Solving Cultural Interaction Problem between China State Construction Engineering Company (CSCEC) and its non-Chinese Customers

Solving Cultural Interaction Problem between China State Construction Engineering Company (CSCEC) and its non-Chinese Customers

Student Name

School

 

Abstract 

The excerpt provides an analysis of a cloud-based cross-cultural communication rectifier. The system has been designed to solve the cross-cultural challenges that exist between CSCEC’s communication team and the UK clients and customer. 

Keywords: Artificial Intelligence, cross-culture, machine learning, robotics, chatbots, cloud-based system.

Table of Contents

Abstract 2

Introduction 2

Company background 2

Problem and justification 2

Research and analysis 3

Customer/user segment 3

Urgent requirements 4

Existing alternatives 4

Cloud-based solution 4

Solution Concept 4

The role of AI 5

Chatbots 5

User experience 5

User interface 6

Cloud-Based Architecture 7

Technical architecture 8

Technical specifications 8

Cloud business model 9

Testing and validation 9

Market partnerships 10

Revenue and resources model 11

Unique value proposition 12

Sum of all technical and business benefits 12

Key differentiating market factors 13

Alignment with customer/user segment and requirements 14

Conclusion 15

References 16

Appendices 18

Introduction

Company background

China State Construction Engineering Corporation (CSCEC) was founded in 1982. Currently, CSCEC operates as a global investment and construction group. It combines a pool of professional development and market-oriented operation. The company ventures into business management activities by use of its public company, CSCEC Limited, under the stock code 601668.SH. in its portfolio; CSCEC has seven listed companies, and about 100 secondary holding subsidiaries. The company has a huge financial resource portfolio. For instance, its revenue increases ten times every 12 years (CSCEC, 2020). This makes the company rank 23rd on the Fortune Global 500. It also emerges 44th brand Finance Global 500 2018 (CSCEC, 2020). Besides, it has been rated the highest in the global construction industry in terms of credit rating. As an innovative company, CSCEC operates in many countries across the globe. It serves millions of customers in these countries. In this regard, the company has ventured into online platforms to market itself, offer significant communication requirements such as reporting. It also serves most of its customers online, and it recently launched a cloud-based customer service model, which focuses on “making customers happy.” In the current era of internet exploration and online communications, CSCEC is on the competitive trajectory trying to reach and inform its customers across cultures. 

Problem and justification 

With CSCEC’s huge customer portfolio, it draws them from different cultural constructs. In itself, the company originates in China, whose culture is distinct from the rest of the world. In this regard, CSCEC faces cross-cultural problems when communicating with its non-Asian customers. According to Mead and Jones (2017), cross-cultural communication fails to deliver the intended purpose due to differences in cultural communication ideals. It thus distorts the message and makes it difficult to fluently relay information from one person to another. For instance, accord to Ostiguy (2017), the Chinese culture offers a high-context communication, characterised with less verbal, more non-verbal, and deliberately leaves some words unspoken because they are culturally obvious. On the other hand, the UK culture exhibits a low-context; where people leverage high verbal, less coordinated non-verbal, and do not leave words unspoken. Given that a substantial number of clients, employees and customers come from the UK, the problem of miscommunication due to cross-cultural discordance is relevant, and CSCEC has to find a solution because it distorts communication. It also affects cross-cultural interactions due to perceived prejudice, misjudgements of others and eventual cross-cultural bias. In this case, a cloud-based solution, especially in the company’s front office, to integrate cultural underpinnings is provided in this article. The solution seeks to ensure that by using a customised cloud-based architecture, CSCEC will create an integrated, versatile, and cross-culturally represented strategic customer relationship management. 

Research and analysis

Customer/user segment

Customer segmentation is important as helps organisations to improve their services based on customer needs (Sari, Sergi & Ozkan, 2020). However, instead of focusing on a forward-looking approach, Jaworski and Lurie (2020) advise marketers to use propensity-based segmentation – by examining past customer behaviours to make predictions about customers. Besides, Güçdemir and Selim (2015) propose the need to use multi-criteria decision making in clustering customer segmentation. Therefore, this part analyses CSCEC’s customers in four segments, geographical, demographical, behavioural, and psychographic (see table 1 below). 

