Digitalizing Gratitude and Building Trust through Technology in a Post-COVID-19 World—Report of a Case from Japan

Abstract

Employee engagement has become a critical issue in Japanese companies. One way to develop it is to improve the relationship among employees through gratitude expressions. In the post-COVID-19 remote work environment, digital devices are essential. This paper confirms that expressions of gratitude delivered via digital devices enhance the relationship between employees. We experimented in a small-town government office where participants (n = 88) were asked to (1) use the Thanks App, an app we developed to express gratitude, for two months and (2) respond to an engagement survey we developed before and after the experimental period. Through cross-analysis of the data from the app and questionnaire, we found that the “trust in colleagues” factor had a strong correlation (r = 0.80, p < 0.001) with our new index computed by the app’s data. The results suggest that the use of the Thanks App may help visualize the trust relationship among teams. This study has a practical value in providing a new team management tool for visualizing team trust. In addition, it provides a new research method for emotional and social psychology using digital devices.

1. Introduction

Since Gallup’s State of the Global Workplace Report 2017 pointed out that employee engagement in Japan is far lower than in other countries, employee engagement has been a critical issue for Japanese companies. In the recently released 2021 edition of the same report, the employee engagement score in Japan worsened by one point. As a result, Japan ranked last out of 141 countries, along with Italy and Algeria.

Last year, the COVID-19 pandemic forced Japanese employees to resort to telework. Telework made it apparent that there are challenges in building trust, employee engagement, or well-being in the work environment without face-to-face communications. Therefore, building trust, employee engagement, or well-being in online work became tangible problems.

Our research on corporate and governmental employee engagement has been ongoing in Japan since 2015. In preliminary studies, we observed two results related to employee engagement. First, per our company survey in 2015, we found that gratitude correlated with supervisor and colleague support, factors that are closely related to employee engagement. Second, in a 2017 questionnaire, we found that 43% of ordinary Japanese people hesitate to feel and express gratitude. From these results, we hypothesize that employees feel gratitude and increased engagement from increased trust and support from colleagues and supervisors.

Recently, gratitude research has studied the social function of gratitude [1,2,3,4]. So, far, gratitude research has studied gratitude traits and emotions for individuals and the relationship among two people. In contrast, social gratitude studies examine the functions and effects of gratitude in groups of three or more people.

From the Job-Demands and Resources (JD-R) model on work engagement studies, one of the factors affecting work engagement was support from supervisors and colleagues. It is the degree to which you expect your supervisor or colleagues to support you when you ask for help. In other words, it means to trust. Meanwhile, according to the “find-remind-and-bind” theory on gratitude studies, expressing gratitude establishes a positive one-on-one relationship. Therefore, one can hypothesize that expressions of gratitude increase work engagement by improving relationships with supervisors and colleagues and increasing expectations for their support.

This study targets private companies or government offices in Japan because Gallup reports that employee engagement in Japan is among the lowest globally.

In addition, we want to conduct gratitude interventions using digital devices for three reasons. First, remote work is expected to become the norm in Japanese companies in the post-COVID-19 era. It will become challenging to express gratitude verbally in a remote work environment making digital devices even more essential. Second, previous gratitude studies have rarely used digital devices for intervention. Therefore, the effectiveness of gratitude through digital devices is not known and needs to be studied. Third, Japanese people, 43% of whom are unappreciative, need more ways to express their gratitude easily. Therefore, we need apps that allow people to express gratitude easily, anytime, anywhere, through digital devices.

Figure 1 shows a conceptual model built from the above theories. We would like to replace the gratitude intervention in this model with a gratitude intervention using digital devices. The purpose of this paper is to confirm that data on the expression of gratitude using digital devices are related to the relationship with supervisors and colleagues as a preparatory step or testing the intervention effect.

In the next section, we review previous research on the psychology of gratitude and work engagement. The third section is a description of our experiment. We describe the experimental methodology and the instruments used in our experiment. In the fourth section, we show the results of our experiment. We will conduct three analyses using the survey data, the digital data on gratitude, and both data together. In the fifth section, we discuss the gratitude using digital devices and the elements of engagement. We discuss the relation between the findings of this experiment and open innovation in the sixth section. Finally, we conclude this paper in the seventh section, and we mention the limitations of this study in the final section.

2. Literature Review

Recently, the social function of gratitude has been studied. Algoe et al. confirmed that gratitude behavior affects the people who witness it [1]. Makri et al. investigated the experience of gratitude in online communities and proposed a process model of social gratitude: the gratitude cycle [2]. Masaki et al. confirmed the impact of collective gratitude on affective commitment in organizations [3]. Sciara et al. confirmed that witnessing gratitude on social networking services (SNS) only partially enhances subjective well-being [4].

The gratitude study was started by McCullough in positive psychology [5]. McCullough et al. developed a questionnaire, the Gratitude Questionnaire-6 (GQ-6), to measure gratitude characteristics in an individual [6] and confirmed, using the questionnaire, that gratitude was related to subjective well-being [7]. All other questionnaires, including the Gratitude Adjective Checklist (GAC) [6], the Gratitude Resentment and Appreciation Test (GRAT) [8], and the Appreciation Scale [9], were developed by psychologists. Recently, a workplace-specific measure of gratitude, the Gratitude at Work Scale (GAWS), was developed [10,11].

Psychologists have identified four factors that inspire gratitude [12,13,14,15]: the value of benefits, the cost of helpers, the helper’s intention, and the obligation to assist. In contrast, Kuranaga and Higuchi found that the “naturalness of the situation” suppresses the gratitude of the Japanese people in their psychological studies [16]. Psychologists also confirmed the effect of gratitude on emotion. Bartlett and DeSteno showed that a grateful person behaves with prosocial behavior [17]. Grant and Gino showed that an appreciated person also behaves with prosocial behavior [18]. Lee et al. confirmed the relationship between receiving gratitude and proactive and reactive helping at work [19]. Many other psychologists confirmed the relation between gratitude and long-term well-being [5,9,20,21,22].

