Introductory Chapter: Customer Satisfaction Alignment with "On the Edge" IT Tools

*Danil Dintsis*

#### **1. Introduction**

This book is about the new role of IT in managing customer relationships. CRM (or customer relationship management) systems are well-known and widely implemented for a long time. On the edge IT technologies provide an opportunity for a breakthrough in customer relationship management and improve the role of IT in the process. In this book the respected authors represent their experience and provide meta-analysis of the IT technologies implementation in the subject area and the results gained.

In the current chapter, the author, who is the academic editor of the book, presents his vision and approach of the role of IT and especially on the edge technologies for customer relationship management.

The growth of the on the edge technologies such as big data [1], machine learning, and artificial intelligence [2] provides new opportunities for customer relationship management. In this chapter, the author makes a general overview of those new opportunities and tools.

I.Implementing artificial neural networks (ANN)

The ANN tools provide support for the vast area of business tasks in CRM area.

	- a. Define probable customer journey (on the example of buying a ticket)
	- b. Define customer lifecycle in a company

III.Fuzzy logic models

• The first hidden layer includes artificial neurons for:

*Introductory Chapter: Customer Satisfaction Alignment with "On the Edge" IT Tools*

example, artificial neurons for the following attributes:

The second hidden layer proceeds the loudness parameter and contains, for

• The third layer proceeds the timbre parameters, and contains the following:

The output layer contains the artificial neurons, which indicate user's satisfaction:

The training algorithm is based on backpropagation analysis with elements of

As the result, a contact center agent can receive an advice about customer emotions in a real-time visual mode. This helps an agent to correct his/her talk with

The fourth layer proceeds speech tempo parameters, such as:

The simplified example of the ANN is shown in **Figure 1**.

convolutional artificial neural network [3, 4].

a customer and gain customer satisfaction.

• Basic voice timbre

• Gender (can be empty)

• Starting surrounding noise level

*DOI: http://dx.doi.org/10.5772/intechopen.91200*

• Level of surrounding noise loudness

• Level of high-frequency loudness

• Level of total voice loudness

• Differences in timbre

• Trend in speech tempo

• Faster

• Slower

• The same

• Neutral

• Better

• Worse

• Excited

**5**

• Dissatisfied

• Trend in timbre differences

• Age (can be empty)

#### **2. Applied research and analysis methods**

#### **3. Implementing of ANN tools**

In this section I deliver our team experience of applying emotion recognition of a customer for call (contact) center systems and leisure services (karaoke and similar). Well-known fact is that customer satisfaction is one of the most important indicators for business. Net present score (NPS) is often used as such indicator based on customer satisfaction surveys. But NPS is a kind of the lagging (or postmortem) indicator. An organization can implement also the so-called leading (or proactive) indicators. For example, contact centers and some other services can use speech emotion recognition services and manage customer satisfaction in a real-time mode.

Detecting customer reaction by emotion recognition is a task, which can be solved using both supervised and unsupervised ANN learning [2, 3]. In this chapter, the author represents the example of the emotion recognition based on voice speed and timbre analysis without speech recognition.

In this model we do not recognize speech, but emotions only. The recognition technique is based on the analysis of the so-called static and dynamic parameters. Static parameters are basic voice timbre: gender (if known). Dynamic parameters are the following: voice timbre change, speech speed and loudness, and surrounding noise level.

Static parameters are basic for a certain caller (or user). They help to adjust the dynamic parameters during the contact session. The ANN contains input layer, which consists of:

Input audio are separated into eight components. Each component is equivalent to a certain frequency in the voice audio range: starting from 300 Hz and to 16 kHz. *Introductory Chapter: Customer Satisfaction Alignment with "On the Edge" IT Tools DOI: http://dx.doi.org/10.5772/intechopen.91200*

III.Fuzzy logic models

c. Estimate demand

*Customer Relationship Management and IT*

a. Estimate customer satisfaction

f. Develop a product/service/offer

**2. Applied research and analysis methods**

c. Analysis of request forms and queries.

and timbre analysis without speech recognition.

noise level.

