising the PV system's healthiness and configuration to maximise the system performance and extend the lifespan, to delay reaching the End-of-life stage.

The following recommendations are proposed to be incorporated into future studies:

To assess the health of PV assets and determine the optimal point at which maintenance is no longer cost-effective, future researchers to consider specific criteria like tracking the decrease in performance ratio (PR) and decrease in energy yield in accordance with the cost assessment of operation and maintenance (O&M). Hence, making better decisions about when it is economically reasonable to decommission the assets by evaluating these criteria over time.

• Future research can include the assessment of carbon footprint and CO2 emissions reduction of the in-service PV panels.

# **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **The Role of UK Universities' Reputation on Economic Recovery in the Crisis Time**

Homa Molavi(B)

Department of Engineering Management, School of Engineering, The University of Manchester, Manchester, England, UK<EMAIL_ADDRESS>
**Abstract.** This article discusses the role of university reputation in economic recovery, particularly in crisis situations. It highlights the importance of maintaining a good reputation for universities. Especially after Brexit, and during the covid pandemic, the burden of compensating the detrimental effects of existing from European Union has been on HE industries' shoulder. However, the role of HE industry in helping UK to bolster up the economy has been ignored. However, 'Regional Disparity' could limit the impact of high-reputed universities.

**Keywords:** UK Higher Education · higher education reputation · university reputation · economic recovery · crisis response

# **1 Introduction**

During 2020, the total gross domestic product (GDP) for the entire globe decreased by 3.4% (Statista, 2023). According to a report by the UK parliament, the COVID-19 pandemic has caused a severe economic downturn in the country, with GDP dropping by 11% in 2020 during the first national lockdown, the largest decline since records began in 1709 (Philip Brien et al. 2022). This unprecedented situation has presented challenges for many businesses (Kapucu and Hu, 2022; Rodrigues et al. 2021; Siddique et al. 2021; Yang, 2020).

Biggest Exporting Industries in the UK in 2023, are respectively, Precious Metals Production in the UK (\$61.9B), Aircraft, Engine & Parts Manufacturing in the UK (\$23.4B), Motor Vehicle Manufacturing in the UK (\$18.8B), Pharmaceutical Preparations Manufacturing in the UK (\$16.5B), while according to UK government website, in 2010, the revenue from education-related exports and transnational education (TNE) activity was 9.53 billion pounds, followed by an increasing trend reached to 25.6 billion pounds in 2020 (The most recent statistics published by UK government, which cover the time period until 2020). So, UK politicians are totally aware of the significant role of HE industry in economic like providing cheap labour, high quality experts, solving issues regarding Brexit, etc. make them to support internalisation.

In HE industry, universities need to fulfill the roles of teaching and research, knowledge transfer, employability, and community outreach but also, social and economic mission (Schlesinger et al. 2015). Moreover, satisfying stakeholders' expectations (Agrey and Lampadan, 2014; Angulo-Ruiz et al. 2016; Broekemier and Seshadri, 2000; El Nemar et al. 2020; Germeijs, 2012) is another requirement for universities. Furthermore, these expectations have been risen by globalization, advent of technologies (Nielsen and Thomsen, 2018), degree of internationalization, and changes in the market (Del-Castillo-Feito et al. 2019).

However, the HE industry in the UK has performed differently, making a significant economic contribution. In 2018–19, the HE industry in England contributed £95 billion to the economy and grew by around a quarter between 2014–15 and 2018–19, supporting over 815,000 jobs across the country (Economics, 2021). According to data from the National Centre for Entrepreneurship in Education, partnerships with universities in the UK can play a significant role in accelerating the country's post- pandemic recovery (West, 2021) by creating jobs and boosting economic prosperity. For instance, the University of Oxford (The University of Oxford, 2021), University of Cambridge, and University of Manchester contribute significantly to the UK economy and support thousands of jobs (28,790, 24,700 and 13,970 jobs subsequently) (University and College Union, 2020).

Based on a report by the UK parliament (UK Parliament, Hansard, 2022), universities in the country are significant contributors to the economy, as they attract a large number of international students and contribute £25.9 billion to education export earnings. In addition, the Department for Education and Department for International Trade have launched a new International Education strategy to increase education exports to £35 billion per year and raise the total number of international students to 600,000 by 2030 (Government, 2019; Hubble and Bolton, 2021; O'Malley, 2019). However, UK higher education institutions (HEIs) are facing crises like the COVID-19 pandemic, which are leading to disengagement (Burkinshaw et al. 2018; Burns et al. 2020; Hazelkorn, 2020) and de-globalization (Allen et al. 2020; Mok et al. 2021; Pan, 2021) through restricting international mobility (Bilecen, 2020; Guruz, 2011; Marginson, 2017; Zotti, 2021) leading in several challenges like funding problems (Corbett and Gordon, 2018; Marginson, 2017; McKinny, 2020), fall in student numbers from the EU (Mathies and Weimer, 2018; Mayhew, 2022; Thompson and Johnson, 2023; Tsiligiris and de Ruyter, 2018), the shaky future of the Erasmus scheme (Brøgger and Moscovitz, 2022; Brooks and Waters, 2023; Kleibert, 2023), etc.

According to oxford dictionary, reputation is the *opinion* that people have about what somebody/something is like, based on what has happened in the past. Reputation is derived from one's *expectations* and *beliefs* about how they have been treated by other entities in the past (Mui et al. 2002). It seems that definition of reputation by (Fombrun, 1996) as a perceptive representation of an organization's past behaviours as well as an estimate of its future behaviours, or as the universal appeal that an organisation has for its key constituents in comparison to others, is most frequently used today.

The gap of neglecting the role of university reputation in the context of economic recovery in a crisis situation is significant since, as numerous scholars studied the impact of crisis and economic or financial matters on performance or efficiency of universities. Therefore, the results of previous research like studying the effects of economic crisis on universities (Harvey Chaputula, 2011; Lee, 1999; Okpala et al. 2011), the economic crisis effect (the financial crisis in 2008) on Italian and German university efficiency (Lehmann et al. 2018), the economic crisis effect (the 2008 economic crisis) on the (in)efficiency of Spanish public Higher Education institutions (Martínez-Campillo and Fernández-Santos, 2020), US seven economic crises effect (1953, 1957, 1990, 1973, 1980, 2001, 2008) on university research and development investments (Daim and Ozdemir, 2015), east Asian economic crisis (1997) on Indonesian HE (Purwadi, 2001) showed the significant negative role of crisis on the efficiency of universities.

However, the statistics mentioned earlier revealed despite these obstacles, not only UK universities have passed challenges (Hilman, 2021) but also, the UK has achieved its international student target a decade early, allowing the government to establish a more discerning and smart approach (Holmes and Partners LTD Immigration consultants, 2022). The focus now is on how HE industry could boost economy and help to recover it while facing several crises. Whether British universities' reputation plays a role in economic recovery which in the following, by using traditional literature review will be discussed. Therefore, the research question is:

What is the role of UK universities' reputation in the economic recovery during times of crisis?

# **2 Method**

The study used a traditional literature review design to identify, screen, and eligibility keywords related to British universities and their reputation. It found no relevant resources for the keyword 'university reputation'. Secondary data analysis revealed a gap in understanding the relationship between university reputation and economic recovery.

# **3 Theoretical Underpinning**

#### **3.1 UK's Higher Education Reputation and Crisis Response**

The UK's higher education sector is globally renowned for its top-tier research and top-ranking in field-weighted citation impact. With numerous universities in the top 10, the UK attracts international students and staff, producing globally recognized, worldleading research and highly cited articles. Despite contributing to over 80% of revenue, reputation is often overlooked (Moberly, 2014). Organizations face reputational threats in crises, with the public often holding institutions responsible for certain issues (Kumar et al. 2020). In the HE industry, the pandemic has affected on HE systems as well (Huang et al. 2022), which has led the World Bank, UNESCO, and UNICEF to dub it the worst education crisis on record. Activities connected to the basic missions of HE have also been disturbed and rearranged by the pandemic.

#### **3.2 Economic Recovery**

Disaster resilience and recovery are complex concepts involving various disciplines. Recovery indicators include social, economic, environmental, and infrastructure. Economic recovery refers to the process of resuming stable conditions after a crisis (Jordan and Javernick-Will, 2012). Economic recovery pertains to the process by which businesses and local economies resume stable conditions following a crisis. Economic recovery and economic impact are distinct terms as they refer to different aspects of disasters. Economic impact focuses on the consequences of a disaster, usually measured in monetary terms, while economic recovery encompasses the process of overcoming those consequences, often measured in time (Chang and Rose, 2012). To test whether something affects on the economic recovery, first we should know the signs or indicators of recovery. Jordan and Javernick-Will, (2012) in their article titled measuring community resilience and recovery through a content analysis of indicators, classified the indicators of community resilience into infrastructure, social, economic, and institutional categories. They mentioned that across all article types, there is an interest in economic recovery with various indicators to measure economic recovery. These indicators include Employment rates, income levels, Gross national income (GNP), Government Revenue, number of businesses, Housing Values, funds distributed through loans, enterprises aided or founded and training programs (Jordan and Javernick-Will, 2012).

# **4 Results**

Comparing to other organisations, HE relies heavily on reputation which could strengthen its strategic plans for educational, economic, and other development. Also, maintaining the UK's reputation for excellence in education could help UK to defeat crises, promote economic growth, cultural exchange, and innovation, cementing UK status as a global leader in this sector (Molavi, 2023). Regarding the role of universities in the economy, natural resources and labor-intensive industries which were once the main sources of wealth creation and economic growth have been repleced by universities as knowledge factories (Drucker, 1993; Florida, 1995; Leonard, 1995; Nonaka and Takeuchi, 1995; Romer, 1993). Thus, universities, by generating knowledge and ideas, have become a crucial factor in gaining economic advantages (Edvinsson and Malone, 1997; Stewart, 2010).

The UK government is utilizing the reputation of its universities to promote economic growth in a planned manner. As a result, universities and colleges are vital to the UK's economy. Therefore, university reputation has an impact on the economic recovery in either the normal or crisis situation. Finally, this article suggests that there is the relationship between university pre-reputation and economic recovery while being mediated by marketisation, education export and better performance in crisis (see Fig. 1).