In psychology, researchers studied interventions using gratitude methods. Meaningful gratitude intervention methods include gratitude list, gratitude journal, three good things, and gratitude letter. Emmons and McCullough confirmed that the participants of their experiment using the gratitude list method increased appraisal of well-being and physical condition [23]. In a gratitude letter experiment, Seligman et al. confirmed that participants decreased depression and increased well-being [24]. Alternatively, Browning et al. confirmed the difficulty of the gratitude letter intervention because participants rarely wrote gratitude letters [25]. Algoe suggested the “find-remind-and-bind” theory, which demonstrated the effect of gratitude on strengthening relationships via her intervention studies [26]. O’Connell et al. [27] confirmed that intervention using a gratitude journal that is reflective, behavioral, and interpersonal improved emotional balance. Renshaw and Hindman [28] experimented with an intervention using instant communication technology instead of a gratitude letter. Moreover, they designed an intervention called brief grateful thinking and conducted a study [29]. Heckendorf et al. [30] confirmed that an Internet- and app-based gratitude intervention is effective for anxiety and depression. Furthermore, Klibert et al. [31] confirmed the effect of gratitude on maintaining positive emotions generated by positive experiences. Alternatively, placebo effects were attributed to interventions utilizing gratitude [32], which have been reported to exert little effect on anxiety and depression as well [33]. Dickens [34] reported that the effects of gratitude interventions are overemphasized. According to a review by van Agteren et al. [35], interventions based on mindfulness-based multifactorial positive psychology are effective instead of gratitude interventions alone. In neuroscience, Zack found that oxytocin is secreted in the brain when one feels gratitude and forms trust between people [36]. Algoe stated that oxytocin research is also useful in psychology [37].

According to the “find-remind-and-bind” theory, gratitude strengthens relationships between individuals in general. It is easy to expand this theory and apply it to employees. On the other hand, organizational culture is one of the most critical elements of employee relationships. Therefore, gratitude contributes to the organization and organizational culture [38]. Many researchers indicated the importance of organization or organizational culture on open innovation. The study of Kim and Choi confirmed that the intensity of organizational change moderates the relationship between the investment in human resource development and the perception of organizational innovation [39]. Srisathan et al. [40] found that organizational culture contributes to open innovation management through the mediating role of organizational sustainability. Naqshbandi et al. [41,42] confirmed that hierarchical organizational culture is negatively related to open innovation but positively related to learning culture. Lastly, Yun et al. [43] proposed organizational entrepreneurship as a culture that controls the dynamic process of open innovation.

Work engagement is the opposite concept of burnout, defined by Schaufeli et al. [44] as “a positive, fulfilling, work-related state of mind that is characterized by vigor, dedication, and absorption.” According to the definition, work engagement comprises three dimensions: vigor, dedication, and absorption. Concurrently, Schaufeli et al. [44,45,46] developed the Utrecht Work Engagement Scale (UWES), a measurement scale for work engagement. The UWES was translated into many languages and used for international comparisons [47,48]. According to Knight et al.’s study [49], 40 interventions for work engagement were tried. Recently, Komase et al. [50,51,52] reported an association between perceived gratitude and work engagement.

The JD-R model [53] is a model of the factors and outcomes of burnout and work engagement. The JD-R model divides multiple factors into two groups: job demands and job resources, where demands are mainly stressors such as workloads and lack of time, and resources are the support factors such as learning and promotion opportunities and support from superiors and colleagues. Several JD-R model studies [53,54,55,56,57,58,59,60,61] support that job demands do not directly contribute to work engagement. Therefore, the only way to improve work engagement is to improve job resources.

Similar concepts to work engagement are job involvement and organizational commitment. Job involvement refers to “the degree to which a person is identified psychologically with his work” [62] and represents the degree to which one is absorbed in one’s work. It implies that a state of high job involvement is working at the expense of life. In addition, Mowday et al. [63] explain the definition of organizational commitment as the degree of passive loyalty and willingness to devote to the company. It means that increasing organizational commitment requires employees to sacrifice their personal lives to serve the company.

This work style may be possible temporarily but not in the long term. If this work style continues, it gradually makes the worker a workaholic and increases the possibility of burnout syndrome. Consequently, companies that enforce this work style increase the risk that workers will not perform to their full potential or will leave the company. Therefore, it is necessary to focus on engagement, the opposite concept of burnout.

However, Allen and Meyer [64] divided organizational commitment into three categories: affective commitment, continuance commitment, and normative commitment. The affective commitment represents positive feelings toward the organization and is confirmed to be related to gratitude and engagement in studies of the JD-R model [53,54,55,56,57,58,59,60,61], Saks [65], Masaki et al. [3], and more. The continuance commitment and the normative commitment represent loyalty and obedience to the organization and are not used as organizational commitment in these studies.

3. Experimental Methods and Instruments

In 2018, we experimented with a division of a company in the same setting as this study to confirm the relationship between gratitude data and factors of engagement. However, the experiment did not yield quantitative results.

There are three reasons for this. First, there were only five teams in the targeted division. To examine the relationship between good team relations and gratitude, we collected the gratitude data on a team basis. Therefore, the sample size is the number of teams, not the number of people. We did not obtain any quantitative results from our five samples. We concluded that we need at least 10 teams. Second, staff members feel guilty about using their private smartphones during work in the Japanese workplace. Therefore, the teams with strict norms rarely used our prototype app. It led us to conclude that we need to formalize its use by either having all staff members own official smartphones or lending all staff members experimental smartphones. Third, there was a low need for appreciation in the sales team. Salespeople in the target division were individually very active and rarely had the opportunity to thank their colleagues. It led us to believe that it would be better to choose an organization with mainly back-office operations.