**4**

which consists of:

• Gender (if known)

• Age (if known)

**3. Implementing of ANN tools**

a. Author analyses his own projects results.

b. Meta-analysis of existing publications and articles.

d. Estimate internal capabilities and capacity

b. Estimate customer demand and develop new products/services/offers

e. Estimate external (market/technology) capabilities and capacity

In this section I deliver our team experience of applying emotion recognition of a customer for call (contact) center systems and leisure services (karaoke and similar). Well-known fact is that customer satisfaction is one of the most important indicators for business. Net present score (NPS) is often used as such indicator based on customer satisfaction surveys. But NPS is a kind of the lagging (or postmortem) indicator. An organization can implement also the so-called leading (or proactive) indicators. For example, contact centers and some other services can use speech emotion recognition services and manage customer satisfaction in a real-time mode. Detecting customer reaction by emotion recognition is a task, which can be solved using both supervised and unsupervised ANN learning [2, 3]. In this chapter, the author represents the example of the emotion recognition based on voice speed

In this model we do not recognize speech, but emotions only. The recognition technique is based on the analysis of the so-called static and dynamic parameters. Static parameters are basic voice timbre: gender (if known). Dynamic parameters are the following: voice timbre change, speech speed and loudness, and surrounding

Static parameters are basic for a certain caller (or user). They help to adjust the dynamic parameters during the contact session. The ANN contains input layer,

Input audio are separated into eight components. Each component is equivalent to a certain frequency in the voice audio range: starting from 300 Hz and to 16 kHz.

The second hidden layer proceeds the loudness parameter and contains, for example, artificial neurons for the following attributes:

The fourth layer proceeds speech tempo parameters, such as:

The output layer contains the artificial neurons, which indicate user's satisfaction:

The simplified example of the ANN is shown in **Figure 1**.

The training algorithm is based on backpropagation analysis with elements of convolutional artificial neural network [3, 4].

As the result, a contact center agent can receive an advice about customer emotions in a real-time visual mode. This helps an agent to correct his/her talk with a customer and gain customer satisfaction.

*ANN Schema.*

### **4. Stochastic graph models for customer lifecycle and behavior prediction**

In this section I deliver the stochastic graph model [5], which controls a user ticket buyer—behavior at the ticket-selling Website. The goal of the ticket selling organization is to maximize revenues with minimal expenditures and simultaneously serve as many clients as possible. The ticket system should be able to predict customer behavior at the Website, offer special prices in a dynamic mode, and change number of offered places (e.g., additional carriers in a train or additional flights).

2. If V2 path is selected, the system makes estimate of the demand based on statistical data. Certain dynamically based tariff is generated for a user

3. If the user authorizes (V3 node), preferred routes and destinations can be

a. Select a route and destination from a preferred list (V6 node).

a. Select the route, time, and carrier (move to V4 node). Note: each of the mentioned parameters implies on ticket cost. The ticket seller can make

ii. Increase/decrease costs for certain routes/carrier types/time

offered. Let us examine the probabilities at the next step

*Introductory Chapter: Customer Satisfaction Alignment with "On the Edge" IT Tools*

*DOI: http://dx.doi.org/10.5772/intechopen.91200*

changes dynamically, for example:

slots.

5.From the V3 node the user can:

c. Authorize (move to V3 node).

d. Leave the page (move to V1 node).

i. Add or remove carriers.

b. Move to a new selection page (V5 node).

b. Move to a new selection page (V5 node).

c. Leave to the start page (V1 node).

4.From V2 node the user can:

**Figure 4.**

**7**

**Figure 3.** *Stochastic loop.*

*Stochastic routes at step 1.*

The stochastic graph tools [6] are worth to solve this class of tasks, because user's behavior at a Website is probabilistic at each step. Below we discuss step by step the model of a ticket-buying process.

The stochastic graph nodes indicate current state of a system, and edge reflects an action which leads to a new state. Each edge is weighted with a probability of a certain action—p(ij)—where i is a starting node and j is a target node (**Figure 2**).

Usually a node will have a loop, which indicates that a user stays at a current state or leaves the system with a certain probability p(i,i) (**Figure 3**).

1.The first node is "Start page open" V(1).

