grow using artificial intelligence to screen human intelligence

TABLE OF TABLES Summary of GROW application Summary of GROW’s function Summary of GROW’s objectives and goals Summary of GROW’'s key issues Evaluation of the solutions Implementation tim

VIETNAM NATIONAL UNIVERSITY, HO CHI MINH

UNIVERSITY OF ECONOMICS AND LAW

Group: X5

01 |Hoàng Khánh Vân K214110872

02 | Đặng Bảo Uyên Nhi K214111328

03 | Nguyễn Quốc Khang K214110861

04 |Nguyễn Trương Lợi Phương |K214110867

TABLE OF CONTENTS INTRODUCTION

1 Introduction about IGS and Masahiro Fukuhara

1.1 IGS

1.2 Masahiro Fukuhara

2 Introduction about GROW

2.1 The journey of establishment - GROW’s history

2.2 Details of GROW

2.3 How GROW works

3 Summary about GROW

3.1 Overall

3.2 Summary of GROW's functions

HOW BUSINESSES USE GROW

1 Septeni

1.1 Former approach that enterprises used to recruit before the applicability of GROW 1.2 The approach that enterprises used to recruit after the applicability of GROW

2 All Nippon Airways

2.1 Former approach that enterprises used to recruit before the applicability of GROW 2.2 The approach that enterprises used to recruit after the applicability of GROW

3 Mitsubishi

3.1 Former approach that enterprises used to recruit before the applicability of GROW 3.2 The approach that enterprises used to recruit after the applicability of GROW OBJECTIVES

