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XLIII Encontro da ANPAD - EnANPAD 2019
São Paulo/SP - 02 a 05 de outubro
THE IMPORTANCE OF ANTICIPATIVE STRATEGIC INTELLIGENCE AND
ENVIRONMENT SCANNING FOR PERSONALIZATION
Autoria
Maitê Klein - maiteklein@outlook.com
Prog de Pós-Grad em Admin/Esc de Admin – PPGA/EA/UFRGS - Universidade Federal do Rio Grande do Sul
Raquel Janissek-Muniz - rjmuniz@ufrgs.br
Prog de Pós-Grad em Admin/Esc de Admin – PPGA/EA/UFRGS - Universidade Federal do Rio Grande do Sul
Agradecimentos
This study was financed in part by Conselho Nacional de Desenvolvimento Científico e
Tecnológico (CNPq).
Resumo
Internet has generated large amounts of consumer data, enabling firms to collect, anticipated
and understand the consumer environment. Organizations can use a personalization strategy
that can be defined as the use of technology and customer information to delivery content in
order to satisfy their preferences. In view of this, the research objective is: to verify the
relationship between anticipative strategic intelligence and environment scanning with
personalization. For such proposal, was developed a systematic literature review. To support
the study analyses we used “Nvivo” software. This research contributes to both academic
and business knowledge. First of all, following a SLR process, it was seen as a suitable
strategy to define the objective and propositions was presented a broad review of the
literature that can serve as a model for future research related to personalization, anticipative
strategic intelligence, environment scanning and more. Furthermore, was developed
unprecedented debates linking anticipative strategic intelligence and environment scanning
clarifying the importance of those concepts for personalization. In business terms, we
contributed reporting the importance of scan and understand the environment that involves
watching significant events and trends in the whole environment and this knowledge might
support executives in identifying strategic threats and opportunities regarding to
personalization.
XLIII Encontro da ANPAD - EnANPAD 2019
São Paulo/SP - 02 a 05 de outubro
THE IMPORTANCE OF ANTICIPATIVE STRATEGIC INTELLIGENCE
AND ENVIRONMENT SCANNING FOR PERSONALIZATION
ABSTRACT
Internet has generated large amounts of consumer data, enabling firms to collect, anticipated
and understand the consumer environment. Organizations can use a personalization strategy
that can be defined as the use of technology and customer information to delivery content in
order to satisfy their preferences. In view of this, the research objective is: to verify the
relationship between anticipative strategic intelligence and environment scanning with
personalization. For such proposal, was developed a systematic literature review. To support
the study analyses we used “Nvivo” software. This research contributes to both academic and
business knowledge. First of all, following a SLR process, it was seen as a suitable strategy to
define the objective and propositions was presented a broad review of the literature that can
serve as a model for future research related to personalization, anticipative strategic
intelligence, environment scanning and more. Furthermore, was developed unprecedented
debates linking anticipative strategic intelligence and environment scanning clarifying the
importance of those concepts for personalization. In business terms, we contributed reporting
the importance of scan and understand the environment that involves watching significant
events and trends in the whole environment and this knowledge might support executives in
identifying strategic threats and opportunities regarding to personalization.
KEYWORDS: Anticipative Strategic Intelligence. Environment Scanning. Personalization.
Literature Review.
INTRODUCTION
Internet have generated large amounts of consumer data enabling firms to collect,
analyze and respond to specific information from their customers. This is not something
completely new. Peppers and Rogers (1993) anticipated that marketing focus would be on
individual customers rather than mess messaging in the future. Fallowing the predict of the
authors, nowadays the object of personalization might be understood as any part of the
marketing mix: product, promotion, placement, or price. The communication and the content
of massages also can be personalized in different ways - designing websites or e-mails, using
different pictures or videos adding personal information such as name of customers in the
messages, etc. However, personalization is a company decision based on previously collected
customer data (Arora et al. 2008; Vesanen & Raulas 2006). In addition, can be define as the
use of technology and customer information (County, 2003) based on known, observed, and
predictive information to design, management, and delivery content and business processes to
customers (Meister et al. 2002) in order to satisfy their preferences and needs (County, 2003).
In 2017, Gartner published an analysis that states in 2018 organizations that had
invested in the full spectrum of online personalization outsell companies that had not by more
than 30 percent. In additional, from customer perspective, a survey from Marketwired (2017)
argues that 71 percent of shoppers on average express some level of frustration when their
experience is impersonal. Furthermore, after receive a personalized recommendation 49
percent of shoppers made impulsive buys and 44 percent likely to repeat the purchase more
times after personalized experiences.