There are at least three probable next steps by a user (**Figure 4**):

*Introductory Chapter: Customer Satisfaction Alignment with "On the Edge" IT Tools DOI: http://dx.doi.org/10.5772/intechopen.91200*

**Figure 4.** *Stochastic routes at step 1.*

**4. Stochastic graph models for customer lifecycle and behavior**

In this section I deliver the stochastic graph model [5], which controls a user ticket buyer—behavior at the ticket-selling Website. The goal of the ticket selling organization is to maximize revenues with minimal expenditures and simultaneously serve as many clients as possible. The ticket system should be able to predict customer behavior at the Website, offer special prices in a dynamic mode, and change number of offered places (e.g., additional carriers in a train or additional flights). The stochastic graph tools [6] are worth to solve this class of tasks, because user's behavior at a Website is probabilistic at each step. Below we discuss step by step the

The stochastic graph nodes indicate current state of a system, and edge reflects an action which leads to a new state. Each edge is weighted with a probability of a certain action—p(ij)—where i is a starting node and j is a target node (**Figure 2**). Usually a node will have a loop, which indicates that a user stays at a current

state or leaves the system with a certain probability p(i,i) (**Figure 3**).

There are at least three probable next steps by a user (**Figure 4**):

1.The first node is "Start page open" V(1).

b. Select a route destination (V3).

a. Authorize in the ticket system (V2).

c. Leave the ticket system (loop) (V1).

**prediction**

**Figure 1.** *ANN Schema.*

**Figure 2.**

**6**

*Simple stochastic graph.*

model of a ticket-buying process.

*Customer Relationship Management and IT*

	- a. Select the route, time, and carrier (move to V4 node). Note: each of the mentioned parameters implies on ticket cost. The ticket seller can make changes dynamically, for example:
		- i. Add or remove carriers.
		- ii. Increase/decrease costs for certain routes/carrier types/time slots.
	- b. Move to a new selection page (V5 node).
	- c. Authorize (move to V3 node).
	- d. Leave the page (move to V1 node).

#### 5.From the V3 node the user can:

• Price (mark this attribute with parameter named P)

*Introductory Chapter: Customer Satisfaction Alignment with "On the Edge" IT Tools*

chronous learning. **Table 1** is the excerpt from the decision matrix.

tools in CRM systems in different countries and businesses.

All attributes are represented as fuzzy linguistic variables in L. Zade terms [0;1] with step 0.2, which is like a common 5 grade model (1–5 grades). Based on those weights (and a lot of others as well), the learning organization developed a series of learning methods combining class-based, online, blended, synchronous, and asyn-

In this chapter, the general overview of this approach is given, including new data from the latest projects. The "big data" tools such as ANN, graph models, fuzzy sets, and others can help organizations in building strong customer relationship, based on real-time knowledge about customer demand, happiness, and trends. In this book, the respected reader learns about concrete implementations of IT

**Learning method/attributes Class traditional Webinar Blended Self-paced Micro-learning** Price 0.2 0.4 0.2 0.6 1 Mobility 0.2 0.8 0.6 0.6 0.8 Personal approach 0.6 0.2 0.8 0.4 0 Actual content 0.8 0.6 0.4 0.4 0.6

1 RU Training center, Global Business School, Moscow, Russian Federation

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

• Personalized approach (PA)

*DOI: http://dx.doi.org/10.5772/intechopen.91200*

• Mobility (MB)

**Author details**

*Awareness level of fuzzy model attributes.*

**Table 1.**

Danil Dintsis1,2

**9**

2 Global Business School, Barcelona, Spain

provided the original work is properly cited.

\*Address all correspondence to<EMAIL_ADDRESS>
• Actual content (AC)

**Figure 5.** *Stochastic routes at the end of customer journey.*

Next, I show the main path on this graph, at which a user moves to buy a ticket procedure.

	- a. Buy ticket—move to V(7) node.
	- b. Return to the selection page (V5 node).

As a result, the stochastic graph model can adjust the ticket system behavior according to the user's behavior based not only on previous statistical data but on the probability of his/her behavior in the current session. Those algorithms can improve both customer satisfaction and operator's value.

#### **5. Role of fuzzy logic in defining and balancing customer demand while creating a new product or a service**

Fuzzy (or linguistic) models usually implement subject matter (or common) terms, for example, "good," "cheap," "far," etc. Fuzzy models, as described by Lotfi Zade and other researchers [7–9], provide formal models based on linguistic terms by simultaneous inclusion of an element into several sets. The author of the article provided a research [10], in which he shows the extended definition area of fuzzy element weight from interval [0; 1] to[ ∞; ∞]. The extended definition area provides the opportunity to apply fuzzy logic together with scoring models.

Let us consider a task of developing a new customer offer (either product, goods, or service) based on customer feedback analysis, company capabilities, technological opportunities, and other factors. The author previously published his experience in applying fuzzy logic to develop new service offers for lifelong learning business [10].