**Fig. 1.** Research outcome (author)

# **5 Conclusions**

Successful crisis response for countries dealing with crises like the pandemic involves investing in building reputation recognition before the crisis hits. Being proactive and anticipating potential crises and their impact on the organization, particularly on its reputation, is crucial for surviving such situations. This research suggests that the reputation of UK higher education institutions has helped in managing crises. The UK's higher education reputation has been instrumental in retaining its competitiveness and attracting international students. Additionally, the reputation of the UK higher education system has played a role in mitigating the economic impact of crises, especially the pandemic.

While university reputation can play a role in economic recovery (Molavi, 2023), 'Regional Disparity' could be a potential limitation associated with high-reputed universities' impact. The disparity refers to resource inequalities between the various regions, with some being characterised by sparsity or prosperity (Rice and Venables, 2003; Öberg and Aronsson, 2022). Universities with strong reputations are often concentrated in specific regions or countries. This concentration can exacerbate regional disparities in economic recovery, as areas without renowned universities may struggle to attract investment or develop skilled workforces. Consequently, the university reputation may not uniformly benefit all regions, potentially leaving some areas lagging behind in the recovery process. Also, local businesses may be received insufficient support. Universities with strong reputations often have close ties to industries and businesses, attracting research partnerships, talent recruitment, and entrepreneurial activities. However, this focus on established industries and companies can overshadow support for local startups, small businesses, and emerging sectors. Neglecting the development and nurturing of local businesses can hinder economic diversification and limit job creation opportunities in innovative and entrepreneurial endeavours.

To address such regional disparities, policymakers may need to consider strategies like investing in satellite campuses, establishing research partnerships with neighbouring universities, or promoting online education and remote learning opportunities. These measures can help bridge the gap and provide access to educational resources and expertise, enabling the region to better participate in economic recovery efforts.

Recommendations have been made for the UK government and higher education institutions to prioritize investment in reputation management for the sustainable growth of the country's higher education sector. The reputation of UK higher education has become essential in the current global crisis as it can attract foreign students, investors, and partners while increasing visibility for the institutions and the country as a whole. Making brand recognition is a crucial factor for universities to attract mega projects to boost economic growth or recovery.

# **References**

Drucker, P.: Post Capitalist Society. Butter worth Heinemann, New York (1993)

Economics, F.: The economic contribution of the higher education sector in England: summary report prepared for Universities UK, 27 September. London. Frontier Economics. In (2021)

Edvinsson, L., Malone, M.S.: Intellectual capital. HarperBusiness (1997)

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **A Systematic Review of Conversational AI Chatbots in Academic Advising**

Suha Khalil Assayed(B) , Manar Alkhatib, and Khaled Shaalan

Faculty of Engineering and Information Technology, The British University in Dubai, Dubai, UAE

<EMAIL_ADDRESS>
**Abstract. Purpose** – This paper aims to review several studies published between 2018 to 2022 about advising chatbots in schools and universities as well as evaluating the state-of-the-art machine learning models that are deployed into these models.

**Methodology** – This paper follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), it demonstrated the main phases of the systematic review, it starts with screening 128 articles and then including 11 articles for systematic review which focused on the current services of the advising chatbots in schools and universities, as well the artificial models that are embedded into the chatbots.

**Findings –** Two main dimensions with other sub-dimensions are extracted from the 11 included studies as it shows the following: 1- Advising chatbots AI Architecture which includes other sub-dimensions on identifying the deep learning based chatbots, hybrid chatbots and other open-resources for customizing chatbots; 2- The goals of the advising chatbot as it includes both the admission advising and academic advising.

**Conclusion** – Most of studies shows that advising chatbots are developed for admission and academic advising. Few researchers who study the chatbots in high schools, there is a lack of studies in developing chatbots for students advising in high schools.

**Limitations and future work –** This study is constrained to review the studies from 2018–2022, and it is not exposed to the chatbots artifacts, even though, the human-chatbot interaction has an essential impact on students' experiences. Future research should include the impact of chatbots interactive design and students' experiences.

**Keywords:** Artificial Intelligence · Chatbot · Conversational AI · Machine Learning · Student · Systematic Literature Review

# **1 Introduction**

Artificial Intelligence plays an essential role in developing a chatbot that can learn how to understand human and making a decision. The most important components of the artificial intelligence that can make a successful chatbot are the natural language processing (NLP) and machine Learning (ML) (Khan & Rabbani, 2021; Bird et al., 2021). In general, the chatbot works by taking the natural language instructions as input and the output is expected to be very relevant to the user's input (Caldarini et al., 2022). Interestingly this is explained the reason behind that machine learning is becoming increasingly ubiquitous across different industries including education, finance, healthcare, etc. Hence, the researchers and developers around the world are inspired to develop the state of-theart chatbots by embedding different machine learning algorithms such as naïve Bayes algorithm and support vector machine (SVM) (Bird et al., 2021; Tamizharasi et al., 2020; Kushwaha & Kar, 2020; Assayed et al. 2022) and other deep learning algorithms mainly the convolutional neural network (CNN), long short term memory network (LSTM) and recurrent neural network (RNN) (Dhyani et al., 2021; Sperlí, 2021; Prasetyo & Santoso, 2021; Kliestik et al., 2022; Assayed et al., 2023). Accordingly, several authors conducted different reviews about state-of the-art chatbots in education and learning. Ji et al. (2023) conducted a review about learning classes and subjects in schools without comparing the state-of-the-art chatbots technology and applications. On the other hand, Nee et al. (2023) conducted another systematic review to define the potential contribution between educational tools in elementary, middle, high school and universities, with no details about the models or algorithms that are deployed in these chatbots. Furthermore, Assiri et al. (2020) proposed a review of academic advising computer systems in undergraduate education without focusing on chatbots. Despite the importance of readers and developers to evaluate the architecture as well as the performance of advising chatbots but few authors who defined the novelty in chatbots applications. For instance, developers need to know the number layers that used in deep learning model, the type of machine learning that deployed into the system, and etc. Therefore, this study aims to close this gap by conducting a systematic literature review (SLR) to propose the students advising chatbots for particularly students in high schools or prospective students in higher education. Accordingly, this paper reviewed several studies by considering chatbots for students advising and evaluating the AI and machine learning models that are deployed into these agents. Three research questions will be formulated in this paper:

RQ1: What are the current AI models and algorithms that deployed into the Advising chatbots?

RQ2: What are the current Advising chatbots services at universities?

RQ3: How do AI conversational advisers support high school students?

The SLR in this paper will provide numerous state-of-the-art academic advising chatbots' architectures as well as exposing the developers and researchers to the main goals and services that can be used to enhance the students' services into different educational institutions. This study organized as follows: Sect. 2 explains the methodology and the approach that adopted in this paper, while Sect. 3 presents the discussions and results, finally the conclusion and limitation are both presented in Sect. 4.

# **2 Methodology**

# **2.1 Approach**

This paper follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), it demonstrated the main phases of the systematic review, it identifies the number of study articles that are explored, identifying the excluded and included articles with stating the reasons. The main advantage of using PRIMSA is to improve the transparency for this review and providing a clear analysis for all the eligible study articles (Selçuk, 2019). Figure 1 illustrates the entire approach and it shows more details in each single stage.

### **2.2 Resources**

The below Table 1 and Table 2 show the databases & journals that were used in exploring the articles and other references.

### **2.3 Search Criteria**

The search strategy conducted by using the resources that are mentioned in previous Sect. 2.2. The search implemented by using Boolean operators (AND, OR). The criteria include three categories and it's defined in three statements as explained in Table 3. However, in this study the authors adopted the search criteria to retrieve publications between 2018–2022, in order to study the state-of-the-art advising chatbots.

**Table 3.** The search categories and criterias.

**Fig. 1.** The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). It demonstrates the main phases of the systematic review.

# **2.4 Identification**

The articles are collected based on the defined criteria in Sect. 2.3 and the number of articles that are retrieved from the databases that mentioned in Sect. 2.2 are (N = 162).

# **2.5 Screening and Eligibility**

The number of records are limited to the five years (2018–2022) with total number (n = 128), afterward we select only the full-text documents, with total number is (N = 19).

#### **2.6 Excluded Strategy**

Some documents are excluded from the screening list, the article from Suárez et al. (2022) is excluded from the list, the title met all the predefined search criteria the title: "Using a Virtual Patient via an Artificial Intelligence chatbot to Develop Dental Students' Diagnostic Skills", the context is about the dental's students satisfactions with AI chatbot that enhance interacting with virtual patients.

# **2.7 Included Strategy**

The number of studies that are considered in the literature review are (N = 11), these articles are all about using the chatbot or conversational AI in assisting and supporting students at universities, students are the targeted audience in all studies. Table 4 shows these studies.

# **2.8 Literature Taxonomy**

According to Randolph (2009) the effective literature review should begin with matching the proposed thesis's review with cooper's taxonomy of literature reviews. Cooper's (1985) proposed to have a taxonomy for the scope of literature review which includes the following: The focus of the research, the goal, the perspective of the reviewers, coverage, organization and the audience. According to our selected (11) studies, we can construct the taxonomy of the literature review from the authors perspectives toward the main topics of their articles. The taxonomy in this literature is constructed into two main categories, the first one is academic chatbot's AI architecture, the second category is the goals of academic chatbots and the. Each particular category explored the contributions of students advising and counseling. However, the majority of the studies are focused on the deep learning algorithms and basically in seq2seq architecture such as BRNN, BLSTM, and attention models in benefiting particularly the admission department at universities. Figure 2 illustrates the taxonomy of the selected articles.

# **3 Results and Discussion**

This reviews investigates the state-of the-art advising chatbots from 2018 to 2022 and two main dimensions with other sub-dimensions are extracted from the 11 included studies as it shows the following: 1- Advising chatbots AI Architecture which includes other sub-dimensions on identifying the deep learning based chatbots, hybrid chatbots and other open-resources for customizing chatbots; 2- The goals of the advising chatbot as it includes both the admission advising and academic advising.

The below findings are answered to the research questions that are highlighted in the introduction section.

(*continued*)

#### **Table 4.** (*continued*)

#### **3.1 Advising Chatbots AI Architecture**

This section answers the below research question:

RQ1: What are the current AI models and algorithms that deployed into the Advising chatbot?

Haristiani (2019) views the chatbot as one the advanced technologies that can be applied in education, inspiring researchers to explore different aspects of chatbots. The performance and the efficiencies of the AI chabots depend basically on the state-of-theart algorithms that are selected to use it (Santana et al., 2021). There are multiple types

**Fig. 2.** The Taxonomy of the Selected Articles

of machine learning that are used in the models, However, Fig. 3 depicts the revealed studies based on AI architecture.