Further, we learned that staff motivation is an issue from our interviews with several local governments. Staff members were told only to demand or blame their residents, even if they contributed to them. Therefore, the local governments wanted to motivate their staff members by having them express their appreciation to each other.

In addition, we thought that the local government offices, which have many back-office works, would be suitable for our experiment. However, many local government offices in Japan prohibit the use of staff’s own smartphones at work. Therefore, we needed to lend out many smartphones for our experiment. We set the number of participants to be less than 100 to reduce the cost of renting out the smartphones. Nevertheless, we needed more than 10 teams to obtain quantitative results.

Based on the above conditions, we searched for a small-town hall with less than 100 staff members and more than 10 teams. As a result, the town office of Miyake Town in Nara Prefecture agreed to cooperate in 2019.

3.1. Recruiting

In the government office, 98 full-time staff members worked in 15 teams. We recruited participants from these staff through the information systems department in the government office. Consequently, 88 people participated in our experiment under an agreement.

This town government office has a large number of staff members in the age range 30–50s (75% of the total office) and a small number in their 20s (about 10%). Specifically, most of the employees in their 40s and above have been with the office for more than 20 years. This fact leads us to expect little change in human relations.

In addition, there are several specialized teams such as childcare, civil engineering, education, nursing care, and insurance. There is less staff turnover in these specialized teams due to the need for specialized knowledge and experience. Therefore, we believe that relationships are closer in the specialized teams.

3.2. Equipment and Process

For our experiment, we provided the participants with our engagement questionnaire, smartphones, and the “Thanks App,” the smartphone gratitude application. The participants responded to the questionnaire before and after the experiment. In the experiment, the participants used the “Thanks App” for two months.

The details of the experimental process are as follows.

First, we obtained permission from the town mayor to conduct our experiment. Then, we posted the invitation for study participants on the town government office’s information system. After the participants were confirmed, we rented iPhones from a rental company for a fee. We installed the Thanks App on all the rented iPhones and created an account for each participant. We prepared dummy e-mail addresses required for the accounts.

We prepared explanatory materials and consent forms for the experiment. We went to each participant with these materials and asked them to sign the consent form. We handed out iPhones, with the Thanks App installed, only to those participants who signed the consent form. On the iPhone, the website for answering the engagement questionnaire was already open. We asked them to answer the questionnaire on the spot.

One month after the experiment commenced, we made an interim report to the town’s mayor through a contact person. During the two months of the trial, we did not specifically intervene with the participants. One week before the end of the experiment, we sent out a request through the contact person for the participants to fill out the engagement questionnaire again. On the day the experiment ended, we thanked each participant and collected the loaned iPhones one by one. One month later, we reported the results of our experimental analysis to the mayor through the contact person.

3.3. Questionnaire

In the experiment, we used the Organizational Engagement Questionnaire (OEQ) we developed in Japanese. The questionnaire is a 43-question survey that measures the 10 factors listed in

Table 1. The 10 factors in our questionnaire.

Category	Factor	Description
Engagement	Work fulfillment (WF)	Near work engagement
	Love for organization (LO)	Job satisfaction and organizational commitment
Colleague	Teamwork (TW)	Role clarity and job responsibilities
	Trust in colleagues (TC)	Closeness with colleagues
Supervisor	Reliable supervisor (RS)	Expectations that the supervisor will protect you from threats
	Trust in supervisor (TS)	Closeness with a supervisor
System and culture	Autonomy and feedback (AF)	A part of job characteristics
	Fairness of evaluation (FE)	Distributional and procedural justice
	Openness in organization (OO)	Psychological safety culture
Behavior	Recommendation behavior (RB)	Recommending your company to others

. The questions translated into English are shown in Appendix A. We discuss the reliability and validity of the questionnaire in Appendix B. The questionnaire used a seven-point Likert scale from 1 (strongly disagree) to 7 (strongly agree).

3.3.1. Development Process

We designed the questionnaire based on work engagement [44,45,46,47,48] and the JD-R model [53]. We collected many questions used to confirm the JD-R model in previous studies [53,54,55,56,57,58,59,60,61]. Further, we also added the questions for organizational climate (OC) for Japanese companies [66,67], team development [68], and organizational citizenship behavior (OCB) [69,70,71] because the JD-R model did not contain them. We translated all the English items to Japanese in the collected questions.

We identified 18 factors by exploratory factor analysis using the 1000 responses in Japanese that we collected through a research company. In the factor analysis, the loadings of each factor were equal to or greater than 0.5. The Cronbach’s alpha coefficients of each factor were equal to or greater than 0.8.

One of the 18 factors was work engagement. However, not all of the other factors may affect the work engagement factor. Therefore, we computed partial correlations between the average scores of 18 factors and obtained nine factors with significant correlations to the work engagement factor. Hence, we contained work engagement and the nine factors to our initial engagement questionnaire.

Finally, we shortened question items in the initial questionnaire using item-total (I-T) correlation because it contained many questions. The I-T correlation is the correlation between a score of one item and an average score of all items. We sorted the question items in descending order of the I-T correlation. We then calculated the correlation between the cumulative score of the items with the highest I-T correlation one by one and the overall score, selecting the question items with the correlation value of 0.95 or higher. We completed the final questionnaire, which is shortened to 43 items.

3.3.2. Measurement Factors

Work fulfillment (WF) is a factor related to work engagement [44,45,46]. Shimazu et al. developed a Japanese version of the Utrecht Work Engagement Survey (UWES-J) [47,48]. Because our use of UWES-J would be for commercial use, we created our own questions. However, we confirmed the conceptual validity between our items and the items of UWES-J using confirmatory factor analysis. Similar to UWES, the factor contains three sub-concepts: vigor, dedication, and absorption.