Learning organization efficiency on the other hand depends on student's satisfaction and their willingness for continuous learning and capabilities to deliver learning services cheap and adapt them to changes in customer demand.

Based on customers'surveys, and their analysis, the author defined students' professional improvement and satisfaction as one of the critical success factors. The main satisfaction attributes are:

*Introductory Chapter: Customer Satisfaction Alignment with "On the Edge" IT Tools DOI: http://dx.doi.org/10.5772/intechopen.91200*

All attributes are represented as fuzzy linguistic variables in L. Zade terms [0;1] with step 0.2, which is like a common 5 grade model (1–5 grades). Based on those weights (and a lot of others as well), the learning organization developed a series of learning methods combining class-based, online, blended, synchronous, and asynchronous learning. **Table 1** is the excerpt from the decision matrix.

In this chapter, the general overview of this approach is given, including new data from the latest projects. The "big data" tools such as ANN, graph models, fuzzy sets, and others can help organizations in building strong customer relationship, based on real-time knowledge about customer demand, happiness, and trends.

In this book, the respected reader learns about concrete implementations of IT tools in CRM systems in different countries and businesses.

#### **Table 1.**

Next, I show the main path on this graph, at which a user moves to buy a ticket

6.From V4 node a user can select a place (**Figure 5**). Note: each place has attribute of cost (e.g., more convenient according to general statistical information and more preferred by this certain user). The ticket system can have varying costs in order to gain more revenue from this certain order or by

filling the carrier. In the first case the price increases and in the second

As a result, the stochastic graph model can adjust the ticket system behavior according to the user's behavior based not only on previous statistical data but on the probability of his/her behavior in the current session. Those algorithms can

**5. Role of fuzzy logic in defining and balancing customer demand while**

Fuzzy (or linguistic) models usually implement subject matter (or common) terms, for example, "good," "cheap," "far," etc. Fuzzy models, as described by Lotfi Zade and other researchers [7–9], provide formal models based on linguistic terms by simultaneous inclusion of an element into several sets. The author of the article provided a research [10], in which he shows the extended definition area of fuzzy element weight from interval [0; 1] to[ ∞; ∞]. The extended definition area provides the opportunity to apply fuzzy logic together with scoring models. Let us consider a task of developing a new customer offer (either product, goods, or service) based on customer feedback analysis, company capabilities, technological opportunities, and other factors. The author previously published his experience in applying fuzzy logic to develop new service offers for lifelong learn-

Learning organization efficiency on the other hand depends on student's satisfaction and their willingness for continuous learning and capabilities to deliver learning services cheap and adapt them to changes in customer demand.

Based on customers'surveys, and their analysis, the author defined students' professional improvement and satisfaction as one of the critical success factors. The

decreases. See stochastic paths from V4 node in **Figure 5**.

a. Buy ticket—move to V(7) node.

b. Return to the selection page (V5 node).

improve both customer satisfaction and operator's value.

**creating a new product or a service**

ing business [10].

**8**

main satisfaction attributes are:

procedure.

*Stochastic routes at the end of customer journey.*

*Customer Relationship Management and IT*

**Figure 5.**

*Awareness level of fuzzy model attributes.*

#### **Author details**

## Danil Dintsis1,2

\*Address all correspondence to<EMAIL_ADDRESS>
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## **References**

[1] Big Data. Glossary. Gartner group. Available from: https://www.gartner. com/en/information-technology/ glossary/big-data as of January 02, 2020

[2] Ng A. Machine Learning Yearning. 2018. Available from: https://www. deeplearning.ai/machine-learningyearning/

[3] Ng A. Deep learning: From basics to practice. V.1. Seattle: Imaginary Institute; 2018

[4] Ng A. Deep learning: From basics to practice. V.2. Seattle: Imaginary Institute; 2018

[5] Ibe OC. Fundamentals of stochastic networks. O'Reilly Publishing; 2011. ISBN:<PHONE_NUMBER>989

Section 2

Analyzing Modern

CRM Practices

**11**

[6] Dintsis D, Dedegkayev A. Parallel decomposition of the stochastic model. In: Conference on Information math models, cybernetics and AI. Moscow – Ryazan. 1999

[7] Zadeh L. The concept of a linguistic variable and its application to approximate reasoning–I. Information Sciences. 1975;**8**:199-249