**Fig. 3.** The AI based chatbots architecture

#### **3.1.1 Deep Learning**

Khan et al. (2021) proposed the random forest algorithm, decision tree and support vector machine for supporting students and parents who visit the campus. The authors started with preprocessing the dataset by using the features engineering such as TFIDF with adding the Ngram feature and the results shows that Decision Tree algorithms and Support Vector Machine are not responding well like Random Forest algorithm, thus, the RF model is adopted in the system as an effective approach for assisting students in universities. In fact, the result of any model depends on the validation that are accepted Recently many developers are inspired in using the seq-2seq model by adding more layers such as attention layers and LSTM/ BLSTM. Chandra & Suyanto (2019) developed a chatbot in one of the Indonesian universities by using a small dataset which collected from the admission office. The system developed by using seq2seq model, since the data is basically a conversation between the admission officers and the students, however, the authors added BLSTM layers as well as attention layer in order to improve the accuracy of the chatbot. Furthermore, another deep learning chatbot is implemented as a proof of Concept in Ho Chi Minh City University of Technology (HC-MUT) by combining the Embedding layer and the Bidirectional-LSTM with the Attention mechanism for solving the classification tasks from students' majors-degree requests (Le-Tien et al., 2022).

#### **3.1.2 Hybrid Learning**

There are several authors conducted studies in the hybrid learning, which can vary in the complexities of the model, some models might have a combination between the supervised machine learning and deep learning, and others might have a combination between different deep learning models together. For example, Yu et al. (2020) developed a hybrid model for wearable inertial sensor-based systems in order to prevent older people falls that can cause them injuries, this model called ConvLSTM which outperformed the both CNN and LSTM individually. However, In reference to the literature in this study, a few studies focused on the hybrid learning, Chandra & Suyanto (2019) developed a chatbot as customer service in the Telekom University in Indonesia for assisting students in the admission processes, the authors adopted the Seq2Seq approach with combining both the BI-LSTM and Attention models together.

#### **3.1.3 Open-Source Tools for Customizing AI Chatbots**

Some authors design the chatbots by using AI packages such as Rasa Natural Language Understanding (NLU) which is an open source framework for NLP solution that take user's input and try to extract the intent, Meshram et al. (2021) adopted this configuration in the proposed chatbot, after the data is trained, they fed it into the NLU model pipeline, however they used the Anaconda platform and python language in order to deploy the required packages.

#### **3.2 The Goals of the Advising Chatbots.**

In this section the below research questions are answered:

RQ2: What are the current Advising chatbots services at universities? RQ3: How do AI conversational advisers support high school students?

Several researchers studied the dialogue systems and advising chatbots in schools and universities. However, few of them who investigated the advising chatbots in high schools. Assayed et al. [23] developed a machine learning chatbot called "HSchatbot" to assist students in some of high schools to classify their enquiries based on type of their questions, nevertheless, most of the authors focused on studying the impacts of chatbots on students' admission at universities. Since most universities and colleges around the world receive thousands of applications yearly during a limited time and as a result the admission officers would receive many calls from students and parents (Fig. 4).

**Fig. 4.** The distribution of academic chatbots based on goals

### **3.2.1 Students Admission Advising**

El Hefny et al. (2021) developed a chatbot called "Jooka" for supporting prospected students who are targeting the German University in Cairo (GUC), which can answer to their admission enquiries. Jooka enhances the advising process by providing instant responses to the both students and parents in both languages (English and Arabic). Moreover, Khan et al. (2021) proposed a model for supporting students and parents to provider in 24/7 services by answering any question that are related to registration and admission. Another chatbot called "CollegeBot" that developed by Daswani et al. (2020) to assist the university's website's visitors to navigate the website effectively, however, the authors in this study present a proof-of–concept model for the CollegeBot to the San Jose State University. Moreover, Meshram et al. (2021) developed a chatbot called "college enquiry chatbot" that aims to answer any admission-related questions.

#### **3.2.2 Academic Advising**

Students-faculty advising play avital role on students' success (Sneyers, & De Witte, 2018), advisers can assist the students in making the right decision in selecting the courses as well as monitoring the progress of students in order to be sure to graduate on-time. Bilquise et al. (2022) developed a bilingual chatbot for supporting the current students academically in order to be sure that are following the academic plan as well as to graduate on-time and it supports both languages the Arabic and English. Another novel chatbot is developed by Le-Tien et al. (2022) for advising the current students in Vietnamese universities to select the appropriate courses that match with their academic plan and accordingly students would be able to declare their major successfully.

# **4 Conclusion**

This review investigates the current state-of-the-art advising chatbots in schools and universities. Most of studies shows that advising chatbots are developed for admission and universities academic advising. Despite the vital role of academic advisers in high schools where students' future careers are shaped by choosing the best fit majors and universities, few studies conducted on chatbots that focused on this stage, resulting in the gap in the development chatbots for student advising in high schools.

# **4.1 Limitations and Future Work**

This study is constrained to review the studies from 2018–2022 utilizing specific accessible databases, moreover, the taxonomy used in this SLR does not cover the chatbots artifacts, even though, the human-chatbot interaction have an essential impact on students' experience. Future research should include the impact of chatbots design and students' experiences while also exposing to other familiar databases such as Web of Science and Scopus.

# **References**

Cooper, H.M.: A Taxonomy of Literature Reviews (1985)

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Chatbots: Can They Satisfy Customers in the Banking Sector?**

Omer Aftab Qureshi1(B) , Stephen Wilkins2, and Huda Iqbal3

1 The British University in Dubai, Dubai, United Arab Emirates<EMAIL_ADDRESS>2 Graduate Programmes in Business Management, The British University in Dubai, Dubai, United Arab Emirates<EMAIL_ADDRESS>3 Abu Dhabi University, Al Ain, United Arab Emirates<EMAIL_ADDRESS>
**Abstract. Purpose** – The purpose of this research was to investigate and explore the influence of chatbots service quality on customer satisfaction in the banking sector.

**Methodology** – The sample size for this research was determined to be 25, sufficient to achieve data saturation. This research utilised qualitative inductive research approach, exploratory in nature, implementing non-probability, stratified purposeful sampling technique, based on the age category of commercial bank consumers.

**Findings** – Overall, a total of eleven themes and sub-themes emerged from the gathered data, resulting in three aggregate dimensions, which were "perceived chatbot service quality", "customer satisfaction" and "responses and intentions". **Practical implications** – The study could be extended for different target market, creative strategy, other media and more countries. The findings of this research aimed to help banks further understand customer satisfaction, along with their responses and intentions, when it comes to interacting with chatbots.

**Originality/value** – This research added value to customer experience, which mattered the most, when it comes to them being satisfied with their interaction with chatbot. The output of this research developed a conceptual framework which identified the factors causing customer dissatisfaction once they interacted with chatbots.

**Keywords:** Chatbots · Artificial Intelligence · Customer Satisfaction · Intentions · Word of Mouth (WOM) · Technology Acceptance Model 2 (TAM 2)

# **1 Introduction**

Technology has overtaken the world through artificial intelligence (AI) in this digital era (Letheren, Russell-Bennett & Whittaker 2020). With rigorous developments in AI, chatbots have taken the centre stage transforming the customer service profession, and its interaction with the users (Wirtz et al. 2018). Currently, chatbots are utilised to provide 24/7 support service specifically in the fields of marketing, sales and support, where on average it has helped increase sales by 67% (Press 2019; Ashfaq et al. 2020). Przegalinska et al. (2019) discussed that chatbots are the form of AI technology, that interact and engage with humans verbally, or through texts. Interestingly, many firms and industries are utilizing, and depending heavily on using chatbots as virtual agents to assist them in their business. With the assistance of these virtual agents, users can acquire the required information, place an order, or purchase at their convenience (Sivaramakrishnan, Wan & Tang 2007).

With the inception of chatbots in different business sectors, the market size is growing exponentially, from \$250 million (in 2017), to almost \$1.34 billion, by the year 2024 (Luo et al. 2019). The focus of this study was on the banking sector, utilising chatbots, as their representatives to communicate with their customers. Trivedi (2019) discussed that the banking industry has been the early adopter to implement the use of chatbots for providing information and interacting with the customers. Offering relevant information and providing a sound virtual experience leave the customers satisfied (Price 2018).

The purpose of this research was to investigate and explore the influence of chatbots' service quality on customer satisfaction in the banking sector. When talking about the various types and forms of AI, human-chatbot (virtual agents) interactions have been the most prominent ones. One such study published by Zarouali et al. (2018) stated that human-chatbot interaction needs further exploration, in order to have a better understanding about the chatbots' service quality, and the customers' satisfaction.

The research in point conducted an extensive investigation on this topic, which enables the readers to have a better understanding about the influence of chatbots on customer satisfaction, specifically in the banking sector of UAE, since there is a lack of published evidence in this area. Therefore, this research was the perfect platform to explore and learn about the influence of chatbots on customer satisfaction in the banking sector of United Arab Emirates (UAE).

Chatbots are also used extensively in other fields, such as hospitality, tourism, healthcare, and education, serving different purpose in each of these sectors. They differ from the banking sector in the way that chatbots are more customer-focused and sophisticated when compared to the other sectors, since the information provided by the banks is highly confidential. Thus, the study seeks to answer the following research questions:

Furthermore, to have more augmented quality of data, qualitative inductive research method was applied, being exploratory in nature, as it assisted in providing enriched data, with in-depth information and feedback from the participants. Data saturation was achieved by around 25 participants, adopting a cross-sectional research design, investigating consumers' responses and intentions towards interacting with chatbots, by conducting interviews over a period of 3–4 months.

The outcome of this research developed a conceptual framework, which identified the factors causing customer dissatisfaction once they interacted with chatbots. Overall, a total of eleven themes and sub-themes emerged from the gathered data. This research further added value to customer experience, voluntariness to interact with chatbots, subjective norms, and the actual system usage. Additionally, certain practical implications were also discussed for the banks to consider and implement.

# **2 Literature Review**

# **2.1 Artificial Intelligence in the Banking Sector**

Chatbots, digital assistants, voice-activated services, concierge robots, and virtual assistants are all different terminologies used for similar type of AI-enabled systems (Prentice & Nguyen 2020). In this research, the focus is on the chatbots which are utilised by the banks, as a customer service representative.

Various authors and researchers have defined AI in different ways. From the time it was first discovered to the present time, the definitions have evolved as well, keeping in mind the various perspectives. To start with, in 1950, Turing (P. 435) defined it as "such a human interaction between another human and the machine where an individual is unable to differentiate between the human and the machine, hence the machine is termed to be intelligent". Haenlein and Kaplan (2019) stated that "such an ability of the system that can explain the external data accurately, that can learn from the same source, and by using and incorporating that data, specific tasks and goals can be achieved by being flexible".