1 Company overview and current situation

3 Setting goals

4 Summary

IV KEY ISSUES

1 List of key issues

2 Analysis of key issues

it is likely to

have the most meaningful impact

2.2 Al can lead to some errors during the process of evaluating the competency and personalities of

candidates

2.3 The interaction between the HR department of a company and the candidates is limited

2.4 GROW can be competed by other competitors in its industry

2.5 The standards of competency and personality assessment are limited

3 Summary of key issues

V EVALUATION OF THE SOLUTIONS

5.2 Second issue - Al can lead to some errors during the process of evaluating the competency and

personalities of candidates

5.3 Third issue - The interaction between the HR department of a company and the candidates is limited

5.4 Fourth issue - GROW can be competed by other competitors in its industry

5.5 Fifth issue - The standards of competency and personality assessment are limited 5.6 Cost estimation

2.3 Conclusion

TABLE OF FIGURES Figure 1 Hiring company’s user flow

Figure 2 Candidate's user flow

Figure 3 Step-by-step workflow

Figure 4 Using GROW

Figure 5 Competency Evaluation in GROW

Figure 6 Implicit-Association Test (IAT), GROW’s Personality Assessment

Figure 7 How the Artificial Intelligence in GROW Works

Figure 8 Point chart of ANA

Figure 9 A competency - query - rubric structure in ISG

Figure 10 Trello’s tracking tool

Summary of GROW’s function

Summary of GROW’s objectives and goals

Summary of GROW’'s key issues

Evaluation of the solutions

Implementation timeline of recommendations for the first issue

Resources used for recommendations of the first issue

Implementation timeline of recommendations for the second issue

Resources used for recommendations of the second issue

Implementation timeline of recommendations for the third issue

Resources used for recommendations of the third issue

Implementation timeline of recommendations for the fourth issue

Resources used for recommendations of the fourth issue

Implementation timeline of recommendations for the fifth issue

Resources used for recommendations of the fifth issue

Cost estimation for implementing recommendations

Risk mitigation of recommendations

Contingencies for recommendations

Summary of recommendations

Assessment of recommendations

Recommendations summary of first issue

Recommendations summary of second issue

Recommendations summary of third issue

Table 24, Recommendations summary of fourth issue 42

PREFACE Competencies and personality are two distinct but related concepts that are often used in the context of assessing individuals in various domains, including education, employment, and personal development Competencies refer to the specific skills, knowledge, and abilities that a person possesses in order to perform a particular task or role effectively Personality, on the other hand, refers to the set of traits, characteristics, and behaviors that make up an individual's unique psychological structure Competency and personality assessments have become increasingly popular in both education and recruitment settings There are several reasons for this demand, such as identifying the strengths and weaknesses of candidates, making informed decisions, enhancing development, improving fit, and reducing bias in the recruiting process However, recruiting talented employees can be challenging for any organization and some common problems are the competition with other businesses and finding qualified candidates that best suit the recruiting firms’ job requirements That is why GROW was created as it is a software application that uses artificial intelligence (AI) to screen human intelligence It is designed to help organizations and businesses identify the best candidates for a job, particularly those who have the necessary cognitive abilities and problem- solving skills In this case study, objectives, key issues and recommendations are discussed and proposed to remedy and improve the current state of GROW

® Capital: JPY 391 million (as of December 31, 2022)

¢ Founder: Masahiro Fukuhara

e Established in: May, 2010

¢ Location: Head Office T 150-0022, 4F, 1-11-2 EbisuMinami, Shibuya-ku, Tokyo 150-0022, Japan 1.2 Masahiro Fukuhara

Masahiro Fukuhara is a founder and CEO of Tokyo-based people analytics startup Institution for a Global Society (IGS), which he started in 2010 Prior to founding IGS, Fukuhara was a Managing Director at asset management firm Barclays Global Investors (BGI) where he made investment decisions based on computer-driven models He has nearly two decades of working experience in data analysis and machine learning, came up with the idea of using artificial intelligence learning to build an application to help change people's behavior for the better and to assist HR in recruitment

Concerning Fukuhara’s academic level, he earned his Bachelor's degree in Economics from Keio University and MBA from INSEAD He also holds a Master's degree (with Honors) in International Finance from Grandes Ecoles HEC and a Ph.D from Tsukuba University Graduate School of Business Sciences He is currently a Project Professor at the Economics Department at Keio University, a Visiting Professor at Tokyo University of Science, and a Project Professor at Hitotsubashi University Graduate School

2 Introduction about GROW

2.1 The journey of establishment - GROW’s history

Before starting IGS, Fukuhara was a managing director at asset management firm Barclays Global Investors (BGI) where he made investment decisions based on computer-driven models

The mantra at BG] was “quantify everything,” and it instilled Fukuhara with the belief that quantitative judgment would result in greater risk-adjusted returns Following Black Rock’s acquisition of BGI, and sensing

an opportunity to pursue his curiosities for himself, Fukuhara left the firm and founded an educational venture, IGS At first, Fukuhara ran IGS as a small private cram school in a bustling part of Tokyo In early 2015, Fukuhara began holding weekly brainstorming sessions with former colleagues, friends, and researchers to flesh out a tool that can assist candidates in identifying their key strengths and weaknesses while at the same time helping human resources in hiring candidates based on required personality traits and competencies Masahiro Fukuhara proposed the idea: instead of using human “intuition,” GROW used “big data” in order to develop a scientific, objective, and constantly improving engine to recruit, screen, and develop human capital 2.2 Details of GROW

2.2.1 What is GROW

GROW is an artificial intelligence platform and mobile app developed by Tokyo-based people analytics startup IGS to recruit and analyze employees GROW is designed to help organizations screen job candidates based on their human intelligence, competencies, personality traits, and other factors The platform uses a combination of Artificial intelligence learning algorithms and natural language processing techniques to evaluate the assessment data from the evaluators and candidates and provide insights that can help organizations make more informed hiring decisions

From the 7-year milestone of establishment, GROW has taken steps to develop and successfully gain

a number of trusted customers, since then there have arisen many new problems that need to be solved such

as:

¢ Strategic focus to expand the use of GROW in more areas

e Investing in developing, improving and enhancing AI operations, making GROW a reliable replacement partner that customers can trust

2.2.2 Statistics about GROW’s business

e Business strategy:

o GROW's goal is to promote the improvement of old technologies, to innovate and create new technologies to continue to be a pioneer in the field

o Building a team of specialists to perform management and supervision work for future market expansion goals