Figure 1

Urgent requirements

From the customer segment above, it emerges that customers can be segmented culturally based on their geographical, demographic, psychographic, and behavioural characteristics. It also emerges that in the UK and China, these cultural characteristics differ, yet as CSCEC operates in the UK, the Chinese culture is already dominant in its organisational culture. Therefore, the following are urgent requirements:

• Integrate the UK and Chinese communication culture at the CSCEC customer relationship (CRM) system. This will be essential, based on Soltani et al. (2018), to imbibe the customer-oriented culture. 

• Foster-cross-cultural communication, where the company’s CRM uses a flexible approach in making employees appreciate their culture, while at the same time respecting the culture of the clients and customers.

• Provide an online-based innovation that eliminates cross-cultural barriers in communication. According to Mkrttchian et al. (2019), this has been achieved between Russia and Australia, where electronic devices are used to configure cross-cultural integration. Based on Hamutoglu and Basarmak (2020), this is possible by updating the devices with cultural underpinnings. This has been made simpler through artificial intelligence and machine learning. 

Existing alternatives

When it comes to cross-cultural integration, there are existing alternatives. Some of the notable alternatives include:

• Employees and managers of a company learning culture of the country they operate in. This approach applies to most companies operating in multicultural environments. 

• Employing people from the local culture. Such employees would feel the gap of cross-cultural differences. 

These two alternatives are viable based on the understanding that there exist cross-cultural representations. However, the same problem persists in the organisation, where there exists cross-cultural discordance among the employees from different cultures. Secondly, with human-based interventions, they are prone to bias due to cultural biases between the two cultures. 

Cloud-based solution 

Solution Concept

In a contemporary digitising business environment, Artificial Intelligence (AI) is taking shape. Through the AI, businesses re able to gain customer insights, preferences, and trends, and provide solutions towards a customer-centric marketing and product/service provision (Lee & Day, 2019). Therefore, the cloud-based concept emanates from the combination of AI and chatbots. 

The role of AI

In this context, AI will play the role of enabling machine intelligence to make them flexible rational agents in perceiving cross-cultural environment between the Chinese employees at CSCEC and clients and customers in the UK. Therefore, the following points will be the role of the AI, performed in part and integrated to facilitate the cross-cultural integration:

• Provide expert systems in CSCEC strategic SRM approach.

• Provide possibilities for natural language processing (NLP). Imran et al. (2020) carried similar research on cross-cultural polarity and emotion detection through sentiment analysis, coupled with deep learning on COVID-19 related tweets. The author realised that cross-cultural polarity and emotion detection through sentiment analysis provided different perceptions for different people from different cultures. However, through the machine learning techniques (Naïve Bayes, and Logistic Regression), the author realised that the COVI-19 communication team could find trends in the tweets that informed their criteria of sending and responding to communication across cultures. 

• Speech recognition. According to the study by Ding and Kong (2019), while employing a corpus-assisted discourse analysis and intercultural rhetoric analysis, the authors find a cross-cultural understanding between the US and China, in terms of cultural applications in communication on the internet, computer, machine, and recognition technology. 

• Robotics and automatic programing – with CSCEC enhancing its customer communication through the cloud-based platform, robotics and automatic programming will help enhance the functionality of the chatbots. 

Chatbots

From the role of the AI as noted above, the next concept area is the use of chatbots. This cloud-based solution will use keyword chatbots together with AI to automate customer service (Ghandeharioun et al., 2019). According to Zhang et al. (2020), AI and chatbots can enable language learning. Besides, they increase natural conversations and build relationships with users. Chatbots can be used for lifestyle modification programs. It is through this area that the concept develops. The chatbots, when installed on CSCEC strategic CRM, will collect significant intelligence from customers. This will inform the machine through AI about trends in cultural understandings among the concerned parties. This information will be useful in finding the cross-cultural gaps. Therefore, the company’s strategic CRM will become a customer-facing and front-office enabler of interactions. For instance, while receiving information from the Chinese employees, chatbots through emotional and cross-cultural cognitive abilities will encode the message into the UK context. 