Love for organization (LO) integrated some questions from the affective organizational commitment and job satisfaction survey used in Saks’s study [65]. Saks confirmed that job engagement and organizational engagement are distinguished in his study. The factor contains four sub-concepts: meaningfulness, pride, preference, and satisfaction for the organization.

Teamwork (TW) is based on the Team Development Assessment (TDA) in a 4-D system used at the National Aeronautics and Space Administration [68]. Because there was no Japanese version of the TDA, we created our own Japanese questions. It originally comprised eight questions, but three questions remained as a result of shortening. This factor includes the sub-concepts of interest resolution, work commitment, and role clarity. Work commitment is a concept near job involvement.

Trust in colleagues (TC) picked up three questions from the rewarding coworker relations used in May’s study [72]. These questions were also not available in Japanese, so we translated them. These three questions ask about psychological connection, respect, and closeness among colleagues.

Reliable supervisors (RS) is based on the second factor, namely, confidence in superiors, of the OC scale in Japanese developed by S. Miyairi [66]. Our factor RS consisted of questions such as viewpoint variability, responsibility, policy explanation, and willingness to try new things.

Trust in supervisors (TS) comprised four questions analytically selected from the Multidimensional Leader-Member eXchange (LMX-MDM) [73]. We created Japanese translations of LMX-MDM’s questionnaire because we could not find a standard translation of LMX-MDM. LMX-MDM has four dimensions: affect, loyalty, contribution, and professional respect. However, due to the shortened questions, four questions on affect and loyalty remained in our factor.

Autonomy and feedback (AF) comprised nine questions about job characteristics [65,72,74]. We created Japanese questions because there was no standard translation of job characteristics. The nine questions include job autonomy, stakeholder expectation, feedback information of job purpose and job impact, opportunities and actions to learn about the organization’s key issues, transparency in decision making, opportunities to talk about work-related issues, and opportunities to participate in decision making.

Fairness of evaluation (FE) is based on the Organizational Justice Scale (OJS) [75]. Our questionnaire is based on a modified version of the Japanese version of the Organizational Justice Scale (OJS-J) [76]. The modification was made so that each question could be answered independently. Organizational justice (OJ) is composed of four dimensions: procedural justice, distributional justice, interpersonal justice, and information justice. However, only four questions remained in this questionnaire: two questions on distributive fairness and two questions on procedural fairness.

Openness in organization (OO) is based on Sekimoto’s [67] OC scale in Japanese. Sekimoto identified seven factors that characterize the OC of Japanese companies. However, the factor OO is composed of four questions picked up from the second factor “free and open-minded” and the fourth factor “flexibility, creativity, and originality” in Sekimoto’s seven factors. The four questions relate to the context in which true feelings, being yourself, being different from others, and straight expression are allowed.

Recommendation behavior (RB) is based on the OCB scales [69]. OCB is strongly influenced by culture, and dimensions defined in Japan and the United States vary. Therefore, there are many types of OCB scales [70,71]. However, for this factor, the organizational support behaviors in the Japanese version of the OCB scale were mainly selected. The three selected questions represent the behaviors of recommending your company, preserving image of your company, and defending your company.

3.4. Thanks App

We developed the Thanks App as a test tool, a simple smartphone application (Figure 2).

The primary function of the app is to send and receive a gratitude image with a short comment. The app has 110 images categorized into 11 kinds of positive affections, such as gratitude, gladness, and praise, and five levels of emotional strength. The user selects the recipient, chooses an image to express gratitude, writes a comment, and sends it to the recipient. The user can see the history of the images and the comments they sent and received.

The app can make closed groups to limit the number of recipients in the same group. In the experiment, we prepared a group that joined all participants. The app can also make subgroups to distinguish departments or teams. We created subgroups for each team to make it is easier to find recipients.

The app collects sent and received gratitude data the more often it is used. Gratitude data includes the sender’s ID, the receiver’s ID, an image ID, the level of emotional strength, and a comment. The gratitude data are saved in the database.

Using the gratitude data, we can show the social network structure formed by gratitude. We named this network “gratitude network.”

4. Results

A total of 88 participants joined in our experiment; 62 respondents (63% males, average age 49.1 years; 37% females, average age 40.7 years) answered the questionnaire before using the Thanks App. All participants used the Thanks App for about two months. At the end of the experiment, 26 respondents answered the original questionnaire anew. We decided not to use the second set of response data for our analysis because the number of responses was too small.

We originally planned to analyze and compare the before and after response data. Since we decided not to use the second response data, we changed the analysis plan to the following three steps. First, we aggregated the questionnaire data from each team in each factor. Second, we computed the app sent and received data from each team and formed network structures. Finally, we calculated the Pearson’s correlations between the questionnaire data and the app data.

4.1. Pre-Experiment Engagement Factors

Table 2. Average engagement scores on 10 factors for each team.