© Setting security policies to avoid leaking customer information, especially to third parties that can cause damage to both parties

o IGS started providing GROW as a “Software as a Service” tool for interested companies by using

a B2B2C strategy and began providing GROW to HR functions in organizations

¢ Number of users and visits:

© Starting in 2015 with around 2000 users, until June 2017, GROW had 74,000 users and clients, including students at prestigious universities

© Looking at the modest figure of 2,000 participants in December 2016 and the growing number to 74,000 users in 2017, it can easily be seen that the strategic path chosen by GROW is perfectly suitable, and the team's output will aim to complete and correct outstanding shortcomings

e Key partners:

© Funding organizations and investors: IGS’s education arm,

o Marketing support: Asahi Shimbun, a prominent media company,

¢ Customer segmentation:

o Customers include Mitsubishi Corporation, All Nippon Airways (ANA), advertising company Septeni, DeNA, Rakuten, AXA and many others Even government organizations such as the Ministry of Economy, Trade & Industry of Japan (METI) and the United Arab Emirates start use GROW in a variety of different ways to manage talent recruiting, screening, hiring, placement, and development

2.2.3 How GROW collects, gathers data and applies AI in its platform:

GROW collects and gathers data in several ways, and applies AI technologies to analyze the information

e Self-Assessment: GROW uses self-assessment surveys to gather information about individuals’ goals, challenges, and progress The platform also collects data on individuals’ skills, strengths, and weaknesses

e IAT personality assessment: is an implicit association test developed to assess personality GROW copyrighted technology uses finger movement recognition to assess one's personality based on the theory

of Big Five personality traits GROW is the only assessment solution available on smartphones that

offers bias-free assessment methods previously used by international organizations Al is used to analyze and visualize the results of IAT personality assessment

* Goal Tracking: GROW allows individuals to track their progress towards their goals The platform collects data on individuals’ activities, such as the number of times they complete a task or the amount of time they spend on a particular activity

e Feedback: GROW collects feedback from individuals and teams through surveys and assessments The platform uses this feedback to help individuals and teams identify areas for improvement Candidates will receive a secured Uniform Resource Locator (URL) code with their personal assessment record, this document can be used after the assessment has ended

¢ Performance Data: GROW collects performance data from various sources, such as social media, fitness trackers, and productivity apps This data is used to provide personalized recommendations and coaching

2.3 How GROW works

2.3.1 Overview

User creates a GROW account and completed an in-app tutorial, she or he could evaluate the competencies of another user (classmate, coworker, acquaintance and so on), complete a self-evaluation, or complete the IAT IGS used its AI algorithms to analyze the resulting data, from both candidates and evaluators, in order to develop and customize HR-related services for clients and users

The primary functions of GROW are:

¢ Powering GROW: GROW uses a powerful Al engine to analyze candidate data and generate insights The platform is designed to be highly scalable and can handle large volumes of candidate data

® Competencies assessment: GROW employed a peer feedback tool to reveal 25 specific competencies which IGS had chosen based on extensive, social science research The platform uses AI learning algorithms to identify patterns in candidate responses and provide insights into their strengths and weaknesses

e Weighting: GROW assigns weights to different competencies based on their relevance to the job role This helps organizations prioritize candidates who have the skills and experience that are most important for the position

® Personality assessment: GROW employed a gamified version of the implicit association test IAT and this measures hidden bias in social psychology GROW also analyzes candidates’ personality traits,

such as emotional intelligence, adaptability, and resilience The platform uses natural language processing techniques to analyze candidate responses and provide insights into their personality traits

e Al ratings and evaluator: Finally, GROW generates Al ratings and evaluations for each candidate based on their competencies and personality traits These ratings can be used to compare candidates and make more informed hiring decisions

Candidate's user flow

e Business Process Modeling Notation (BPMN):