User experience

From the solution concept identified above, the user experience relies on three approaches. Firstly, there is the need to eliminate cross-cultural eliminations between the Chinese employees at the company, and its UK-based clients and customers. In this regard, Duijst (2017) advocates for the personalisation of information and communication aspects. Through the creation of a customisable conversational interface, it will enable the users to interact with employing conversation, both verbal and written. For instance, Chinese customers will not need to speak in English to communicate with UK customers. Rather, they can still communicate in their language and the machine will translate the message contextually eliminating cultural barriers in verbal and non-verbal communication. 

Secondly, the presence of a conversational agent – the use of chatbots that interpret texts will enhance the user value of conversation personalisation, where text information can be changed into audio. This is important as it will not limit customers to one mode of communication at any one given time. In this regard, customers will not be limited by cross-cultural aspects of knowing the other’s culture to have effective communication. It will thus enhance customer experience through various ways as noted below. 

• A better understanding of the customers by the company - to facilitate the increased customer experience. For instance, a combination of AI and machine learning will gather and analyse social, historical, and behavioural data of specific customers having an account with the company. After the machines understand specific customers by storing and manipulating their historical data, will understand their levels of emotional needs, cultural underpinnings, and be able to predict the gradual change in cultural awareness. 

• Real-time decisions a predictive behaviour analysis – the solution will provide decisions based on the most recent data that is available. This offers the customer flexibility in terms of their demands and expectations. For instance, the chatbots will integrate the present language that the customer uses, relate it with the past trend of demands, then focus on providing the effective prevailing solution. 

User interface 

The user interface will be as interactive as possible. Through the use of AI for hyper-personalised interactions and the utilisation of real-time data, the focus will be on delivering real-time and customised conversation. Based on the study by Gacanin and Wagner (2019), consumers and businesses tend to embrace AI due to its ability to personalise experiences, which are much quicker and convenient compared to the traditional ways (alternatives above) (see appendix 2). In this regard, an interactive user interface will be designed to match the skills, experience, and expectations of the users based on their cultural underpinnings. The following human factors in the interface design will have empathised:

• Limited short-term memory – people can only retain up to 7 items of information at a go. In this regard, the interface will have a few icons and activities to be carried out at the same time as possible. 

• Interaction preferences – the interface will have various options for language and methods of communications. These will include aspects such as language choice, screen resolutions, communication models (audio, video, and text). 

The following image shows the user interface characteristics effective for the concept design. 

Figure 2: User interface summary

 

Cloud-Based Architecture

In the design of this product, the architectural design applies the Minimum Viable Product (MVP) architecture approach. This is as based on the diagram below:

Figure 3: MVP architecture. Source: (Lecture Notes DBC_Day_5, Slide 41)

From the MVP diagram above, the larger consideration of the design architecture is functional. This will enhance consumers’ aspects such as experience, reliability, usability, and delightfulness. 

 

Technical architecture 

A prototype of the product (Back-end)

Technical specifications 

The back-end architecture design above has three major components that define its technical specifications. The table below provides a summary of these specifications. 

 

Cloud business model

Cloud computing as an IT subject and paradigm supplies firms with resources of computing for a cut-price administration. For this business idea, cloud computing will provide several advantages. For instance, the model will base on the production of everything as a service by combining many layers like software as a service (SaaS), Platform as a Service (PaaS), and infrastructure as a service (IaaS). Apart from these, through the use of SaaS, the application of the cloud-based business model provides testing as a service (TaaS), whose applications are service-based (see appendix 1) (Husni & Saifan, 2017). 

Testing and validation 

To carry out testing and validation, Husni and Saifan (2017) six steps testing criteria is used:

Step 1: Deriving scenario from users – In this step, the process will entail defining the cases for the testing (Husni & Saifan, 2017). The authors explain the need to establish functional objectives needed by users and expected outcomes from the user perspective. The table below provides these details. 

 

Step 2: Developing test cases – the authors explain the need to define test cases depending on the requirement that the tester develops. In the context of this product, there are two requirements of testing. Firstly, the testing of the ability of the developed strategic CRM can relay messages from one language and culture to another language and culture without distorting the information. In carrying out the testing, the created platform will have a Chinese employee send information (voice or text), and have the customer from the UK receive (to identify the level of information accuracy. 95% accuracy will be considered a success. Secondly, the testing will be purely on information contextualisation. This will test whether the system can combine different capabilities of people and how culture affects them, and have the system to encode information that responds to different categories of customers. 