[8] Zadeh LA. "Fuzzy sets" archived 2015-08-13 at the Wayback machine. Information and Control. 1965;**8**(3): 338-353

[9] Dubois D, Prade H. Fuzzy Sets and Systems. New York: Academic Press; 1988

[10] Dintsis D. Implementing fuzzy sets for "big data" analysis based on large training center feedbacks. In: IEEE AEIT Annual 2015 Conference; October, 2015; Naples, Italy

Section 2


Nanomaterials such as chitosan NPs, metal NPs (AgNPs, Copper NPs and gold NPs), and metal oxide NPs (TiO2, ZnO, MgO, and CuO) and CNTs are suitable agents that are well known for their antimicrobial activity and thus show great potential in providing antimicrobial and scavenging activity to food packaging. AgNPs are known to be inhibitory against multiple fungi [62, 96]. The AgNPs have been shown to inhibit fungal growth, when they are deposited over multilayered linear low-density polyethylene (LLDPE), and this resulted in 70% reduction of *Aspergillus niger* [76]. In another study, the application of 45 ppm Ag NPs caused a decrease in mycotoxin production (up to 80%) and changes in the enzymatic profile in *Aspergillus niger* [97]. The biosynthesized AgNPs showed outstanding activities for inhibiting four mycotoxigenic fungal strains (including *Alternaria alternata*, *A. ochraceus*, *Aspergillus flavus*, and *Fusarium solani*) [98]. Chitosan/silver, chitosan/gold, and chitosan/cinnamaldehyde nanocomposite films have also demonstrated antimicrobial activity against *Aspergillus niger* [99]. TiO2 NPs, used as a food additive and for food contact material, have been applied to food packaging [92]. ZnO NPs have been an extremely promising antifungal agents for inhibiting the growth of mycotoxin-producing fungi [98]. CuNPs with the size range of 3–10 nm have also been found to have a superior antifungal activity toward *Fusarium oxysporum* [100]. NPs are especially attractive when exploiting eco-friendly energy-efficient, cost-effective, and green approaches. The use of extract of *Cymbopogan citratus* (DC) stapf, commonly known as lemon grass [100] and leaf extract of *Cinnamomum camphora* [101] in NPs synthesis, has been reported and has been found to be efficient in terms of reaction time as well as stability of the synthesized NPs. Essential oil-loaded biopolymeric nanocarriers also

*Toward Safe Food Systems: Analyses of Mycotoxin Contaminants in Food and Preventive… DOI: http://dx.doi.org/10.5772/intechopen.101461*

show promising antimicrobial and antioxidant activity and are suitable material for active food packaging due to inhibition of microbial growth in different food products [102].

#### *<IP_ADDRESS> Incorporation of moisture repellents and moisture absorbers in food packaging*

Excess water reduces food shelf-life as it can promotes fungal proliferation inducing undesired changes in food quality. Thus, the moisture absorbers that are active non-migratory packaging and anti-wetting agents can be used in food packaging to reduce food water activity and provide an environment less suitable for mycotoxincausing fungi [94]. Anti-wetting/moisture repellents can be made up of hydrophobic coatings on the surfaces of packaging materials.

Another strategy could involve the preparation of nano-engineered silicate-based hybrids coated onto both the intercalated and exfoliated silicate-based nano-composites. These materials are known to play an important role as agents that prevent the permeability of gaseous agents (e.g., O2, CO2). An attractive feature of using nanoengineered silicate-based hybrids arises from the fact that they are among minerals that are widely found in nature abundantly. Silicate minerals can have the surface easily modified due to the high possibility of ion exchange whereby a hydrophobic silicate can be modified/converted to an organophilic by exchanging a cation on its surface with an organic cation.

#### **3.2 Smart packaging/intelligent packaging systems**

In processing facilities, packaged foods are tested randomly during a production run. The downside to this is that there is no assurance that unsampled packages meet quality and safety standards. Recent efforts have thus been directed to the development of intelligent packaging systems that allow for real-time monitoring of food quality and boosting communicating with suppliers or the consumer at any point of the supply chain, or at the time of use [103]. These give ability to continually monitor the content of a package headspace and also provide a means to assess the safety and quality of the contained food long after it has left the production chain [62]. This can assist in ensuring adequate control after delivery to the supermarket, which is often not possible.