Team No.	Respondents			Average Scores of the 10 Factors in Our Questionnaire
	Male	Female	Total	WF	LO	TW	TC	RS	TS	AF	FE	OO	RB
1	3	0	3	5.243	4.832	5.565	5.333	4.607	4.582	3.966	4.590	4.758	5.173
2	3	4	7	3.745	3.824	4.237	4.677	3.943	4.003	3.969	4.431	5.004	3.752
3	1	4	5	4.843	4.300	5.259	5.609	4.720	5.755	4.539	5.352	6.052	5.147
4	4	1	5	4.240	3.800	4.330	4.339	3.923	4.104	3.801	4.302	4.601	4.182
5	5	1	6	3.210	3.995	3.995	4.511	3.467	4.171	3.857	4.043	4.376	3.727
6	4	3	7	4.904	4.645	5.857	5.867	4.893	5.539	4.623	5.006	6.143	4.813
7	3	2	5	4.489	4.499	5.073	5.135	4.524	4.706	4.518	4.501	4.854	4.747
8	4	2	6	4.241	4.502	4.674	4.775	4.139	4.668	4.002	4.419	4.794	4.542
9	2	0	2	4.395	3.878	5.000	5.011	4.300	4.887	4.501	5.127	4.631	4.448
10	3	1	4	3.768	4.374	4.092	3.907	3.801	3.626	3.949	3.688	3.934	4.131
11	2	0	2	3.605	3.750	4.002	4.337	4.197	3.880	3.834	4.000	4.122	4.000
12	2	5	7	4.837	4.535	5.052	4.724	4.228	4.823	4.335	4.397	5.251	4.638
13	1	0	1	4.270	4.000	4.000	4.000	4.200	4.253	4.004	4.247	4.253	3.629
14	1	0	1	4.952	4.998	6.000	6.000	6.000	4.506	6.000	6.000	6.000	4.727
15	1	0	1	7.000	5.750	4.997	5.631	5.994	5.253	4.887	5.000	5.488	5.629
Total	39	23	62	4.516	4.379	4.809	4.924	4.462	4.584	4.319	4.607	4.951	4.486

shows the average scores of our engagement on 10 factors for each team before the experiment.

The respondents weakly engage the organization because the total scores of all the factors are between 4.3 and 5.0. The factors with scores higher than 4.9 in total factor scores are TC and OO. The factors with scores less than 4.4 are LO and AF. These results indicate the job characteristics of small government offices. The before data shows that everyone knows each other and that opinions are freely expressed in a small office. The after data indicates that there is a lot of routine work and little feedback from participants in government work.

However, the scores of engagement factors differ from team to team. The staff in teams 3, 6, 14, and 15 are somewhat more strongly engaged because the scores of some factors are higher than 5.5. However, teams 14 and 15 only had one respondent; therefore, it should be considered only as a reference. Conversely, the staff in teams 2, 4, 5, 10, and 11 are weakly disengaged because the scores of multiple factors are less than 4.0; therefore, these scores mean negative responses.

4.2. Gratitude Count

We retrieved the Thanks App data from the database and counted the gratitude data for each team (

Table 3. Gratitude data count for each team.

Team No.	No. of Users	Gratitude Count			Gratitude Count/User
		Total	Within a Team	Between Teams	Total	Within a Team	Between Teams
1	3	366	75	291	122.0	25.0	97.0
2	8	198	74	124	24.8	9.3	15.5
3	6	629	339	290	125.8	67.8	58.0
4	5	103	25	78	20.6	5.0	15.6
5	8	258	76	182	32.3	9.5	22.8
6	6	954	303	651	159.0	50.5	108.5
7	8	375	148	227	46.9	18.5	28.4
8	6	169	36	133	28.2	6.0	22.2
9	2	23	2	21	11.5	1.0	10.5
10	8	215	32	183	26.9	4.0	22.9
11	6	273	87	186	45.5	14.5	31.0
12	11	319	141	178	29.0	12.8	16.2
13	2	27	2	25	13.5	1.0	12.5
14	7	236	67	169	33.7	9.6	24.1
15	2	209	8	201	104.5	4.0	100.5
Total	88	4354	1415	2939	49.5	16.1	33.4

).

The total sent and received was 4354 during the two-month experiment. The number of thanks between teams was higher (2939) than within teams (1415).

The team that sent the most thanks was team 6, with 954 sent, followed by team 3, with 629 sent. In team 6, the number of thanks within a team was less than that between teams. Conversely, there were more thanks within a team than between teams in team 3.

The team that sent the least gratitude was team 9, with only 23 thanks, followed by team 13, with only 27 thanks. We believe that the opportunity to send gratitude was too small because those two teams had two people.

We also calculated the gratitude count per user per team because the number of members was different for each one. However, we obtained the same results related to the teams that sent the most and least thanks. Thus, teams 3 and 6 sent thanks most often, and teams 9 and 13 barely sent thanks at all.

4.3. Gratitude Network

We drew the gratitude networks for each team based on the app’s data in Figure 3. In all the graphs, the nodes express persons and the edges express more than one thanks.

The 15 graphs in Figure 3 are classified into three different structure types: a dense network type, as in team 3; a sparse network type, as in team 4; an intermediate network type, as in team 5. To distinguish between network types, we considered the indicators of these structures.

Network density was one such indicator. Network density is the ratio of the actual number of edges to the total number of possible edges. However, network density did not provide a good representation of sparse network types. The network density for team 4 was 0.60, but the value could not determine a sparse network.

As an extension of network density, we devised a new indicator, the Triangular Network Density (TND). The following formula defines this indicator:

$${\rho }_3=\frac{{{\displaystyle \sum }}_{i>j>k}^n{d}_{ij}{d}_{jk}{d}_{ki}}{{}_n{}^{ }C{}_3}, d_{ii}=0, d_{ij}=d_{ji}, d_{ij}\in \left\{0,1\right\},$$

(1)

where n > 2 is the number of nodes; i, j, k = 1, 2, …, are the node’s numbers; d_ij is the status of an edge between the i-th node and the j-th node. The status of d_ij is equal to 1 when the i-th node connects to the j-th node. If these nodes are not connected, the status of d_ij is equal to 0. The numerator in the above formula counts the triangular structures of a given graph. The denominator is the number of combinations in the above formula that count possible triangular structures of a graph with n nodes. However, this indicator cannot be defined using the formula when n = 1 and n = 2. Therefore, we identified that the indicator is equal to 0 if n = 1 because of no edges or if two nodes do not connect in a graph with two nodes, and equal to 1 if two nodes do connect in an n = 2 graph.

Table 4. Triangular network density for each team.