Step 3: Choosing a suitable cloud service vendors. According to Husni and Saifan (2017), selecting the service provider is important in ensuring that the created system functions well. Therefore, depending on the user and available infrastructure in the UK, the chosen provider is SOASTA through collaboration with CSCEC. The selected cloud testing vendor will provide a piece of additional information about system functionality. Besides, the test platform will help to adjust some features to fit the cross-cultural requirements. 

Step 4: Establishing the needed infrastructure – Husni and Saifan (2017) explains that by identifying the required infrastructure and setting up a cloud server provides the platform for testing traffic of the cloud system. In this regard, the cloud system will be embedded on the company’s website and establish an online CRM. This will be enabled by the assistance of service providers like Microsoft and Oracle. 

Step 5: Beginning the testing – To start the testing, the test cases will be used. To execute the testing, the infrastructure, capabilities, and service of the cloud will be tested to identify its execution of the intended performance and outcomes of the software. In this regard, functional testing will be carried out to make sure that the service offered is the one exactly needed in the context. For accuracy, Husni and Saifan (2017) advise to use human tester through manual means, and program tools and software as an automatic means. Some of the functional testing approaches will include:

• Unit testing – single or group unit components – such as a Chinese employee using English to communicate through the cloud and testing the cultural connotation of the same message relayed to a UK client or customer. This will allow testing a function strain or component separately from the remaining of the system. 

• Integration testing – this will entail the use of multiple units or group units as a single unit. For example, testing verbal and textual messages relayed from UK customers to Chinese employees.

• System testing – This will take testing of the system as a whole to gauge compliance, performance, accuracy and whether it meets the required specifications. 

Step 6: submitting the outcomes – The outcomes of the testing will be delivered to the user (CSCEC). According to Husni Saifan (2017), an analysis is supposed to be carried out and feedback relayed back for critical decision making about the next course of action. 

Market partnerships

Such a cloud system is not a stand-alone. In this regard, it will require market partnerships in different perspectives. Therefore, the table below provides the selected market partners and their particulars:

 

Revenue and resources model

To maximise on business revenue, especially an innovation, Yi, Yu and Zhang (2021) explain the need to change the way a business claims the value, and introduce new revenue streams. Therefore, the following are the revenue streams and identified models for CSCEC solutions.

Figure 5

 

Unique value proposition

In this section of the unique value proposition (UVP), Payne, A., Frow and Eggert (2017) explain it as a bundle of benefits that a customer expects to derive based on his/her perspective, and this summarises why the customer will turn to a given product and service of one company and not the other. In this regard, thinking about CSCEC cloud solution entails reasoning with the customers by understanding their pains. The solution thereof responds to the customers’ pains in terms of providing sustainable solutions. This is because there exists a difference between what the customers want and the solutions available in the market. The cloud solution provided for CSCEC possesses a unique selling proposition, which makes it different from what is already available in the market. For instance, most of the solutions available use open software which is prone to risks such as cybercrime and hacking. 

Sum of all technical and business benefits

The provided cloud solution for CSCEC entails both technical and business benefits. Linking these two provides a competitive advantage, which includes using technological innovation to provide a solution to socio-cultural challenges that directly impact business development. In this context, the UVP as provided here is categorised into a user experience (UX), and user interface (UI). The table below shows the sum of all the technical aspects of the solution (UX), and the corresponding user interface (UI). 

Figure 6

Key differentiating market factors

According to Porter’s generic model, a product or brand-differentiating factors enable attainment of competitive advantage. The selected key differentiating factors for this product relate with enabling customers to find multiple solutions in a pool of service provisions from this product. According to Roy (2018), the nature of disruptive and sustaining technologies provide different impacts during commercialisation. Based on this, a decision whether to commercialise disruptive technologies or to commercialise sustaining technologies lies within different market components. These components include revenue generation, product realisation, research support, and market potential. For CSCEC cloud-based solution, differentiating market factors function within the limits of product realisation and revenue generation. However, due to market sophistication and the different ways competitors will tend to position their competing products, an integrated approach to dealing with the differentiating market factors have been identified. The table below provides the available options.