Intelligent systems use different innovative communication methods, which include sensors (already discussed under <IP_ADDRESS>), indicators, and data carriers, that can measure changes in the environmental conditions inside packaging. These systems are attractive in mycotoxin research. The inclusion of nanosensors especially in food packaging systems could help in detecting the spoilage-associated changes and mycotoxin-causing fungi and thus can be alerted consumer and producer on food contamination [104]. These selective and sensitive nanosensors have been efficiently incorporated into food packaging, applied as labels or coatings to add an intelligent function to food packaging [105].

#### **4. Conclusion**

Analytical detection methods for mycotoxin that are affordable, easy to operate, and including LFIAs and electochemistry have been discussed. LFIA especially offers point-of-care diagnosis, which could be affordable to laboratories and farmers.

#### *Food Systems Resilience*

The means of improving the LFIAs such as using electrospinning for production of membrane are recommended for increasing the acceptability LFIA. Food packaging is recognized as a means of preventing/controlling formation of mycotoxins. Aggressive research programs and yet affordable are needed to prevent, treat, and contain outbreaks of mycotoxins in grain, and grain producers and thus increase marketability of African products.

#### **Author details**

Dikabo Mogopodi1 \*, Mesha Mbisana1 , Samuel Raditloko1 , Inonge Chibua1 and Banyaladzi Paphane2

1 University of Botswana, Gaborone, Botswana

2 Botswana University of Agriculture and Natural Resources, Gaborone, Botswana

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Toward Safe Food Systems: Analyses of Mycotoxin Contaminants in Food and Preventive… DOI: http://dx.doi.org/10.5772/intechopen.101461*

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Section 2


**Lucy Mulugo 1,\*, Florence Birungi Kyazze 1, Paul Kibwika 1, Bonaventure Aman Omondi 2 and Enoch Mutebi Kikulwe 3**

Received: 16 October 2020; Accepted: 19 November 2020; Published: 7 December 2020

**Abstract:** Despite the promotion of tissue culture (TC) banana to curb the spread of diseases, farmer use of such quality planting material remains low. This study utilizes the Double-Hurdle model on cross-sectional data of 174 banana farmers in Central Uganda to analyze the drivers for uptake of TC banana plant materials. Results show acceptability (β = 0.74; *p* < 0.01), adaptability (β = 0.69; *p* < 0.01) and availability for farmer use (β = 1.04; *p* < 0.01) along with social influence, farmer competences and socioeconomic factors positively influence farmer uptake of the TC banana plantlets. For uptake intensity, the main drivers include acceptability (β = 0.39; *p* < 0.05), accessibility (β = 0.39; *p* < 0.01) and farmer competences. This study demonstrates that seed security factors with farmer competencies, social influence and socioeconomic factors influence farmer decisions on uptake of TC technology for banana production. Findings emphasize the need for more involvement of extension services and research institutions in the education and promotion of TC plants in farming communities. We recommend that banana TC developers and promoters focus attention on banana varieties that are acceptable and adaptable to farmer environmental conditions.

**Keywords:** seed security; banana tissue culture planting material; uptake; banana farmers; central Uganda

## **1. Introduction**

The per capita food output in Sub-Saharan Africa (SSA) has considerably declined, thus, contributing to increased food and income insecurity [1,2]. At the center of the debate on food and income, insecurity is the inability of the smallholder farmer to use quality plant material void of pests and diseases [3]. In the SSA region, the propagation system is characterized by both formal and informal plant material supplies. For banana (*Musa* spp.), the majority of smallholder farmers depend on the informal supply (including home-saved material from previous season harvests) [4]. For example, over 90% of banana farmers in East and Central Africa rely on suckers sourced from friends, neighbors, relatives and/or their own fields to either expand or establish new banana plantations [5,6]. The high prevalence of pests and disease in the home-saved plant material, however, has necessitated research and development of expert practitioners to increase the use of high-quality, formal supplies of plant material [7]. Such an approach, embedded in the farmers' social, cultural environment,

guarantees quality banana propagation material [8]. One such effort is the development of tissue culture (TC) planting materials (TC banana planting materials, TC banana plantlets and TC seed are used interchangeably throughout the manuscript), which are always free of pests and diseases.