# **2.2 Chatbots in the Banking Sector**

Chatbot technology is being applied in various businesses and industries like banking, hospitality, tourism, and healthcare. With the help of these conversational chatbots, different businesses can collect and store larger sets of data in terms of variety, while assisting them to become more cost effective and sustainable (Campbell et al. 2020; Um, Kim & Chung 2020).

With the help of appropriate coding, programming, and algorithms, chatbots are trained to identify similar customer queries and respond to them in a specific pattern (Campbell et al. 2020). Belanche et al. (2020) highlighted that robots (chatbots) are being considered as frontline employees for most of the businesses, as they can easily communicate with the customers, receive and store larger data of information, and are cost effective. Chatbots are well-equipped with intuitive and empathetic skills, which is helping them in replacing all kinds of jobs performed by humans in various industries, particularly those of customer service representatives (Belanche et al. 2020; Huang & Rust 2020a, b). However, Um, Kim and Chung (2020) contended albeit chatbots make work easier for organisations to utilise them, they still have drawbacks; failure to understand and provide appropriate service to the customer, incur additional costs; maintenance, installation and training.

#### **2.3 Extant Literature on Chatbots**

John McCarthy; the father of "artificial intelligence" coined this term first in 1956 during a summer research project (Haenlein & Kaplan 2019). Since the research in the field of AI-enabled systems (chatbots) is still in its infancy stage, the following Table 1 is devised based on the key information provided by various journal articles.

#### **2.4 Banking Sector of UAE**

The world has witnessed substantial developments in AI-based innovations across the global banking sector, which serves customers that are using diverse banking networks (Kumar, Sujit & Charles 2018). There is still an impending need for research when it comes to the implementation of AI-based technologies in the banking sector of UAE. Hence, it was necessary to present a concise synopsis of UAE's banking sector.

Over the years, UAE has become a strong proponent and fore-bearer in introducing the latest technologies to its various sectors. Talking about the banking sector, nearly all the banks based in UAE have now implemented AI-enabled systems (chatbots and virtual assistants) (Mehta & Bhavani 2017). Al-Marri, Ahmed and Zairi (2007) signified the fact that when it comes to the perception of customer service quality, UAE's banking sector is large enough to cover this area with vast outreach and better outcome. Digitalisation of the banking sector backed by AI-enabled systems has given the UAE's banking sector a growth of 13.9% in terms of profitability (KPMG 2020).

While banks in UAE are making extensive efforts to retain and satisfy their customers, it is imperative to primarily satisfy their needs employing the cutting-edge technological innovations that are in place (Kumar, Sujit & Charles 2018). Alhosani *et al.* (2019) discussed the benefits and drawbacks of implementing the latest technologies. One of the major drawbacks faced by UAE's banking sector in implementing the latest innovations is that they have been the target of cybercrime attacks, resulting in data compromise of approximately 14 million records in the year 2018, whereas, UAE lost roughly \$1.1 billion to cybercrime attacks in 2017 (Rosberg 2018; Alhosani *et al.* 2019). Another major reservation faced by the banks in UAE is the customer satisfaction, which is not earned easily in this part of the world (Sleimi, Musleh & Qubbaj 2020).

#### **2.5 Customer Satisfaction**

The word "satisfaction" is derived from Latin "satis" meaning "enough", and "facere" meaning "make or to do". Hence, the combined meaning of these words mean to provide the products and services which have the capacity to be "enough" (Oliver 2010). The meaning of the word satisfaction is also dependent on the context. For instance, if "satisfaction" is used in a marketing context, its meaning becomes more specific (Parker & Mathews 2001).

Söderlund (1998) defined customer satisfaction as, "the value one receives after purchasing or using a product or service". It is worth mentioning here that the context in which customer satisfaction is mentioned is very important, since the meaning changes from one context to another.

**Table 1.** Extant Research on Chatbots (Table Compiled by the Researcher)

(*continued*)

# **2.6 Repurchase Intention**

When someone mentions customer satisfaction, they cannot leave repurchase intention out of it, as they both go hand in hand. It will not be wrong to say that it is the customer satisfaction that leads to repurchase intention (Hellier et al. 2003). Repurchase intention is defined as, "the willingness to purchase something again" Dictionary (2021a).

Suhaily and Soelasih (2017) mentioned that customer satisfaction is a compulsory factor when it comes to the repurchase intention towards a product or service, based on the overall quality provided to the customer. Furthermore, Mittal and Kamakura (2001) elaborated that once the customer reaches threshold or the tolerance level towards the repurchase, it may vary from person to person to display the repurchase intention, as not everyone have similar characteristics when it comes to repurchasing.

# **2.7 Word of Mouth (WOM)**

Word of mouth (WOM) is another important factor that plays a vital role when it comes to customer satisfaction and repurchase intention. According to the Cambridge Dictionary (2021), WOM is defined as "such a process in which the information is being passed on from one person to another through the medium of verbal communication". WOM has developed into an important means of communication for the field of marketing, which is defined as "the conveyance of message from person to person using oral communication" (Matute, Polo-Redondo & Utrillas 2016).

Communication of information between customers about the products/services, or the company is considered crucial when it comes to the repurchase intention, as customer needs to be completely satisfied before making an informed decision to purchase a certain product/service (Litvin, Goldsmith & Pan 2006). WOM has also transformed over time into electronic word of mouth (eWOM) and algorithmic word of mouth (aWOM), where eWOM is electronic conveyance of message or information, and aWOM is the AI enabled word of mouth using all the sophisticated technology to communicate among people (Williams, Ferdinand & Bustard 2019).

# **2.8 Technology Acceptance Theories**

Despite the ease and convenience provided by chatbots to the organisations, more attention needs to be paid to customer satisfaction, since chatbots are AI-enabled systems, programmed by humans, lacking flexibility (Prentice & Nguyen 2020). Various researchers have come up with different technology acceptance theories, where the most relevant ones are Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology Model (UTAUT), Unified Theory of Acceptance and Use of Technology Model 2(UTAUT 2), and Technology Acceptance model 2 (TAM 2).

### **2.9 Technology Acceptance Model 2 (TAM 2)**

Technology Acceptance Model 2 or TAM 2 is an extension of the Technology Acceptance model, developed by Venkatesh and Davis (2000). TAM 2 integrated additional external constructs to the original TAM model (Venkatesh & Davis 2000). The objective of this theory was not only to contemplate on user acceptance of technology, but also to understand the user adoption behaviour through social influence and cognitive instrumental processes (Venkatesh & Davis 2000).

One such study published by Ali et al. (2021) argued that the extension of TAM can also be adopted when it comes to the use of cardless banking system. They further suggested that perceived risk plays a big role when it comes to consumers' intention of use, based on the extended TAM model (Ali et al. 2021). Hence, this theory was the perfect fit to be used and implemented for the purpose of this research.

# **3 Scope of the Study**

The "scope of the study" for this research is shown above. As illustrated in Fig. 1, the focus was on achieving customer satisfaction while an individual uses the chatbot services in the banking sector. This led the customer towards deciding about their responses

**Fig. 1.** Scope of the Study

and intentions related to the chatbots and their service quality. The focus of this research was, (and not limited to) "Going with the same bank", "Changing bank", "Complaining to the bank (Does the complaints reach the responsible person/department?)", and "Word of Mouth (WOM)", respectively.

As elaborated in the literature review, the outcome of customer satisfaction helps the customer to decide about their responses and intentions. Since chatbots are relatively new in the banking sector, there have been mixed responses about the interaction between the chatbots and an individual, as highlighted in the recent research in different business areas. However, the influence of chatbots on customer satisfaction in the banking sector is novel.

# **4 Data and Methodology**

The target population for this research were all the individuals who interacted with the bank directly or indirectly. The sample size for this research was determined based on reaching data saturation. To target the right participants, this research implemented a non-probability, stratified purposeful sampling technique, based on the age category. For this research, a total of 25 participants were interviewed, achieving data saturation. An expert interview, semi-structured survey instrument was employed with open-ended questions guided by the underpinned theory and literature.

#### **4.1 Data Collection and Sample**

For the data collection of this research, a face-to-face, semi-structured expert interview with open-ended questions was implemented. Non-probability, purposeful stratified sampling technique was used to collect the required number of participants for this research (until data saturation was achieved). The participants were contacted through emails, WhatsApp, and over the phone. Due to Covid-19 pandemic, online platforms; Zoom and Google meets were used to interview the participants, in lieu of interviewing them in-person. Moreover, interview guide was established to stay on track with the purpose of this research, along with the questions for this interview, which were guided by the theory and literature (Kvale 2007; Given & Saumure 2008; Creswell 2009a, b). For collecting audio records, two separate high-quality instruments were used; iPhone 12 Max Pro, and HP Pavillion laptop. The researchers used iPhone's "Notes" and Google Doc's "Dictation" function to transcribe the data.

#### **4.2 Coding Procedure and Thematic Analysis Framework**

NVivo12 software was used to describe and classify data to interpret into codes and themes. With the help of NVivo12 software, a large set of data gathered from interviews was analysed in a systematic way. Initial coding was carried out manually, and then all the coded data and themes were transferred to NVivo12 for further organisation and analysis. It also assisted in describing the essence of phenomenology for this research. Analysing the data using NVivo12 further assisted in enhancing the consistency, and validity of this research. According to Creswell and Poth (2018), it is duty of the researcher to write a detailed description of the themes, that are developed from the study. Once all the data was transcribed into Word document, it was then run through NVivo12 for further analysis into themes, and sub-themes. For this research, the data was coded using open coding, axial coding, and selective coding.

# **5 Findings**

A total of 25 participants were interviewed, which was clearly sufficient to achieve data saturation, using open-ended semi-structured expert interview questions. Out of the 25 participants, 12 participants were expatriates with various nationalities (USA, UK, Spain, Australia, Pakistan, India, South Africa, Macedonia, Bangladesh), while 13 were GCC nationals from UAE, Oman and KSA. 23 participants were Male, whereas only 2 Females participated to answer the questions of this research. All the participants were from various age groups ranging from 18 to 56 and above. As for the educational background, the participants were fairly educated, ranging from holding Diploma to PhD, respectively. The researchers implemented the Five-step process in Framework Analysis based on Ritchie and Spencer (1994), and Ritchie, Spencer and O'Connor (2003).