Figure 7

Alignment with customer/user segment and requirements 

For this product, two user segments have been identified both from demographic and psychographic. For the demographic, the users are categorised in cultural parameters underpinned by the cross-cultural arrangements. Here, the first users are the Chinese employees are CSCEC. This product aligns with them in various ways. Firstly, it provides a solution to communicate across cultures. Instead of having to manually learn about others’ cultures, the cloud-based solution will do that for them. It will contain a machine intelligence that synchronises one cultural context into another. Secondly, it will encode their messages from the Chinese context to the UK context, thus managing to provide a conducive working environment. on the other hand, the UK clients and customers provide the second customer segment group based on culture. From this group, the product will align with their need to receive vital and accurate information from the Chinese employees.

Conclusion

This article has provided a cloud-based solution to solve the cross-cultural interaction challenge that the CSCEC Chinese employees face while communicating with UK clients and customers. This problem requires consideration because cross-cultural communication challenge distorts information, disorient interactions, and leads to increased prejudice and bias among people from different cultures. From the customer or user segment, it emerges that the Chinese employees at CSCEC and the UK clients and customers differ in terms of geographic, demographic, psychographic, and behavioural. From these differences, cross-cultural differences emerge which are ideal in dealing with communication barriers at the company’s current CRM. 

In the creation of a strategic online CRM, this article provides a cloud-based communication system should be embedded. With this system, the Chinese employees do not need to learn about the UK culture of communication to be effective in communicating. Rather, the product provides this solution by integrating machine learning, AI, and robotics to contextualise different cultural aspects. The product provides the system where one relays information in the text or verbal and it is encoded into the cultural context of choice. The product is designed to provide accuracy of 96%, which in this essence will eliminate up to 96% of cultural bias in communication. 

As a Software as a Service (SaaS) platform, it provides CSCEC with an online cross-cultural communication ability where cross-cultural limitations will be eliminated. It will make the company save money for training its employees to integrate with the UK culture especially when it comes to communication. For instance, the product will be able to change from one language to another information that one relays. It will do this while keeping the cultural contexts of the parties communicating. Besides, it will serve customer remotely, with one requiring to log in to the company website to access the service. In future, it will be developed to be downloaded from Google Play Store and Apple Store into people’s smartphones to further ease communication. All that one needs to enter is the name (ID), age, cultural affiliation occupation, level of education, and gender (optional). Eventually, the system, through Google or Microsoft AI systems, will triangulate for the user's information and communication trends, especially from social media platforms. From here, it will be able to identify their communication contexts for easy conversion of relayed messages for a customised form of communication. 

References

Akbaş, O. (2019). Culture, commodity and spectator under the influence of global industry: The case of Karaköy.

Cortellazzo, L., Bonesso, S., & Gerli, F. (2020). Entrepreneurs' behavioural competencies for internationalisation. International Journal of Entrepreneurial Behavior & Research.

CSCEC (2020). About, retrieved from: https://english.cscec.com/AboutCSCEC/Companyprofile/  [6 January 2020] 

Ding, H., & Kong, Y. (2019). Constructing Artificial Intelligence in the US and China: A Cross-Cultural, Corpus-Assisted Study. China Media Research, 15(1).

Duijst, D. (2017). Can we improve the user experience of chatbots with personalisation. Master's thesis. University of Amsterdam.

Gacanin, H., & Wagner, M. (2019). Artificial intelligence paradigm for customer experience management in next-generation networks: Challenges and perspectives. IEEE Network, 33(2), 188-194.

Ghandeharioun, A., McDuff, D., Czerwinski, M., & Rowan, K. (2019, September). Towards understanding emotional intelligence for behavior change chatbots. In 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII) (pp. 8-14). IEEE.

Güçdemir, H. and Selim, H. (2015), "Integrating multi-criteria decision making and clustering for business customer segmentation", Industrial Management & Data Systems, Vol. 115 No. 6, pp. 1022-1040. https://doi.org/10.1108/IMDS-01-2015-0027

Hamutoglu, N. B., & Basarmak, U. (2020). External and Internal Barriers in Technology Integration: A Structural Regression Analysis. Journal of Information Technology Education, 19.