The development of banana tissue culture is a response strategy for addressing the dual challenges of (1) supplying disease-free plant material and (2) enhancing farm-level yields in banana production. First, banana is a major staple for more than half of Uganda's population, and it provides a wide range of products (animal feeds, charcoal briskets, crafts, construction materials, etc.) which significantly contribute to food and income security of the populace and consequently to national development [9]. Despite the value and benefits derived from bananas, diseases such as banana *Xanthomonas* wilt (BXW) threaten its survival in the country. Between 2002 and 2005, BXW caused losses equivalent to 61.1 million US dollars to the country, mainly associated with the East African highland banana (EAHB) "Matooke" (AAA-EAHB genome) and the "Kayinja" beer banana (ABB genome) [10]. As such, farmer use of quality banana material (e.g., tissue culture plantlets) is considered a vital component for increasing banana survival and boosting agricultural productivity in the country.

Tissue culture (TC) generated banana plants are presumed to be free of BXW and recommended for the establishment of clean banana plantations [7]. However, the use of TC banana plantlets remains low [11], at less than 7% of the total banana production in the country [7]. For instance, a study by Akankwasa et al. [12] reveals that only two hundred and fifty mother gardens had been established in Uganda, and 40,000 tissue-cultured plantlets were distributed to banana farmers. Further, results indicate that merely 6% of banana farmers are willing to use TC banana planting material. Existing studies [13,14] have mainly focused on extrinsic (mainly socioeconomic) factors to explain the uptake of TC banana planting materials. However, studies that examine the role of security factors (availability, accessibility, acceptability and adaptability) in light of the farmer environment (competence, social influence and farmer's socioeconomic factors still contribute to the lack of TC plant use and necessitate further investigation.

Research is well established in root and tuber crops [15,16]. Studies on the farmer environment to explain farmer behavior in the uptake of agricultural technologies [17–19] have been conducted, but most of the existing research is qualitative. Research is needed to understand quantitative factors influencing uptake of agricultural technologies such as banana TC plantlets. Particularly, quantitative data are needed on how TC plant availability, accessibility, acceptability and adaptability combine with the farmer environment to influence the uptake of TC banana technology. The objective of this study is to explore the role of seed security factors and the farmer environment in the uptake of TC banana planting materials. Results will be important in designing interventions that ensure the sustainability of the banana crop in central Uganda.

#### **2. Conceptual Framework, Study Area and Methods**

#### *2.1. Conceptual Framework*

The uptake of agricultural technologies like TC banana planting materials is a complex, nonlinear process influenced by multiple factors. As such, the use of a single theory to analyze farmer decision-making in using tissue culture banana planting materials does not provide a holistic picture of the uptake process. Thus, we developed a conceptual framework (Figure 1) that encompasses seed security factors and the farmer environment to analyze the uptake of TC banana plantlets.

**Figure 1.** Seed security factors and farmer environment influencing uptake of TC banana plantlets (source: developed by authors).

The seed security framework with a focus on root tuber and banana (RTB) crops [20–22] explains that for sustained uptake of certified planting materials in a seed system (a seed system is the network of stakeholders involved in producing and planting seed (including vegetative planting material) of a particular crop in a certain area [15,20]), the factors:—availability, accessibility and quality are of relevance. It has been argued that the focus of seed security factors stimulates farmer confidence in the uptake of technologies and is of utmost relevance in identifying why uptake efforts fail (or succeed), thus aiding in the more effective design of future efforts [21].  These studies reiterate the relevance of seed security factors in guiding programs that encourage the uptake of newly introduced technologies.

Availability indicates a sufficient and timely supply of propagation material from existing and functional sources to farmers [20]. Accessibility pertains to farmers' ease of acquiring TC banana planting material. This refers to whether farmers have the financial capital to purchase the plantlets and the feasibility of transporting the banana planting materials from the TC sources to destined localities [20]. Provision of information pertaining to seed technologies has also been found to be key in aiding farmers to access seed [15,23,24]. Acceptability relates to the provision of preferred and desirable seed varieties that are acceptable to meet farmers' tastes and preferences [22,32]. In accordance, Mulugo et al. [33] attest to banana farmers in central Uganda having desirable banana varieties preferred based on taste, aroma, color and texture. Similarly, Akankwasa et al. [12] found that banana taste, flavor, texture and color were key in determining consumers' likelihood of purchasing hybrid banana varieties in the four regions of Uganda.