IGS recommends setting a group of five members for an in-house high performers assessment, the assessment course can easily be sent to members using a URL link by sending directly to the member's mailbox Candidates should access the provided URL and log in to the assessment course consisting of the IAT personality assessment, competencies self-assessment, and competencies peer assessment conducted within the group Once the high performer's data has been collected, IGS will conduct data analysis in order

to define five or six competencies considered high-performance factors within the organization This will be used in further recruitment processes The hiring company will distribute the assessment course to recruitment candidates who will then ask friends or colleagues for peer assessments Based on this data, IGS

will provide data-driven recommendations for talent acquisition that a minimum of five business days are required to prepare the assessment course

Figure 3

Step-by-step workflow

© GROW’s data sources and analysis: Figure 4

Using GROW

Start the GROW app, users can create a GROW account and complete an in-app tutorial which would then allow him or her to evaluate the competencies of other users whether that be a classmate, a co-worker,

an acquaintance, whoever it may be complete a self-evaluation as well as complete an IAT

Figure 5

Competency Evaluation in GROW

The competency evaluation within the GROW app, the first list is a competency chart selected from hundreds of competencies and skills reported in the social science literature based on several criteria, a correlation to relevant job functions is being accessible by peers In the second chart, a request can be chosen to know the form of "gifts" - the four-point scale to evaluate users makes the evaluation easier Figure 6

Implicit-Association Test (IAT), GROW’ Personality Assessment

An implicit association test is a personality assessment The IAT test is connected to personality traits, which is used to review attributes and biases people have users progress through a series of implicit association tasks These are measured based on where users drag certain attributes to the correct attributes as well as the user swiped the movable attribute to predict personality traits This allowed GROW to use its machine learning to reveal anomalies and patterns of swiping behavior and better predict personality Figure 7

How the Artificial Intelligence in GROW Works

In artificial intelligence within the GROW app, the users must receive an average of four to five evaluators In addition, in the self-evaluation in order for the AI algorithm to use the data in several manners, AI uses many data points to determine the likelihood and evaluation was genuine and valid

3 Summary about GROW

3.1 Overall

Table 1

Summary of GROW application

General information

Mobile application JOS & Android App) Funding organizations and investors: IGS’s education arm,

Marketing support: Asahi Shimbun, a prominent media company Universities

Clients: All Nippon Airways (ANA), Septeni, DeNA, Rekuten, AXA Government entities: Japan’s Ministry of Economy, Trade & Industry (METI), the United Arab Emirates

HR-related for clients and users Gamified personality assessment Feedback peer assessment system Candidate’s assessment Identifying potential talent among candidates Providing performer analysis

Unique Selling Proposition Application that can identify potential talent and top talent among candidates

as well as complete an IAT

Start the GROW app, users can create a GROW account and complete an in-app tutorial

GROW employed a peer feedback Assess candidate’s

- the four-point scale to evaluate

users

GROW assigns weights to different competencies Users received ratings from multiple evaluators

GROW employed a gamified version of the implicit association

test IAT

GROW generates for each candidate based on their competencies and personality traits

proficiency in these competencies through self-

assessment

Helps organizations prioritize candidates who have the skills and experience that are most important for the position Hidden bias in social psychology

Compare candidates and make more informed hiring decisions

Septeni is a Japanese digital marketing company that provides a range of services related to online advertising, including search engine marketing, display advertising, social media marketing, and mobile advertising The company was founded in 1997 and is headquartered in Tokyo, Japan Septeni has been recognized for its innovative approach to digital marketing, and has won numerous awards for its work in the field 1.1 Former approach that enterprises used to recruit before the applicability of GROW

Employees are invited to their company's Tokyo office for multiple rounds of interviews, during which time groups of applicants are observed working together to complete Septeni and other typical team-based job tasks Employees have only one chance in a group interview before the company makes a hiring decision

® Weaknesses and challenges of the approach:

o Causing the waste of money and time because employees have to go directly to Septeni for interviews

o Employees are not willing to invest time and money to join because Septeni is just a mid-sized company

1.2 The approach that enterprises used to recruit after the applicability of GROW

GROW collects evaluations about candidates during their study and work at the university from other students and people around the candidate through a peer-feedback mechanism