Team No.	Number of Users	Triangular Network Density (TND)
1	3	1.000
2	8	0.036
3	6	1.000
4	5	0.200
5	8	0.321
6	6	0.800
7	8	0.357
8	6	0.100
9	2	0.000
10	8	0.036
11	6	0.171
12	11	0.133
13	2	0.000
14	7	0.629
15	2	1.000

shows the TND we computed for each team. Using this indicator, we can decide the network structure of team 4 as a sparse network because the indicator value is small (i.e., 0.200). If the dense network type has a density greater than 0.6, we can define the network structures of teams 1, 3, 6, and 14 as dense network types. When the sparse network type has a density of less than 0.2, we can define the network structures of teams 2, 4, 8, 9, 10, 11, and 12 as sparse network types.

4.4. Correlations between Engagement and Gratitude

To confirm the effect of team status on gratitude activity, we calculated the correlation between the survey data and the gratitude data. Specifically, we calculated the correlations between the 10 engagement factors per team and the number of thanks within the team per user. We did the same calculation between the 10 factors and the number of thanks between teams per user and between the 10 factors and the TND per team.

Table 5. Correlations between our 10 engagement factors and the gratitude data from the Thanks App.

Questionnaire (Pre-Survey)		Thanks per Users in a Team			Thanks per Users between Teams			Triangular Network Density (TND)
		r	p		r	p		r	p
Engagement	WF	0.16	0.564		0.68	0.005	**	0.70	0.003	**
	LO	0.10	0.731		0.66	0.008	**	0.69	0.005	**
Colleague	TW	0.50	0.058		0.56	0.030	*	0.68	0.005	**
	TC	0.54	0.037	*	0.64	0.010	*	0.80	0.000	***
Supervisor	RS	0.19	0.504		0.54	0.036	*	0.68	0.005	**
	TS	0.68	0.005	**	0.60	0.019	*	0.67	0.006	**
System and culture	AF	0.15	0.596		0.21	0.453		0.45	0.094
	FE	0.37	0.175		0.31	0.265		0.57	0.027	*
	OO	0.64	0.011	*	0.51	0.052		0.68	0.005	**
Behavior	RB	0.44	0.105		0.73	0.002	**	0.81	0.000	***

* p < 0.05, ** p < 0.01, *** p < 0.001.

shows the correlation between the questionnaire data before the experiment and the gratitude data obtained using the Thanks App for about two months.

The number of thanks per user within a team had a significant correlation with the TS factor. The better the relationship between the supervisor and the team members, the more gratitude exists within the team.

The number of thanks per user between teams had a high correlation with the WF and LO factors. The former represents work engagement, whereas the latter represents organizational engagement, consisting of job satisfaction and organizational commitment. This correlation indicates that teams with higher work engagement and organizational engagement are more likely to be grateful to other teams.

The TND has a significantly strong correlation with the TC factor. However, this indicator significantly correlates highly with other factors, including WF, LO, TW, RS, and TS. This indicates that the better the trust in relationships, especially with colleagues, the denser the gratitude network becomes.

Two factors, AF and FE, show limited correlation with three indicators based on the gratitude data. It implies that satisfaction with job characteristics and the evaluation system is unrelated to appreciation.

The factor OO has a significant correlation with two gratitude indicators in a team. It means that an open culture contributes to the amount and spread of appreciation within the team.

The factor RB has a significant correlation with the number of thanks per user between teams and with the TND. According to the investigation, when we developed the questionnaire, we confirmed that this factor was significantly correlated with WF. The two gratitude indicators also significantly correlated with WF. This result led us to assume that the correlation between the factor and two gratitude indicators derived from the correlation between the factor and WF.

5. Discussion

Our findings supported our goal of ascertaining the link between engagement factors and gratitude data via digital devices. Gratitude data correlates with some, but not all, of the engagement factors.

5.1. Trust in Supervisor and Gratitude Expression

Based on the find-remind-and-bind theory [26], we expect that the higher the amount of gratitude expressed among team members, the higher the TC will be. However, the amount of gratitude expressed within the team correlated with TS, not TC. The find-remind-and-bind theory was developed for one-on-one relationships, such as lovers; therefore, this does not mean that this theory is wrong. Algoe et al. suggest that another social function, such as the witness effect, could work in group appreciation, such as in teams and groups [1]. The Thanks App may have had a witness effect because it shared the experience of a colleague or team member obtaining appreciation from others. Notably, the fact that TS correlated with the amount of gratitude expressed supports the find-remind-and-bind theory because it satisfies the prerequisite of a one-to-one relationship. Because there is usually only one leader in a team, the relationship with the leader appears to be a one-to-one relationship for the members.

If team members dislike or distrust the leader, they may not be willing to express their appreciation to colleagues because they do not want the leader to see their posts (the Thanks App automatically publicly displays gratitude expressions to others). Conversely, if the members like and trust the leader, they will not mind if the leader sees them expressing their appreciation to their colleagues. In this case, the members would not hesitate to express gratitude using the Thanks App, so we can expect an increase in the amount of appreciation expressed. Thus, we believe that the score of the TS is proportional to the amount of gratitude expressed. Therefore, the trust between a leader and team members is needed to improve work engagement through appreciation using digital devices. The app will not be used by team members if such trust does not exist.

In addition, the amount of team gratitude expressed is a similar concept to that of collective gratitude in Masaki et al.’s study [3], but the relationship between the two is unclear.

5.2. Trust in Colleagues and Gratitude Expression

TC has a strong correlation with a newly defined indicator, TND, which becomes higher as the ratio of the triangular network increases. TND is higher when each member expresses gratitude to more team members. Conversely, if each team member expresses gratitude to only one other team member, the TND will remain low regardless of how much gratitude is expressed. Therefore, TND represents the extent of the relationship of expressing gratitude to each other.