Once familiarising with the data collected from the participants was completed, the researchers were then able to identify the initial emerging themes and sub-themes. All these themes and sub-themes were gathered within a framework to develop an initial conceptual thematic framework, as shown below in Table 2.

A total of seven major themes and four sub-themes were developed that were consequently linked with three aggregate dimensions. The finalised thematic framework was developed using the individual interviews (containing the 1st order concepts, 2nd order themes and sub-themes, and aggregate dimensions). The aggregate dimensions deducted from the thematic framework were "*Perceived Chatbot Service Quality*", "*Customer Satisfaction*", and "*Responses and Intentions*".

All the sub-themes that imitated related or parallel data gathered from the individual participants were recorded under the appropriate main themes. All the themes and subthemes formulated the thematic framework based on the three-level framework suggested by Gioia, Corley and Hamilton (2012).

**Table 2.** Conceptual Thematic Framework of Initial Themes and Sub-themes

#### **5.1 Aggregate Dimension One: Perceived Chatbot Service Quality**

Perceived chatbot service quality was labelled as the first aggregate dimension. This dimension revealed how the customers perceived chatbot service quality of their bank. It comprised of the 1st major theme "chatbot efficiency" and its sub-theme "service information and understanding", 2nd major theme "chatbot interaction" and its subtheme "limited options and emotional aspects", respectively. Almost all the participants agreed that chatbots are not very efficient when it comes to assisting or solving their problems, as depicted in the following excerpt from the interview of participant no. 12:

*"I don't know how it would be better, or how people would get used to it, but it needs a lot of time to be more efficient. It is not that efficient nowadays unfortunately, with most of the places that I experienced." (P12)*

Another participant number 10 mentioned that chatbots require lengthy security information before it can verify the customer and proceed further with their request:

*"…Some entities tend to make it as a basic and an easy system for the customers, while some provide a very complicated system. Like in the banking sector, they tend to put some restrictions on securities, and this makes it difficult for the client to get what they want as easily as possible. I think that is the downside of using a chatbot in bank. It further needs to consider the type of service. Some services are easy to use with the chatbot, while some of them like banks is a bit difficult because of the security issues." (P10)*

# **5.2 Aggregate Dimension Two: Customer Satisfaction**

The second aggregate dimension was labelled as Customer satisfaction. This dimension revealed how satisfied the customers felt while interacting with the chatbot of their bank. It comprised of 3rd major theme "experience" and its sub-theme "urgent actions and problem solving", 4th major theme "participant's feedback", and the 5th major theme "human interaction" and its sub-theme "human representative and assistance", respectively. A few examples are portrayed below, from the excerpts of participant's interviews:

*"…if you are in a hurry and you have some other work to do, it is extremely necessary for you to finish with the bank, but it takes 5 to 10 minutes, then that is very annoying. For me, I have been at both the ends, I mean I have experienced both; being satisfied with its service and annoyed with it at times." (P22)*

*"…it was very interesting because this is something all of us have experienced at some point, happily and unhappily. So, I also had mixed kind of reaction while interacting with a chatbot. However, I distinctly remember my first interaction. I was intrigued that how this was going to work. I wouldn't say it was too bad, but it was not the best experience that I had. I still remember my ATM card had gotten stuck in the ATM machine; it was not coming out. I was trying to reach the helpline, but I couldn't reach any helpline. So, that was the first time I interacted with a chatbot. Now the thing with the chatbot is that it gives you almost the perfect information that one could possibly be looking for, and it gives you all the details that you might find necessary for your need of the hour, but also it is limited in a way that it is a programmed machine." (P3)*

### **5.3 Aggregate Dimension Three: Responses and Intentions**

Responses and Intentions was labelled as the third aggregate dimension. This dimension revealed about the participants responses and intentions once they interacted with the chatbot of their bank. It comprised of 6th major theme "suggestions from experience" and 7th major theme "Word of Mouth (WOM)", respectively. The following examples illustrate participant's responses related to the aggregate dimension three:

*"…the other one which I think is about the age factor especially the old people, I don't think that they are very technical people. Maybe out of 100, 2 or maximum 3 people will know or are familiar with chatbot. Other than that, I am sure they are avoiding it and prefer to interact directly with a human representative or visit the branch itself." (P1)*

*"I am a technology savvy person, but everyone is different than the other human, and let us not forget who hardly understand technology like aged people, labour class and uneducated people, for example. So, banks need to consider their situation as well to try and create a way for their easy access as well, because honestly, not everyone is willing to spend money on costly calls and end up with no result at the end." (P20)*

# **6 Discussion**

Analysis of stories and experiences shared by the 25 participants revealed that customers are hardly influenced by chatbots in the banking sector, resulting in their dissatisfaction with it. As mentioned in the extant literature on chatbots, Um, Kim and Chung (2020) suggested that self-service technology is much more efficient than AI-enabled systems like chatbots and virtual assistants. Participants highlighted the fact that chatbots are good and highly efficient in other sectors like hotels, hospitals, airlines etc. They feel that banks are not quite ready when it comes to chatbot's efficiency.

As emphasised by De Cicco, e Silva and Alparone (2020) that people need to connect on an emotional level when they interact with the bank. Unfortunately, that is not the case when it comes to interacting with the automated systems introduced by various banks. Sleimi, Musleh and Qubbaj (2020) also stated that not everyone is content with using the latest technology in the banking sector.

Additionally, the literature also predicted that customers need quality service based on their demands (Pakurár et al. 2019). Again, this is not what participants felt when they interacted with chatbot. It was due to poor chatbot quality in banks. Since no prior research has been conducted about the human interaction with chatbot, this research was able to discover that chatbots provide clear instructions on how to navigate or what options to choose, but for simpler requests only. On the contrary, when it comes to emergency situations, there is still no comparison with a human representative, as humans are much more understanding and cooperative.

Furthermore, in literature Moriuchi et al. (2020) also emphasised that organisations have started implementing chatbots as their customer representative, but they have neglected the customers' side whether they like interacting with them or not. As for the sub-theme, emotional unavailability and limited options were one of the most important findings of this study and the most reported concern by the participants. One such research published by Elsholz, Chamberlain and Kruschwitz (2019) mentioned that chatbots are limited when it comes to commands, languages and emotions.

Additionally, participants further revealed that human representatives are better at assisting and navigating through the offers and services, in a cost-effective manner. It was not the same case with chatbots, as there are different categories and sub-categories of options that take too much time to go through. As per Huang and Rust (2020a, b), AI-enabled systems are outperforming humans, hence a grave threat to their jobs.

Alternatively, a few participants had positive WOM about the influence of chatbots. Such experiences make up for the overall theme. However, a few participants were not bothered by others' opinion. They preferred visiting the bank personally to get their work done. According to the literature reviewed, negative WOM exerts far greater influence than the positive WOM, creating switching intentions (Um, Kim & Chung 2020).

# **7 Implications**

Theoretically, this research supported the marketing literature in the fields of bank marketing, marketing communication, consumer behaviour, product development and relationship management, respectively. Furthermore, the findings of this research could easily be associated with the underpinned Technology Acceptance Model 2 (TAM 2), as demonstrated in Fig. 2. All the black arrows indicate the original model suggested by Venkatesh and Davis (2000), whereas the orange arrows and boxes indicate the theoretical contributions of this research.

**Fig. 2.** Theoretical Contributions

The findings of this research could be considered as the variables of a new quantitative study. Hence, it could be used as an initial point of future studies associated with the influence of artificial intelligence (AI) on customer satisfaction. Moreover, the findings of this research further add value to customer experience, which matters the most when it comes to being satisfied with their interaction with chatbot. Based on what participants went through while interacting with chatbots, it was their experience which created a certain image of using such a system implemented by banks.

On the other hand, voluntariness of interacting with chatbots had an impact on the service quality. The more willing and volunteering the customers are to interact with chatbots, the better will be the service quality. There was also an addition of 5 new variables to TAM 2 model, namely emotionally draining, unwieldy interface/cumbersome navigation, misinterpretation/delusional, backend operational malfunction, and customer privacy and confidentiality. All these factors fall under the classification of customer satisfaction.

This research recognized and developed an understanding of the influence of chatbots on customer satisfaction in the banking sector of UAE. Therefore, based on the findings of this research, it could be linked to many aspects of banks' strategies related to customer satisfaction, responses and intentions, bank marketing, marketing communication, consumer behaviour, product development and relationship management, respectively.

Reflecting on the findings of this research, banks should develop new strategies to satisfy the customers by providing them easy to use services and improving chatbot service quality. Hence, this research would provide the banks with the knowledge of customer satisfaction, responses and intentions, drawbacks of chatbots' service quality, upgrading it to newer easier system, and devising new strategies to make the best out of implementing chatbots in banks in the United Arab Emirates.

The word "AI" or "chatbot" meant something very intelligent in people's perspective, which changed drastically once they interacted with it. Hence, it calls into account the banks' policies and strategies towards consumer behaviour and relationship management. By doing so, bankers will be able to understand better what is causing their customers' dissatisfaction, and how they need to adjust their strategies according to customers' demand. Therefore, the banks and marketing professionals need to understand that although banks are trying to adapt to new norms, technologies and policies followed globally, they need to devise a proper marketing strategy for it.

# **8 Limitations and Future Research**

Firstly, the research was conducted within UAE during the time when COVID-19 was at its peak. Due to the nature of this research, the authors had to conduct semi-structured, open-ended, expert interviews. Inviting participants was not a big issue but agreeing to meet and sit for an interview was quite challenging.

Secondly, the time constraint for data collection was also very crucial, as the researchers had to reschedule many of the online interviews due to the pandemic situation. Hence it took 3–4 months to gather all the required data. Another limitation faced during the completion of this research was not to interview labour class individuals, as they are not able to withdraw their salary from the ATM machine, so interacting with chatbots was completely out of the question for them.

Considering the findings as the foundation to further enhance and develop studies accordingly, it is highly recommended for the future researchers to explore quantitatively the influence of chatbots on customer satisfaction in the banking sector in UAE, using the factors and the proposed conceptual model in this research. For example, if there is influence of hybrid systems on customer satisfaction, how would the customers perceive service quality of hybrid systems? Does interacting with hybrid systems impact customer loyalty? To what extent do customer experiences with hybrid systems influence their intentions?

Moreover, future researchers need to focus more on the ways marketing could minimise the negative consequences on the influence of chatbots on customer satisfaction in the banking sector in UAE, knowing how to manage different marketing and consumer behaviour aspects in predicting their behaviour. Also, cultural, and other environmental factors affecting the influence of chatbots on customer satisfaction in the marketing perspective need to be studied.