GROW also implements an AI algorithm based on data on information and interview results about employees from previous years, thereby predicting and finding suitable employees to pass Septeni's interview round

The result of using Grow:

® Lower the whole business processing process by 90% while maintaining candidate quality

¢ When compared to the past, the proportion of applicants that accept employment offers has grown by 4 times

® Two times as many qualified candidates are coming from outside of Tokyo

¢ Radically boosting Septeni's brand recognition among potential customers and students

2 All Nippon Airways

All Nippon Airways (ANA) is a Japanese airline headquartered in Tokyo, Japan ANA was founded in 1952 and has grown to become a major player in the global airline industry It is the largest airline in Japan by passenger numbers and revenue ANA operates a wide range of domestic and international flights to destinations throughout Asia, Europe, North America, and Oceania In addition to its passenger services, ANA also operates a number of subsidiaries, including ANA Cargo, ANA Wings, and Peach Aviation, a low-cost carrier

2.1 Former approach that enterprises used to recruit before the applicability of GROW

ANA selects candidates by hand from the numerous applications they get to create the upcoming senior leadership team

e Weaknesses and challenges of the approach:

o Owing to the small number of workers, ANA may overlook many qualified applicants, and the traditional hiring procedure may disqualify people who have the potential to grow into effective leaders

in the future

2.2 The approach that enterprises used to recruit after the applicability of GROW

IGS and ANA collaborated to establish 10 competencies they look for in new hires The GROW app is then used by students to evaluate their skills and personality qualities, which results in a "Total Score”

GROW's AI engine creates a "confidence score" based on all information the IGS gathers about the student and their assessors to determine how accurate the IGS is in the outcomes ANA then shows the evaluation results (point chart) for each candidate

Figure 8

Point chart of ANA

(The candidate's place in the screening process is shown by the "Total Score" on the x-axis, the

"Confidence Score" on the y-axis, and the color of the dot

Candidates with a "Total Score" of less than -5 and a "Confidence Score" of less than -4 are not likely to

be Finalist green dot, therefore ANA may utilize GROW to screen out those who will not advance to the final interview stage.)

® The result of using GROW:

o Enhancing capacity to identify brilliant applicants for the recruiting process with high accuracy

o Enhancing capacity to choose pupils, creating, and fine-tuning their evaluation standards

o Targeting more precisely the group of students with high potential in the recruitment process

3 Mitsubishi

Mitsubishi is a Japanese multinational conglomerate with a wide range of businesses, including automotive manufacturing, electronics, finance, and more The company was founded in 1870 and is headquartered in Tokyo, Japan Mitsubishi has a long history of innovation and has contributed to the development of various industries around the world Apart from the automotive industry, Mitsubishi is also involved in a variety of other businesses such as electronics, heavy industry, and finance The company produces a wide range of products such as air conditioners, power generation systems and industrial machinery

3.1 Former approach that enterprises used to recruit before the applicability of GROW

At first, Mitsubishi used the conventional method for hiring The need for top-notch human resources grew as Mitsubishi sought to enhance its business strategy However, the business had several challenges in recruiting excellent candidates and effective management teams

© Weaknesses and challenges of the approach: Mitsubishi had difficulty in recruiting a large number of high- quality candidates when their demand for employees increased during the development of business 3.2 The approach that enterprises used to recruit after the applicability of GROW

IGS and Mitsubishi collaborated to develop an algorithmic model of the ideal Mitsubishi worker Based

on the premise that individuals who were the excellent candidate's age might also be the best match for the organization, IGS located persons who were peers of the ideal people and who had five or more abilities in common with those of the company’s ideal candidates Thereby, Mitsubishi could find excellent candidates

e The result of using GROW:

o Finding out the ideal candidates who have not filled out the company’s recruitment form and inviting them to a meeting to encourage them to join your company

o Reaching out to new talented candidates who do not have much information about Mitsubishi