If we assume that people always express their gratitude to the members they trust, we can explain the correlation between TND and TC. For example, consider a team with 11 team members, including yourself. If each member trusted only one other member, the TC questionnaire would have many “strongly disagree” responses because they do not trust 90% of the team members. Then, the TC score will be close to the minimum value of 1. In addition, because each member trusts only one person, we assume they will express gratitude only to that one person. Then, the TND is close to the minimum value of zero because the triangle network is hardly formed.

Conversely, if a team member trusts all 10 other members, we will have many “strongly agree” responses to the TC questions because they trust 100% of their team members. Therefore, the TC score will be close to the highest value of 7. In addition, because they will express their gratitude to all team members, many triangular networks will be formed, and TND will approach the maximum value of 1. Thus, we can explain that TND and TC are proportional.

Furthermore, if this assumption is valid, it implies that the TC score includes assessing how many of the team members are trustworthy.

5.3. Gratitude Network

The results of this study, “the higher the TC, the more complex is the structure of the gratitude network,” are similar to the social capital (SC) network structure [77]. The SC theory also explains that the more strongly connected and complex the social network is, the stronger the trust is within the community. However, the meaning of trust, which was strongly associated with a gratitude network structure, is different from the trust explained in SC theory.

In the SC theory, individuals who belong to a community with tightly knit relationships will behave in ways that do not go against the community’s will for fear of community sanctions. The community will then form an implicit rule, or norm, to be followed. Because most community members follow these norms, individuals who belong to the community expect other members to do the same. In SC theory, this expectation of adherence to the community norms is called trust [78].

The expectation of adherence to norms is a concept similar to TW in our OEQ factors; TC represents emotional connection, such as closeness between members. TND correlates (r = 0.68) with TW but has a stronger correlation (r = 0.80) with TC. This suggests that the gratitude network is a network of emotions such as closeness. In contrast, the network structure of SC theory can be described as a network of rationality, of “adhering to norms.”

This study is the first to capture the network structure of emotions in data.

5.4. Gratitude and Work Engagement

Another surprising result of this study is that the amount of gratitude expressed between teams correlates with WF and LO. Recently, Komase et al. [52] reported results that perceived gratitude and work engagement are related. Because our WF is similar to the concept of work engagement, if we assume the causality that “perceived gratitude results in expressed gratitude,” then our results would support the study of Komase et al. [52]. However, our results are different from their study. The results indicate that work engagement associated inter-team expressions of gratitude, not intra-team expressions.

We now turn to consider the implications of separating appreciation statements within and between teams. In this experiment, there was no replacement of team members. In addition, it has been at least six months since team formation. Therefore, we can assume that they know their team members relatively well and other team’s members know each other not so well. Under this assumption, relationships between individuals across teams are not “ongoing relationships,” which is the premise of the find-remind-and-bind theory [26]. In such relationships, not much appreciation is expressed. Nonetheless, our results show that teams with higher work engagement express more gratitude to other teams than teams with lower work engagement. It suggests that work engagement can be a factor in the development of new relationships between teams.

5.5. Gratitude Study with Digital Devices

This study is the first to provide a new research method using digital devices for gratitude study. Until now, questionnaires have been the primary measurement method in studies of gratitude characteristics [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22]. Intervention methods, such as writing on paper, such as a thank you letter or expressing thanks verbally, have been used in studies of gratitude intervention [23,24,25,26,27,28,29,30,31].

However, with the advancement of information technology, modern human relationships need to consider real and virtual spaces. For example, in Japan, dysfunctional human relationships on SNS have caused suicides and have become a national problem. Therefore, future research on appreciation needs to consider intervention methods based on real and virtual spaces.

Furthermore, with digital communication tools such as e-mail, chat, and SNS, it is difficult to pick up only gratitude data. Thus, we believe that the use of gratitude-specific applications such as the Thanks App and the collection of gratitude-specific data will be necessary for future gratitude studies.

In addition, the results of this study provide the first suggestions on how to transform digital data of gratitude into psychological information. Converting digital data into psychological information will be critical in online team management because there is less visual information and more digital data in such teams.

5.6. Practical Advantages

We believe that there are several practical advantages to using digital devices for this purpose. The first is the ability to obtain time-series data. Because apps such as the Thanks App have daily use, they accumulate data over time. It is very different from surveys, where you can only obtain a snapshot of a particular point in time. In this study, we found that when we processed the gratitude data, we were able to associate it with trust among supervisors and colleagues. Therefore, by constantly analyzing the gratitude data, it is possible to observe the changing trust among the team over time.

Second, it is easy to implement in real-world organizations. Thank-you cards are used by many companies seeking an analog gratitude intervention. However, thank-you cards, while easy to implement, take time to tally. In addition, thank-you cards do not work in organizations where each individual or team has separate workstations, as was the case during the COVID-19 pandemic. The use of digital devices solves these problems.

6. Discussion: Digitization, Trust Building, and Open Innovation

The current business environment, such as during the COVID-19 pandemic, poses high levels of uncertainty. Thus, closed innovation through in-house research and development is a high-risk option that is based on limited knowledge. Therefore, open innovation is currently becoming increasingly important because it allows companies to make choices that are adaptive to changes based on external information.

6.1. Open Innovation Dynamics

Open innovation is defined as “the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation, respectively” [79].

Schumpeter [80] defined innovation as a new combination of technology and market or product and society. As a pattern of a new combination, Schumpeter proposed five types, namely new products, new markets, new processes, new organizations, and new materials. Simply put, innovation is not an extension of an existing business but a creative destruction by a business with novelty. Entrepreneurship is key to such creative destruction.

Studies on entrepreneurship mainly focused on the traits or characters of individual entrepreneurs. However, this concept is now considered a process or dynamics [81]. The reason underlying this notion is that the success of an entrepreneurial venture, such as a startup, is related not only to the entrepreneur but also to their interactions with various groups of people, such as investors, accelerators, and early customers.