Husni, H & Saifan, A.A. (2017). Cloud Testing: Steps, Tools, Challenges, retrieved from: https://www.researchgate.net/publication/316619907_Cloud_Testing_Steps_Tools_Challenges  [6 January 2021] 

Imran, A. S., Daudpota, S. M., Kastrati, Z., & Batra, R. (2020). Cross-cultural polarity and emotion detection using sentiment analysis and deep learning on COVID-19 related tweets. IEEE Access, 8, 181074-181090.

Jaworski, B.J. and Lurie, R.S. (2020), "Second Principle: Use Propensity-Based Segmentation", The Organic Growth Playbook: Activate High-Yield Behaviors to Achieve Extraordinary Results – Every Time (American Marketing Association), Emerald Publishing Limited, pp. 83-112. https://doi.org/10.1108/978-1-83982-684-920201006

Lee, J. Y., & Day, G. S. (2019). Designing customer-centric organization structures: toward the fluid marketing organization. In Handbook on Customer Centricity. Edward Elgar Publishing

Mead, R., & Jones, C. J. (2017). Cross‐Cultural Communication. The Blackwell Handbook of Cross‐Cultural Management, 283-291.

Mkrttchian, V., Veretekhina, S., Gavrilova, O., Ioffe, A., Markosyan, S., & Chernyshenko, S. V. (2019). The Cross-Cultural Analysis of Australia and Russia: Cultures, Small Businesses, and Crossing the Barriers. In Industrial and Urban Growth Policies at the Sub-National, National, and Global Levels (pp. 229-249). IGI Global.

Ostiguy, P. (2017). A socio-cultural approach. Oxford handbook of populism, 74-96

Payne, A., Frow, P., & Eggert, A. (2017). The customer value proposition: evolution, development, and application in marketing. Journal of the Academy of Marketing Science, 45(4), 467-489.

Roy, R. (2018). Role of relevant lead users of mainstream product in the emergence of disruptive innovation. Technological Forecasting and Social Change, 129, 314-322.

Sari, I.U., Sergi, D. and Ozkan, B. (2020), "Customer Segmentation Using RFM Analysis: Real Case Application on a Fuel Company", Kumari, S., Tripathy, K.K. and Kumbhar, V. (Ed.) Application of Big Data and Business Analytics, Emerald Publishing Limited, pp. 139-158. https://doi.org/10.1108/978-1-80043-884-220211009

Soltani, Z., Zareie, B., Milani, F. S., & Navimipour, N. J. (2018). The impact of the customer relationship management on the organization performance. The Journal of High Technology Management Research, 29(2), 237-246.

Sota, S., Chaudhry, H., Chamaria, A., & Chauhan, A. (2018). Customer relationship management research from 2007 to 2016: An academic literature review. Journal of Relationship Marketing, 17(4), 277-291.

Szymanski, M., Fitzsimmons, S. R., & Danis, W. M. (2019). Multicultural managers and competitive advantage: Evidence from elite football teams. International business review, 28(2), 305-315.

Torelli, C. J., Oh, H., & Stoner, J. L. (2020). Cultural equity: knowledge and outcomes aspects. International Marketing Review.

Yi, S., Yu, L., & Zhang, Z. (2021). Research on Pricing Strategy of Dual-Channel Supply Chain Based on Customer Value and Value-Added Service. Mathematics, 9(1), 11

Zhang, J., Oh, Y. J., Lange, P., Yu, Z., & Fukuoka, Y. (2020). Artificial Intelligence Chatbot Behavior Change Model for Designing Artificial Intelligence Chatbots to Promote Physical Activity and a Healthy Diet. Journal of medical Internet research, 22(9), e22845.

 

Appendices

Appendix 1: Different cloud testing tools.

Figure 8: Different cloud testing tools. Source: (Husni & Saifan, 2017)

Appendix 2:

Figure 9: Comparison between cloud-based testing and traditional testing. Source: (Husni & Saifan, 2017)