# **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **The Challenges Facing Adopting Innovation Process in the Context of Project Management Performance in the Oil and Gas Sector**

Ahmed Basioni(B) and Khalid Al Marri

Faculty of Business and Law, The British University in Dubai, Dubai, UAE<EMAIL_ADDRESS><EMAIL_ADDRESS>
**Abstract. Purpose** – This paper explores the challenges and enablers of diffusing innovation processes within the oil and gas industry and the impact on the project management process in improving project performance.

**Methodology** – This study employs a qualitative analysis methodology with a structured survey to provide the data for this study.

**Findings** – The anticipated study findings will aid in identifying the primary obstacles to and facilitators of implementing innovation systems within the ecosystem of the oil and gas sector.

**Implications** – It is crucial to carry out follow-up research on integrating the "green innovation" concept to confirm the viability of establishing a conceptual framework for critical variables affecting the adoption of the innovation process within the energy industry.

**Originality –** Discussing the innovation process concerning project management and performance contributes to a broader understanding of a sustainable innovation process that supports expanding the oil and gas industry.

**Keywords:** Innovation · Process · organization · Oil · Performance · Project

# **1 Introduction**

Project managers often consider their projects' success to be met if they completed the project on time or even ahead of schedule, within budget, and fully compliant with set scope and quality. This has notably been referenced as project triple constraints: Time, Cost, and Scope. (Shenhar & Divr, 2007) (Fig. 1).

It is difficult to define "Innovation" as a management competency since, in business terms, innovation is the translation of an opportunity to a doable and feasible product. Conventional project management models concentrate on delivering projects and achieving set deliverables with a well-defined set of measurable execution strategies and success criteria. Unfortunately, this was considered with inadequate attention to creative thinking, where opportunities for innovation generally center on a reflexive problemsolving mode. For example, during the execution stage and when the project manager faced risks, he generated a mitigation plan to avoid the consequences of such risks or minimize the adverse impact generated in the project (Gallagher, 2015). The paper aims

**Fig. 1.** Project Triple Constraints (Shenhar & Dvir, 2007)

to discuss the challenges and enablers that face diffusing innovation processes and measurements on performance and efficiency in the context of project management in the energy sector. The research aimed to answer the following question:

*How does the Innovation Process contribution influence the performance and efficiency of the project management, considering the challenges and enablers for adopting innovation within the oil and gas sector*?

In subsequent sections, the study will explore two key areas: theoretical background and research framing. Theoretical background will comprise the literature review of the innovation theories, innovation process concepts and models, individual acceptance theories, project management, and critical enablers. The research framing will consist of theoretical framing, highlighting the existing researchers' studies concerning challenges and opportunities in adopting innovation systems within the oil and gas sector. Finally, the conceptual framework will be proposed for this paper, given the previous works conducted by introducing additional specific variables and dimensions related to the oil and gas sector.

# **2 Theoretical Background**

The primary study dimensions to assess the key challenges facing the implementation of the "Innovation Process" within the ecosystem of the energy sector and the correlation between the adoption of "Innovation Processes" and "Individuals Acceptance" in the project management contribution.

### **2.1 Innovation Concept and Process**

Innovation terminology creates something unique and valuable and adds new value to end users. Innovation is not synonymous with invention, which is related to generating new products; however, innovation is about creating value for customers. One interesting comparison of the two acronyms is that invention is the transformation of cash into opportunities, while innovation is the conversion of ideas into revenue. (Burmester et.al, 2005).

The innovation process mainly consists of three main stages: the front end of innovation, product development, and implementation. The front end is a detailed stage focusing on several sub-elements in terms of problem definition, exploring ideas, scope generation, marketing assessment, and the final stage, resulting in building a feasible business case. The front-end phase is considered the most essential part of the innovation process for proceeding with options to be further considered in the following stages of development and commercialization.

#### **2.2 Innovation Theories and Models**

The field of innovation management is broad and has several facets. Although innovation is sometimes viewed as an ethereal idea, it is a diverse subject of study with many models, theories, and frameworks. One method to characterize innovation is to divide it into two groups depending on the market it serves and the technology it employs. The innovation matrix model allows us to see the most prevalent forms of innovation, as illustrated in Fig. 2. Incremental innovation only makes minor adjustments to an existing product formulation or service delivery strategy, making them marginally better than the prior iteration of the good or service. Disruptive innovation is widely used by various industries where a concept, product, or service establishes a new value network, either by upsetting an established or brand-new market (Christensen et al., 2013). On the other hand, sustaining innovation is market-based and, rather than developing new value networks by meeting consumer demands, enhances and expands existing ones. Finally, radical innovation is uncommon since it has traits of disruptive innovation but differs in that it utilizes both a novel business model and a revolutionary technology simultaneously (Linton, 2009).

Another critical theory that is considered the basis of establishing the conceptual framework of this paper is the diffusion of innovations theory (Fig. 3). It measures the flow of newly generated opportunities through an existing ecosystem. Initial research explored that diffusion ideas were widely spread within consistent social gatherings such as families and communities. The main drive of these early studies was to promote personal innovation by focusing on behavioral adoption. These studies were further developed to examine the diffusion of more complicated advanced technological innovation on a larger scale, such as business processes and information systems, such as the health sector and education organization (Ven et al., 1999).

Studies contended that the innovation adoption process is more complex in organizations than among individuals. The innovation process is categorized into five main phases, recognized in two leading platforms: initiation, which includes setting an outline, and implementation, which includes redefining/rearranging and clarifying. Given this theory, the new directives emphasized the recreation of the innovation tools to correlate to the organizational context to generate new practices for innovation that add value to the organization and contribute to success factors. However, recent research concerns the innovation process to cross firms' borders with a broader spectrum of external domains, including projects, markets, and environment (Rogers, 2010).

**Fig. 2.** Innovation Matrix (Linton, 2009)

**Fig. 3.** Diffusion of Innovation Model (Rogers, 2010)

# **2.3 Individual Behavior Acceptance**

The Theory of Planned Behaviour (TPB) has, since its development some 20 years ago, proved to be a powerful approach to explaining human behavior. It has been successfully applied to a wide range of behaviors. Interestingly, particularly complex behavior, such as managerial decision-making, saw only a few attempts to use TPB. Creativity thinking has been highly acknowledged as a primary component in driving employees' success and ensuring organizations' sustainable and viable advantage in fast-changing and challenging business environments (Anderson et al., 2014). Team climate for innovation is considered one of the vital critical factors of employee creativity, emphasizing the support and reward of team efforts. However, earlier studies need more attention regarding the contribution of personality in influencing team climate for innovation.

# **3 Research Framing**

#### **3.1 Theoretical Framework**

In several literature contexts, the emphasis on process innovation is characterized by two primary constructs: Technological and Organizational innovation. Technological process innovations are tangible outcomes, such as products enhanced through technical improvement processes with extra functionality and benefits. On the other hand, organizational process innovations are intangible strategies related to process establishment, work environment, cultural ecosystem, and creative thinking frameworks (Edquist et al. 2000). Theoretical framework formulation was conducted utilizing factor analysis and empirical research based on three main theories contributing to the influence of the Innovation process on project performance and success factors. This includes Institutional theory, behavioral theory, and attribution theory. The central underlying notion of institutional theory is that structures with formal procedures, standards, and regulations enforce an authoritative framework on social behavior and interactions where Organizational processes can either support or constrain innovation (DiMaggio and Powell, 2012). The behavioral theory emphasizes stability development by reducing misalignment and conflicts in achieving objectives by establishing strategies that comply with constraints. Companies' reaction to performance discrepancies is examined through attribution theory (Ford, 1985). The attribution theory arguments have shown a connection between the intensity of a problem-solving effort and the value and uncertainty of an output (Weiner, 1986). Senior managers should consider innovation a viable remedy in the event of organizational decline, provided causal attributions relating to the stability and controllability of the fundamental causes are made (Rahimi et al., 2018). During the project life cycle, from initiation to the completion and handover, the project manager will dynamically generate all possible opportunities and ideas that add value to ensure achieving the set targets and objectives results are achieved. Due to the fluctuation in the market condition, fast technological progress, population growth, and high resource demand, innovation has become a core competency for project managers.

On the other hand, organizations face challenges in developing and implementing a robust innovation process as part of their ecosystem, such as (Gallagher, 2015): Risk, resistance to implementing new ideas to avoid risks to the organization and maximize the utilization of proven practices. Lack of knowledge means that many organizations need the tools to be innovative, so they cannot develop new ideas. Time constraints, where organizations adopt "reaction mode" "troubleshooting" problems, leave an inadequate period for creative thinking, innovation, and root cause analysis. Creativity is timeconsuming, where the mind should expand thinking about the problem and imagine solutions from a different dimension with proposed links for the final compelling idea for implementation. Organizational structure and size research has shown that once companies get large, they become less risk-tolerant and less willing to innovate (Deeb, 2014). Innovation mandates a culture of collaboration to be an organization's DNA, enforces the importance of creative thinking, and is based on team skills and expertise rather than their positions and authority. Influencing and building trust from leadership must be conducive to encouraging the implementation of the innovation process within the organization (Larson, 2015). Figure 4 illustrates the summary of the theoretical framework.

**Fig. 4.** Theoretical Framework for Project Performance in the context of Innovation Process, risks, opportunities, and theories.

# **3.2 Conceptual Framework**

According to Rogers' theory of disseminating innovations, five factors—relative benefit compatibility, complexity, trial-ability, and observability—influence the acceptance rate. In this paper, we added a third attribute that needs to be tested: individual acceptance influence on the contribution of the innovation process. Thus, it is determined that the dependent variable is the performance defined in terms of financial performance, and the independent variables are a technological factor, organizational factor, and individual behavior factor. Four central hypotheses were developed as follows:

### *Technological Factor*

O&G relies on proven technology to monitor and control process facilities and remotely develop large-scale database and security systems (Lappi et al., 2019). The smooth and effective integration of robust, high-speed, reliable, friendly use, and maintenance-free technology will strengthen the buy-in from all stakeholders in adopting the innovation process within an existing business model. This will positively accelerate decisionmaking, expedite deliverables completion, faster communication, time-saving, high efficiency, and maximum productivity (Almarri K., 2023).