Adaptability refers to the ability of TC banana plantlets to perform well in newly introduced environments and farming conditions. Kilwinger et al. [5] cite prolonged drought effects on banana productivity in central Uganda; Nyombi [34] mentions low soil fertility in the same region. However, previous studies tend to exclude TC established plantations, whose plantlets are characteristically fragile and sensitive to harsh environmental conditions [35]. Sinja et al. [36] confirm that farmers will only take up a technology that is best adapted to their environment. Thus, it is imperative that

TC planting materials adapt to the prevailing soil and environmental conditions in the study region. For this study, adaptability measures drought tolerance capabilities of TC established.

Theories on competence development posit knowledge, attitude and skill to be important factors that determine individual capabilities [37,38]. Competences consist of integrated pieces of knowledge, skills and attitudes that can be used to perform a task successfully, such as the uptake of TC plantlets [39]. Scholars [39,40] have shown skills to be interwoven with knowledge and viewed conjointly as doing or acting in practice. Meijer et al. [19] refer to knowledge as factual information and understanding of how a new agricultural technology works and what benefits the farmers can derive from it. Ugochukwu and Phillips [41], Kuehne et al. [42] and Meijer et al. [19] indicate that farmers' knowledge about the existence of a new technology extends to how to apply it and what the outcomes are in terms of products, yield, potential benefits, risks and costs. In this regard, knowledge is operationalized in this study to include elements of skill and technical knowledge that a farmer needs to grow TC plant materials, as well as the application of local technical knowledge to control BXW. In accordance with the tripartite theory, attitudes have three components- affective, behavioral and evaluative [43,44], which are key for the uptake of agricultural technologies. The affective domain refers to the emotional response of liking/disliking an object [45]. In this context, the TC plantlets. The behavioral component is a verbal or overt tendency by an individual [46] consisting of actions or observable responses that are the result of the object. The evaluative component, on the other hand, constitutes an individual's opinion of either belief or disbelief about a technology. For this study, attitude captures the affective and evaluative domains that include farmers' passion for growing TC plants and their perception of how the use of TC plants may reflect their personality to other people in their community.

Social influence refers to the degree to which a farmer perceives that relevant people believe that he or she should use agricultural technology [17]. Studies [47,48] attest to social influence triggering individuals' behavioral intentions to use new technologies. Thus, for this study, social influence captures the elements of farmer persuasion by members of farmer groups, faith-based leaders and community elders/leaders to grow bananas using TC planting material.

Previous research [12,49,50] shows the importance of farmer socioeconomic factors or characteristics (sex, age, education, farming experience, farm size, membership to farmer groups, and accessibility to agricultural extension services) on farmer uptake of agricultural technologies. Thus, these factors are incorporated into this study to evaluate whether they are critical in the uptake of TC banana plants. It is assumed that these factors in the presence of seed security factors could differently be influencing the uptake of TC banana seed.

#### *2.2. Study Area*

The study was conducted in Luweero and Mukono districts (Figure 2) in Central Uganda, where TC banana planting materials have been promoted for more than a decade. The two districts experience a high prevalence of BXW [51] despite numerous interventions to curb the disease. In each district, villages that hosted the community-based TC nurseries—Gonve and Nambi villages were selected on the assumption that proximity to TC nurseries enhances farmer physical access to TC banana planting materials. Farmers in the two villages were linked to TC laboratories (from whence they obtained TC plantlets) through farmer-managed community-based TC nurseries.

**Figure 2.** Map showing the study areas.

### *2.3. Study Design and Sample Selection*

This study employed a quantitative research design. Data were obtained through a cross-sectional survey conducted in April and May 2018. The unit of analysis was the individual banana farmer. A questionnaire was administered to every respondent to generate data on variables of interest.

With the help of local council village chairpersons, names of all banana farmers from the selected villages that had been trained by The International Institute for Tropical Agriculture (IITA) between 2008 and 2011 (comprising adopters and potential adopters of the TC banana technology) were compiled to generate the sampling frame with a total of 340 banana farmers (Table 1). Unlike Mulugo et al. [17], who sampled users and non-users of the TC banana plantlets, here we selected adopters and potential adopters of the TC planting materials. The farmers had been trained on how to grow and manage TC banana plantlets, business and marketing and the establishment of farmer cooperatives linked to the TC community nurseries. From a predetermined sample size of 174 banana farmers, estimated using the method suggested by Krejcie and Morgan [52], respondents were proportionately drawn from each of the study villages, as shown in Table 1 below.

**Table 1.** Number of farmers selected for the study.