Il OBJECTIVES

1 Company overview and current situation

As of June 2017, GROW had 74,000 users, including students at both prestigious and lesser-known universities Clients included Mitsubishi Corporation, All Nippon Airways (ANA), Septeni, DeNA, Rakuten, AXA, and many others Even government entities like Japan’s Ministry of Economy, Trade, & Industry (METI) and the United Arab Emirates were getting involved

2 Objectives

Helping businesses assess the ability and personality of candidates: Big data will help meet this expanding need by providing pre-hiring assessment and sourcing tools for the requirement Also, incorporating assessment tools helps lower the number of applicants for a given position and raises the general caliber of the hired individuals

Making recruitment fair: By using modern AI technology, errors resulting from personal bias are unavoidable because GROW uses peer feedback to analyze candidate data during the process However, misjudging a candidate's qualifications will result in ineffective hiring So, limiting bias in the evaluation process helps ensure the quality of human resources and makes recruitment fair

Creating a premise for future developments: By demonstrating its technological capabilities, GROW gains the confidence of Japanese businesses and gains momentum toward becoming a useful tool for hiring As a result, numerous contracts were made with Japanese businesses, helping GROW increase its user base

Toward the desire to help students develop: Identifying the strengths and weaknesses of individuals helps each individual take appropriate action to make changes to promote strengths and overcome weaknesses, which improves the hiring and human resource development process

3 Setting goals

Regarding helping businesses assess the ability and personality of candidates, GROW must gain efforts to shorten recruitment time for client businesses to 40% compared to before, helping businesses save at least 80% of recruitment costs

To make recruitment fair, GROW aims to develop high-precision data collection and analysis technology, increasing the accuracy of recruitment to 98%

According to the objective of creating a premise for future developments, GROW needs to serve more than 40% of Japanese university graduates every year

To help students develop, in 2023 GROW must reach 300,000 users, with a growth rate of at least 10% per year

4 Summary

Table 3

Summary of GROW 's objectives and goals

Current

situations

(data)

Goals

Helping businesses assess

the ability and personality

of candidates

- Shortening recruitment

time for client businesses

Making recruitment fair

precision data collection

Creating a premise for future developments

Serving more than 40%

of Japanese university

Toward the desire to help students develop

As of June 2017, GROW had 74,000 users, including students

at both prestigious and lesser-known universities

In 2023 GROW must

reach 300,000 users,

to 40% compared to and analysis technology, graduates every year with a growth rate of at

- Helping businesses save of recruitment to 98%

at least 80% of

recruitment costs

IV KEY ISSUES

1 List of key issues

The conflict between expanding the use of GROW or only focusing on the areas where it is likely to have the most meaningful impact

AI can lead to some errors during the process of evaluating the competency and personalities of candidates The interaction between the human resources department of a company and the candidates is limited GROW can be competed by other competitors in its industry

The standards of competency and personality assessment are limited

Analysis of key issues

2.1 The conflict between expanding the use of GROW or only focusing on the areas where it is likely to have the most meaningful impact

As can be seen from the paper, within the thorough research of focus groups with student users and countless meetings with managers at a wide range of enterprises to identify the implementation methods, GROW has been developed as a gamified mobile consumer app applied the IAT that can result in high rates of accuracy in competency and personality evaluations of each candidate As a result, the high level of efficiency and effectiveness of GROW has led the business to increase, reaching 74,000 users in June 2017 This rapid growth signals a great opportunity for IGS and the app to continuously develop its strength in AI applications

However, this advancement can also pose a great threat for the platform In particular, the growing number of users and data has encouraged the company to expand the use of GROW in a wider scope, but this decision may create a potentially misleading effect that the AI learning algorithms cannot be used to the areas that they

do not receive enough training and input data Specifically, due to a large amount of customer input data in the newly expanded sectors, any redundancy in the data may cause failures in learning, thus the AI algorithms may show unpredictable results

Moreover, the company has to remember to update the data and information systems frequently if they want to expand the use of GROW on a large scale If not doing so, due to the lack of improvement, the inconsistency in the results may happen when the AI machine learning cannot have the ability to process bits of information