Therefore, the first hypothesis is speculated as follows:

**H1:** Technological factors will influence the adoption of the innovation process

### *Organisational Factor*

Adopting an innovation process in the majority of an organization may lead to paired contrast impact, generating a motivation for innovation development, but on the other side, causing outrage in the business that could lead to risks. The continuous nature of innovative changes requires organizations to involve all available resources in the innovation process constantly. However, their size could be improved and sometimes needs to be improved to achieve consistent innovative transformations in the economic system. This causes the emergence of many stochastic processes that, due to their unpredictability and weak controllability, negatively affect the effectiveness of innovations (Zotov et al., 2020). Therefore, the second hypothesis is speculated as follows:

**H2:** Organisational factors will influence the adoption of the innovation process

#### *Individual Behavior Factor*

Personality differences are affecting the adoption of the innovation process. Individual willingness to try new things and risk aversion influenced the promotion of creative thinking. A positive attitude adds value to the business unit, company/firm, or industry. Individual behavior includes several factors such as motivation, trust, mindset, fear, social norms, self-image, risk perception, familiarity, acceptance, agility, etc. (Robert and Flin, 2019). Therefore, the third hypothesis is speculated as follows:

**H3:** Individual Behaviors influence the adoption of the innovation process

### *Project Performance*

The hypotheses consider one of the two intellectual streams: essential (in need) and available. The essential stream claims that low-performance organizations or projects strive to implement innovation to improve adverse circumstances (Amabile & Conti, 1999). One of the primary project performance indicators is financial performance in terms of return on assets (ROA) as a profitability ratio to measure financial success. In contrast to, for example, return on equity, ROA is more relevant as a profitability indicator since it illustrates how assets or resources are used to create revenue instead of investments because oil and gas firms are often capital intensive (Merrow, 2012). Therefore, the fourth hypothesis is speculated as follows:

**H4:** Innovation Process will positively affect project performance (Fig. 5)

**Fig. 5.** Proposed Conceptual Framework

# **4 Discussion**

Positive disruption is a crucial initiative for the organization to shift from a status quo slow position to a transformational era with high momentum in terms of productivity and efficiency. Despite the firm or organization scale, project practitioners are the engines of innovation. In the majority of organizations, the ideation process is the simple stage. However, converting those ideas and opportunities into feasible concepts and then upgrading them to the final product or service.

In order to manage a breakthrough or innovative project, it is essential to blend new ways of thinking with familiar processes. Oil and gas are innovation attraction sectors where critical businesses require safe and cost-effective solutions to meet the worldwide strategic growth plan. The research recognizes four opportunity stages to integrate the innovation process within significant projects, including the Bridging stage, an initiation stage consisting of preparation during front-end planning. It integrates innovative practices, technologies, and organizational processes to enhance project performance. The engaging stage focuses on creating an innovative incentive scheme for engaging stakeholders, establishing simplified tendering and contracting prices, and generating a reward scheme with creative thinking, crowdsourcing, and innovative solutions. The leveraging stage takes place post-contract award, and the entire supply chain is generated. Establish a platform with all stakeholders to develop new opportunities, improve practices, and enhance technologies. Finally, the exchanging stage falls after project completion, where lessons learned, and innovation history are communicated among the entire organization ecosystem to share experiences for future growth (Davies et al., 2014). The literature review observed a strong relationship between the organization, technology, and innovation in promoting digitalization and Artificial Intelligence as critical contributors to the firms' performance enhancement. With its iterative nature, the agile approach to project management could be ideal for creative projects with a high degree of uncertainty.

Other theories emphasize the level of acceptance and buy-in from the human factor, including leadership support and employee engagement to adopt innovation and technology within the ecosystem of the organization for value generation. The following relationships are:

One **limitation/gap analysis** in this study that needs further development and exploration is the correlation of "Green Innovation" within the Oil and gas sector on the company's performance. The link between green innovation and the market for energyrelated products has drawn more attention as environmental consciousness has grown. Miao et al. (2017) state that green innovation can increase energy usage effectiveness. In addition, the transfer of advanced technology is advantageous for lowering total energy consumption and intensity (Waheed et al. 2021).

# **5 Conclusion**

Researchers examine the diffusion results and implementation processes to understand better and increase the market acceptance rates of innovations. The majority of organizational innovation study has been on bureaucratic companies that are hierarchically constituted and competing in single marketplaces. Organizational climate, culture, and climate-related elements impact an organization's innovation ability. People are where innovation begins. They could be in management or professional employment. Thus, it is critical to maintain their innovation-related motivation. The culture of their workplace also impacts people. Workers' inventive behavior would also be influenced by how they feel about their organization's support for innovation. In order to implement the creative atmosphere, several aspects must be prioritized, including challenging work, freedom to think and adapt, time allotment, leadership support and encouragement, learning from errors, involvement, and trust-building (Prather, 2000).

# **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Soft Law in International Investment Law**

Bini Saroj1,2,3(B) and Abba Kolo3

1 Bar Council of Kerala, Kochi, India<EMAIL_ADDRESS>2 CIArb, Dubai, UAE

3 Faculty of Business and Law, The British University Dubai, Dubai, UAE

**Abstract.** This paper aims to analyze the concept of soft laws and its significance in international law with special emphasis on its effect on international investment law. In this paper, the authors have relied upon the works of legal scholars and arbitral jurisprudence to understand their observations about the role of soft laws and apply such observations to the present times to analyze the strengths and drawbacks of soft law in the current international investment law context. The paper argues that although formally non-binding, yet in practice soft law instruments do influence the behavior of states and foreign investors either directly or indirectly. They also play a significant role in the formulation of international law by serving as precursors to the formulation of binding rules by states or by developing the rights and obligations of the parties mainly through interpretation of vague or open-ended investment treaty provisions by arbitral tribunals.

**Keywords:** Soft Law · Hard Law · International Law · Non-Binding · Treaty · Investment Law · ICSID

# **1 Introduction**

International investment law (hereinafter referred as "IIL") and Investment related dispute settlements have shifted from the periphery of international law to its core over the course of past few years. And in the process of becoming the epicentre of the international regime it has also actively shaped the international law while adapting to its predetermined structures and notions. There is an ongoing debate among scholars about the role of soft law in international investment law. The Authors in this paper argue that the importance of soft law in international investment law has grown exponentially and that states and international organisations frequently and purposefully select softer forms of legalization as superior institutional frameworks in today's day and age.

#### **1.1 Research Objectives**

• To examine the nature and characteristics of soft law instruments in international investment law.

# **2 Research Methodology**

The authors have conducted a thorough review of academic literature on the topic. This involved studying scholarly articles, books, and other publications that discussed the concept of soft law in the context of international investment law. Secondly, the authors engaged in the analysis of relevant case laws and regulations. This entailed examining judicial decisions and legal provisions that are related to soft law in international investment law.

Overall, by employing a doctrinal research methodology, the authors have gained comprehensive insights into the concept of soft law in international investment law, its sources and forms, and the challenges and limitations it poses in practice.

#### **2.1 What are Soft Law Instruments?**

There is no consensus on a standard definition of soft law. Lord Arnold McNair, first president of the European Court of Human Rights coined the term "soft law". (Fabián Augusto Cárdenas Castañeda 2013) Hard law refers to precise legally binding obligations and that can delegate authority for interpreting international law. From this perspective, hard law is considered as the sources of international law mentioned in Article 38 of the International Court of Justice (ICJ) Statute. The realm of "soft law" begins once legal arrangements are weakened along one or more of the dimensions of obligation, precision, and delegation. This softening can occur in varying degrees along each dimension and in different combinations across dimensions.

For example, in 2005, Michael Joachim (Joachim 2005) defined "Soft law" as the normative instruments of law that have virtually little to no legal obligation and are imposed due to voluntary acceptance of the states. In other words, Joachim ascribes no legal weight to soft law in the absence of voluntary acceptance of the instrument by states (Weil P. 1983). On other spectrum, Meyer (Meyer T, 2010), defines soft law as *"those international obligations that, while not legally binding themselves, are created with the expectation that they will be given some indirect legal effect through related binding obligations under either international or domestic law."* (Zimmermann A. 2021; Dinah 2009; Gabrielle Kaufmann-Kohler, 2010; Richard Baxter 1980).

For purposes of this paper, the authors use the shorthand term soft law to distinguish this broad class of deviations from hard law—and, at the other extreme, from purely political arrangements in which legalization is largely absent. Soft law comes in many varieties: the choice between hard law and soft law is not binary (K.W Abbott and Snidal 2000, pp. 54).

#### **2.2 Characteristics of Soft Law**

The legal effect of different soft law instruments is not the same.

Even if these are the general characteristics of soft law at certain times, they acquire the characteristic of the hard law such as treaties while in an application. While the legal effect of different soft law instruments is not necessarily the same, it is characteristic of all of them that they are carefully negotiated and often carefully drafted statements, which are in some cases intended to have some normative significance despite their non-binding (Boyle A 1999, pp. 906).

# **3 Types of Soft Laws**

Different forms of soft law can be used based different factors. Generally, based on the functions soft law may be categorized into two types primary and secondary (D. Shelton 2008).

**Primary soft law** addresses the international community as a whole adopting organization. Primary soft law often functions as an intended precursor to the adoption of a later treaty or a customary form. It includes Draft Multi-Lateral Treaties, Model Treaties, Model Laws.

**Secondary Soft laws** are instruments designed to fill the vacuum. These documents might from various groups such as governmental non-governmental or private parties. They might be used to fill the gaps deliberately left in hard law (D. Shelton 2008). It includes Resolutions, Principles and guidelines, Decisions of tribunals, Restatements of law institutes, annotations and commentaries on different treatise.

# **4 Functions of Soft Law**

Due to technological changes and globalization, international law has become complex, and the traditional international hard law is not adequate to meet the requirements to accommodate the growing complexity of international relations. Soft law plays an important part in the international regime and it does so in a variety of forms (Jean d'Aspremont 2008 pg. 1081).

#### (i) **Functions as a guiding light to fill in the gap created as result of absence of treaty law**

Soft law is often used as a guiding light by international courts and tribunals when there is an absence of treaty law to guide their functions. Soft law tools provide for a compliance regime for both national and international players (D Shelton 2004). Soft law helps the players to adapt to the rapid changes in the international law, soft law instruments are therefore very malleable and useful to fill the gaps in the international law and often considered as a close substitute to the treaty law (F. Sindco 2006).