In other words, open innovation cannot be represented only by creative people and creative environments. Open innovation can be represented by the interaction of the people involved and their evolution over time, including environmental changes. This is the dynamics.

6.2. The Culture for Open Innovation Dynamics

Organizational culture influences intrapreneurship, that is, the entrepreneurship of the employees of an existing company. Organizational culture refers to the inherent values and shared assumptions of an organization [82], which are formed by the ideology of the founders and the common experience of the organization. Organizational culture influences the thoughts, feelings, and behaviors of members through shared norms. If the organizational culture constantly requires following the norm, then organizational culture will inhibit the intrapreneurship of the employees. For example, if an individual seeks to disrupt the norm, then all other members will oppose such an individual. Consequently, open innovation cannot be achieved.

Therefore, cultural entrepreneurship clearly exists within the organization, independent of individual employees. This concept is called organizational entrepreneurship. To promote open innovation, organizational entrepreneurship should be an organizational culture that encourages the pursuit of creative ideas and entrepreneurial opportunities. Frequently, organizational culture is expressed in the form of organizational structures, systems, procedures, values, philosophies, and credos. With these constructs, the organizational culture can influence the thoughts, feelings, and behaviors of employees [83].

Thus, an organizational culture of organizational entrepreneurship is necessary for open innovation. In particular, understanding the organizational culture is necessary for controlling open innovation dynamics [43]. In addition, a culture of open innovation dynamics should be one in which the thoughts, feelings, and actions of employees encourage “creative destruction.” Recently, Yun et al. [43] devised an interaction model between entrepreneurship, intrapreneurship, and organizational entrepreneurship as the culture for open innovation dynamics.

6.3. Trust for Open-Innovation Dynamics

However, a hindrance to organizational entrepreneurship is normative trust as defined by social capital theory, which is, “the expectation that members will act according to organizational norms,” because organizational entrepreneurship requires a creative disruption of organizational norms. For example, in an organization where normative trust is strong, creative destructive behavior is a violation of trust by supervisors and colleagues. Hence, the reputation of an employee who engages in such behavior will be lowered. As a result, the employee will not be able to start a business within the company due to lack of cooperation from others.

Conversely, emotional trust through intimacy is important for organizational entrepreneurship. This is because the creative destructive behavior does not violate the trust of supervisors and colleagues. Hence, employees’ evaluations will not be lowered by creative destructive behavior. Even if one employee does not follow the norm, other employees with high levels of emotional trust will become followers who facilitate creative destruction. Savolainenn and Ikonen [84] proposed a conceptual model of this complex and dynamic process of emotional trust building.

Therefore, the type of trust in organizational culture plays a decisive role in organizational entrepreneurship.

In addition, entrepreneurship is a process and is dynamic. Thus, tracking the time course of emotional trust is crucial. The digitization of gratitude enables the visibility of non-visible emotional trust as data. We believe that this fact can be useful in forming a culture of open innovation dynamics.

7. Conclusions

From many studies of the JD-R model, one factor that influences work engagement is the expectation of support from supervisors and colleagues. Meanwhile, from the find-remind-and-bind theory, it is known that gratitude strengthens the relationship. Therefore, we can hypothesize that if we can improve our relationships with our supervisors and colleagues through gratitude, we can improve our work engagement. We want to improve employee engagement by expressing gratitude via digital devices.

As a first step, in this study, we confirmed the relationship between gratitude data using digital devices, such as Thanks App, and responses to an original survey on engagement. Our results demonstrated that gratitude data was associated with the trust of supervisors and colleagues, which was a factor of engagement and a factor equivalent to work engagement.

Using these results, if we continue to use the Thanks App, we will be able to check the trust and engagement of our team without having to conduct surveys. In addition, because the gratitude data is updated from time to time, it suggests the possibility of understanding trust and engagement in real-time. Such information has significant implications for the management of online teams, which will become the norm in the post-COVID-19 era.

In addition, this study provides a new intervention method using digital devices for gratitude studies. By connecting digital data with assessment data, we provide implications for how digital devices can measure psychology. However, we cannot guarantee universality because this study is the first example of these interventions and psychometric methods. We believe that our study will provide direction for new developments in social and emotional psychology research.

8. Limitations

There are five limitations of this study.

First, this study is only one example of the relationship between appreciation and engagement questionnaires using digital devices. In this study, we chose the Miyake Town Office for several reasons. However, if the target organization is changed, the results may be different. Specifically, if we chose a private company organization instead of a government organization, the results may differ. To make the theory more universal, it will be necessary to try the same approach in many organizations.

Second, in this study, we do not know the effect of the intervention. The app-based intervention effect on the engagement factor is unknown because this study consequently included pre–post questionnaire responses and gratitude data in the analysis. It requires operational improvements to ensure that the pre- and post-surveys are properly administered.

Third, it is not always possible to obtain the same effect using other apps due to the functionality, design, and ease of use of the Thanks App in the experiment. For example, some participants may stop using an app because they do not like its usability. Thus, there is no guarantee that a study using a different application will yield similar results. An impractical solution is to design the experiment in a way that the subjects are constantly monitored.

Fourth, this study relies on the Japanese language. For example, we used “Arigato” as a word to express gratitude with the Thanks App. Whether we can obtain the same results in other languages requires an international comparison. However, we are not planning to do so.

Fifth, the study was unable to confirm the convergent validity for TW, TC, and AF of the OEQ (see Appendix B). TC is particularly important because it concerns the conclusions of this paper. Scholars suggested that it measures trust in colleagues instead of trust in people. Alternatively, a difference exists in that general trust measures trust in strangers, whereas TC measures trust in familiar people. Therefore, various forms of trust are considered different. To verify convergent validity, a Japanese scale with a closer concept of trust is required. However, we were unable to identify such a scale.