Besides, if the AI machine learning does not have enough data about the novel areas, then the AI application can generate inaccurate results and cause great losses This is also a matter that can affect GROW’s current working efficiency

As a result, the conflict between the GROW expansion into new areas or only concentrated on the sectors where it possibly has the most powerful effects becomes a major problem for the development of GROW

2.2 AI can lead to some errors during the process of evaluating the competency and personalities of candidates

The functions of evaluation about the competency and personalities of candidates become core values that GROW brings to its company customers and helps its platform achieve the remarkable milestones in the number of users However, while GROW uses AI to reduce diagnostic errors that the traditional interview process often suffers from, there is the risk that the use of AI can introduce new potential errors during the process of evaluating the competency and personalities of candidates Specificially, candidates who complete the test on the GROW platform can recceive inaccurate results about their personalitites and competency, as the test does not reflect the actual evaluation This is the negative result when the GROW’s AI systems cannot flexibly adapt to the differences between the real-world situations, the proper data and the situations used in training the intelligence The major reason is because AI systems are not as equipped as humans to recognize when there is a relevant change in context or data that can impact the validity of learned predictive

assumptions Therefore, Al systems may unknowingly apply programmed methodology for assessment inappropriately, resulting in errors

2.3 The interaction between the HR department of a company and the candidates is limited

Thanks to the advancement of GROW and its benefits, companies and organizations can save a large amount of time and money recruiting candidates that best fit their demands and their job requirements Moreover, this type of recruitment can help students opt out of paying expensive travel costs for the interview that are far away from their living locations However, this kind of recruitment still reveals some challenges, typically the interaction between the HR department of a company and the candidates is reduced Particularly,

by using the GROW's predictions on evaluating candidates, the company eliminates the traditional face-to-face interview, thus the HR department does not need to create an offline interview with their candidates This leads

to the elimination of the face-to-face interview significantly and hence reduces the interaction between the company and the candidates The cohesion and understanding between the company and the candidate can therefore disappear because the company does not understand the personality of the candidates by person, but only through the data that GROW collects and the AI learning machine generates

2.4 GROW can be competed by other competitors in its industry

Throughout the research from the case study, it can easily be seen that GROW’s business network only concentrates on targeting three primary markets: Japan, France and The United Arab Emirates Its primary funding company that supports for its marketing activities is Asahi Shimbun, one of the most popular daily newspapers in Japan This enterprise primarily enhances GROW’s marketing strategies in Japan As a result, GROW has not focused a lot on marketing activities on a larger scale outside Japan yet And this leads to the low level of GROW’s brand identity GROW can also be affected by the competition from numerous fierce competitors These companies with huge budgets can enhance their marketing activities to its large targeting markets Also, a wide variety of enterprises can use the same technique of GROW to develop their platforms: using AI technology to generate the results of evaluation with high rates of accuracy In general, while GROW's primary targeting market is small, many competitors with a wide scale of coverage all over the world can create

a strong competition to GROW in its industry

2.5 The standards of competency and personality assessment are limited

According to the company, based on scientific research, GROW supports the enhancement of personality evaluation for the enterprise customers by using the concept of the Big Five personality traits This type of

personality assessment model can help the recruiting firms easily find out special characteristics of each potential candidate However, the use of the Big Five personality traits can cause a wide variety of limitations First, it may screen out qualified candidates For many jobs, there is not a mainstream personality that fits the job type Such tests may also exclude talented candidates who think outside the box Secondly, this model of evaluation can cause flawed results Candidates may respond based on what they think the employer wants rather than on their true personalities; therefore, results are not always accurate Moreover, the purpose of the test may not fit into the recruiting firms’ hiring process In particular, there is an increase in the number of personality requirements for a position in a company, while the model of personality evaluation only assesses candidates based on the consistency of behaviors in certain situations Besides, GROW only provides 25 behavioral characteristics when assessing the competency of candidates As a result, the standards of competency and personality assessment are still restricted that need to be updated

3 Summary of key issues

Table 4

Summary of GROW’ key issues