The changing attitude of the judiciary towards soft law instruments can be clearly observed in the case of Qatar-Bahrain Maritime Delimitation where the court was of the opinion that any soft law instrument can have a binding effect and, the classification between hard and soft law is not only on the basis of it being a treaty or not. (A. Boyle 1999) this view of the court was echoed in the case of Richardson v. Forestry Commission as well as the Common wealth vs. Tasmania (M. Chinkin 1989). Furthermore the Nicaragua Case, Gabickovo-Nagymaros Dam Case and the Nuclear Weapons Opinions are supplementary examples of how the judiciary has echoed the use of soft law instruments in forming a more binding effect by supporting the customary law (A. Boyle 1999). In the context of international investment law, tribunals have relied on soft law to fill-in gaps in procedural and substantive applicable laws.

The binding effect of these soft instruments varies in different cases, like in the case of Interocean Oil Development Company and Interocean Oil Exploration Company v. Federal Republic of Nigeria where the soft law instrument was given a full binding effect, or in the case of Fouad Alghanim & Sons Co. for General Trading & Contracting, W.L.L. and Fouad Mohammed Thunyan Alghanim v. Hashemite Kingdom of Jordan wherein soft law instruments were functioning in the role of guiding principles without completely binding the parties (A. Boyle 1999).

With changing times, the ICSID has also come to recognise the IBA guidelines as an essential reference and apparatus in implementation of international regime in the case of Total S.A. v. Argentine Republic (A. Boyle 1999). Total S.A. v. Argentine Republic and Alpha Projektholding GmbH v. Ukraine are examples of cases where procedural soft law was used in international investment law.

#### (ii) **Functions as an aid for interpreting and reinforcing treaty law**

Soft laws such as recommendations, resolutions, standards and decisions of other tribunals are used to interpret the meaning of terms used in treaty law (A Boyle 1999). Soft law explains and clarifies treaty law that is ambiguous in nature by either clearly ambiguous terms of the treaty or by providing the clear purpose for which the treaty is implemented, this supplementary nature of soft law is clearly visible in the rules for protection of foreign investment (J. Friedrich 2013).

In recognition of their limited legitimacy, investment tribunals would like to be seen to decide cases based commonly accepted principles rather than based on their idiosyncrasy or personal views. By demonstrating that their decisions are in accord with such principles, perhaps as elaborated by other tribunals, it helps enhance the legitimacy and credibility of the tribunal (Thomas Walde 2004; Moshe Hirsch, 2011; Zimmermann, 2021). This is illustrated in the observation by the tribunal in *Saipem SpAv. People's Republic of Bangladesh,* ICSID Case No. ARB/05/07, 21 March 2007, para. 67:

Furthermore, substantive soft law can be used to guide interpretation of investment treaties, provide guidance on issues such as sustainable development and corporate social responsibility, and environmental protection. *Copper Mesa v. Ecuador* and *Philip Morris v. Uruguay* are examples of cases where substantive soft law was used in international investment law.

In *Copper Mesa v. Ecuador* (PCA Case No. 2012-2), The tribunal considered a range of soft law instruments to determine the content of the BIT's provisions on fair and equitable treatment and expropriation. In *Philip Morris v. Uruguay* (ICSID Case No. ARB/10/7), The tribunal considered a range of soft law instruments, to determine the legitimacy of Uruguay's measures under the BIT's provisions.

And in *Urbaser S.A and Consorcio de Aguas Bilboa Bizkaia Ur Partzuergoa v. The Argentine Republic,* ICSID Case No. ARB/07/30, Award of 8 December 2016, the tribunal relied on soft law instruments in holding that human right to water is an obligation that binds foreign investors as it does State actors.

The aforementioned cases demonstrate investment tribunals are used soft law for interpreting the investment treaty standards (Giovanna Adinolfi 2021).

#### (iii) **Functions as a precursor to treaty law**

The adoption of soft law instruments may serve as precursor to treaties or hard law. Soft laws such as conventions and resolutions can use as a tool for future negotiations. (A Boyle 1999, Zimmermann, 2021) Nonbinding documents are initially used as the first step for adopting treaty law. The reasons for that step include the state's freedom to adopt more progressive norms (AcKiss/D Shelton 2004).

In the context of international investment law, parties to some of the more recent bilateral and multilateral treaties have for example, incorporated some soft law standards. While some of these standards were incorporated in the Preamble of treaties, which suggests that they were probably meant to have non-binding effect rather than binding others were incorporated in the substantive parts of the treaties, which suggests that they were probably meant to be binding or serve as applicable law in the event of a dispute just as the other substantive standards except if the terms used by the State parties to the treaty are hortatory (Adinolfi 2021; Korzun 2023; Jean-Michel Marcoux, 2021).

#### (iv) **Functions as a collaborator with hard law to produce customary international law**

Soft law instruments collaborate with hard law to provide a foundation for creation of customary law in the international regime. Treaty law applying or referencing the soft law has increased the interaction between soft law and customary law (J. Charney 1993, Zimmermann 2021). In the recent times this interplay has been evident as not only the treaty law but the soft law instruments are used as well to establish the existence of law (T. Treves 2010; Adinolfi 2021; Korzun 2023). The decisions of the ICJ to rely on resolutions to support its decision are an example of the increased importance of soft law in the international customary law (A. Boyle 1999). The Court also upheld the role of soft law instruments in laying down the foundation of customary law in the Nicaragua Case m (M. Chinkin 1989).

# **5 Strengths and Weaknesses of Soft Law**

#### **5.1 Strengths of Soft Law Instruments**

Soft law instruments are gaining prominence in the international legal regime as opposed to the more accepted treaty law because of the following reasons:

#### **5.2 Weakness of Soft Law Instruments**

Soft law is flexible in the sense that it allows easy procedures for its implementation and amendment, however this also means that they are a more informal instrument of law to and there are no harsh consequences to noncompliance. (M. Chinkin, 1989). As compared to the binding treaty law, soft law does not require the state players to incorporate the soft law into their domestic regimes, as a result the soft law may at times be conflicting to domestic laws of state players.

# **6 Conclusion**

The international legal regime, is not a simple straightforward regime, there is no black and white when it comes to soft law. The International Legal Regime requires its instruments both soft and hard to adapt and provide solutions for the challenges that arise due to the ever-developing nature of the International Regime. Over the course of this article, the author has tried to establish that there is an emergence of shades of grey when it comes to the role of soft law instruments in the International Legal Regime, and that soft law is no longer classified only on the basis of it not being a treaty. The authors during the course of this study have highlighted the increasing importance of soft law instruments and our Analysis on the Role of soft Law instruments in the international Investment law has resulted in understanding that the effect of the traditional disadvantages of the soft law have subsided in that, soft law is no longer only a non-binding instrument which is utilized by the state and international players to avoid the consequences of hard law as soft law generates binding effects on the key players in international investment process. Upon analysing the current situation of the soft law in the international investment context, we shall agree with the opinion of Irit Mevorach that traditional concepts of hard law and soft law regarding implementation are misleading and inaccurate. Our analysis suggests that the distinction between hard and soft law is more nuanced than binary.

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# **The Impact of Workforce Agile Behavior on Organizational Innovation Performance for Manufacturing Enterprises**

Amal Abdulrahim Mohamad(B)

The British University of Dubai, Dubai, UAE<EMAIL_ADDRESS><EMAIL_ADDRESS>
**Abstract. Purpose** - This paper intends to study the relationship between workforce agile behavior and organizational innovation in manufacturing enterprises. The research focuses on the impact of three characteristics of workforce agility (proactivity, flexibility, and resilience) on organizational innovation performance using the planned behavior theory (PBT), with a particular emphasis on product and process innovation.

**Methodology** - The research of this paper uses the theoretical background for the selected topic to propose a conceptual research framework to improve manufacturing enterprises' innovation performance by understating the significance of workforce agility.

**Findings -** The expected outcome of the paper is to understand the relationship between agility in the workforce and innovation performance in manufacturing organizations. It can show the positive impact of a proactive, flexible, and resilient workforce in manufacturing enterprises to guarantee continuous growth in the rapid market landscape.

**Implications -** This study can support leadership and employees in the manufacturing sector to understand the importance of providing agile capabilities in the work environment to improve innovative organizational performance, sustain competitive market advantage, and respond to internal and external changes.

**Originality/value -** The paper addresses the literature gap on how organizational innovation can be influenced by workforce agility behavior in the manufacturing sector and helps understand the significance of agility practices in sustaining competitive advantages.

**Keywords:** Organizational Agility · Workforce Agility · Agile Manufacturing · Organizational Innovation · Theory of Planned Behavior (TPB)

# **1 Introduction**

Agility is becoming increasingly vital for all businesses in today's highly competitive and consistently changing economic landscape. Organizational agility (OA) has emerged as one of the valuable organizational paradigms managers should follow to generate sustainable competitive advantages and as a critical business success factor (Teece et al. 2016). An increasing number of researches show that business enterprise and R&D investments improve innovation; however, a recent study emphasizes the importance of OA in driving corporate innovation and competitiveness (Franco & Landini 2022). In addition, Sherehiy et al. (2014) address that for a few decades, academia and industry have been concerned with how organizations can effectively adapt to unpredictable, dynamic, and continually changing surroundings. Nevertheless, there were minimal empirical studies on the relationship between agile organizations and agile workforce behavior as drivers of innovation. According to Breu et al. (2002), the success of an agile organization results from the strategic application of individual competencies and behavior toward agility. Nevertheless, no studies have examined workforce agility (WA) from an attitude perspective, showing people's intention to behave agilely (Asari et al. 2014).

Moreover, there is less attention to the significance of WA as a driver of organizational innovation success, even though it is an essential organizational dimension (Franco & Landini, 2022). The impact of WA on organizational innovation performance has received less research attention in the manufacturing industry (Franco & Landini 2022). As a result, this study aims to view different frameworks related to agility and workforce behavior in originations. Also, to understand how organizational innovation performance in manufacturing enterprises correlates to WA. Studying the impact of WA in agile manufacturing on innovative organizational performance, it provides insights into how these enterprises' employees' capabilities may affect their agile behavior to remain competitive in the dynamic markets.

To address this gap, the paper investigates the relationship between workforce behavior toward agility as a critical component of OA to improve organizational innovation performance and sustain competitive advantage. Therefore, the paper attempts to address the below research questions:

The paper will review the literature on agility frameworks, OA, the agile behavior of the workforce, agile manufacturing, and organizational innovation performance. Then, the theoretical framework related to the topic will be explained and linked to the selected variables, followed by the hypothesis related to the research model.

# **2 Theoretical Background**

#### **2.1 Overview of Agility Frameworks, Agile Organizations, and Agile Manufacturing**