Personalized experiences require external information from costumers and an
environment understanding. One of the most important things in personalization is to
comprehend that user’ interests and information needs vary. Some users are interested in the
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latest stock quotes, while others are interested in weather or sports news (County, 2003). But
this is expected, when the main objective of companies is to be competitive and survive in
very difficult conditions with an environment that might be simple or complex, static or
dynamic, but always changing (Lesca & Janissek-Muniz, 2015). Moreover, scan and plan
scenarios allow firms to explore multiple external forces to create a rich set of paths on how
the future may unfold (Schwarz, Ram & Rohrbeck, 2018).
Recent studies have considered personalization as two categories that Murthi and
Sarkar’s (2003) define as personalization process and personalization and firm strategy (Frick
& Li, 2016; Pappas, Mikalef, & Giannakos, 2016 & Pöyry, et al., 2017). In the first one, the
studies are focuses on technical issues related to personalization. In the second one, is focused
on the impact of personalization on firm performance. Moreover, some studies present
personalization as a solution for information overload when companies’ delivery products and
purchase experiences to the tastes of individual consumers (Chellappa & Sin 2005; Newell &
Marabelli, 2015; Chen et al. 2017).However any of those research explore personalization as
a well define process incorporating an intelligence process since that information is crucial for
personalization and intelligence means the ability to recognize, locate, associate and link
information with the objective to synthesize and construct a representation (Lesca & Lesca,
2013). Furthermore, considering that personalization needs to collect and analyze costumer
data, an environment scanning strategy might help the companies to collect and use of
information about events, trends, and relationships in the company external environment to
plan the organization’s future path of action (Aguilar, 1967; Choo & Auster, 1993).
In view of this, the research objective is: to verify the relationship between anticipative
strategic intelligence and environment scanning with personalization. For such proposal, was
developed a systematic literature review of personalization concepts, started by searching for
studies that have the term: “Personalization” on AIS Electronic Library (AISel). Were
included 27 researches present in both journals and conferences. To support the study
analyses was count with the aid of Nvivo software.
The present study contributes to both theoretical and managerial knowledge. First of
all, analyze and complement the concept of personalization based on IS literature. Second,
improves in recognizing different debates related to personalization, anticipative strategic
intelligence and environment scanning, responding the propositions of this study and showing
the importance of information and environment to personalization strategy. Besides that,
direct efforts to present avenues for future research in the theoretical field and highlight the
importance of those concepts for companies that might use it to plan the future, increase sales,
communicate with customers and create better products and services, for example.
The next section, present the method used to conduct the systematic literature review,
followed by the theoretical development identifying the main debates linking the anticipative
strategic intelligence and environment scanning concepts with personalization. Moreover, the
theoretical and managerial conclusions, limitations and some directions for future studies.
1 METHOD
To conduct a Systematic Literature Review- SLR (Webster & Watson, 2002) was
followed Kitchenham et al. (2010) procedures presented in the Fig.1. The process consists of
three phases: planning, conducting and reporting. The first one explores the definition of
objectives, whole the search process and specifies the inclusion and exclusion criteria. The
second phase illustrates the execution of the search process. In the last one, present some
quantitative and qualitative results regarding to the peppers selected.
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Figure 1: Summary of SLR Phases
Source: created by authors according to Kitchenham et al. (2010).
1.1 PLANNING THE REVIEW
In this phase, the research objective and questions are defined; moreover the
propositions of this study presented in Table 1 and the conceptual framework (Figure 2). As
mentioned before, the objective is to verify the relationship between anticipative strategic
intelligence and environmental scanning with personalization. Furthermore, present as result
some debates regarding to these concepts expounding the importance of anticipative strategic
intelligence and environmental scanning for personalization.
Table 1: Propositions
Proposition:
Reference:
P1: Information is a key source related to the
personalization.
Arora et al. 2008; Vesanen & Raulas 2006;
County, 2003.
P2: Anticipative strategic intelligence is related
to personalization.
Blanco & Lesca, 1997; Lesca, 2003; Freitas,
Freitas, Janissek-muniz & Brodbeck, 2008.
P3: Environment scanning is related to
personalization.
Choo, 2001; Lesca, 2003; Choudhury, Vivek
& Sampler, 1997.
P4: Anticipative strategic intelligence, related
to personalization, enable to reduce information
overload.
Alba & Hutchinson, 1987; Haubl & Trifts
2000; County, 2003; Lesca, Freitas, Janissek-
Muniz, 2003.
P5: Environment scanning, related to
personalization, enable to reduce information
overload.
Alba & Hutchinson 1987; Haubl & Trifts
2000; Choo, 2001; County, 2003.
Source: created by authors.
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Figure 2: Conceptual Framework
Source: created by authors following the propositions.
Regarding to this objective, the second process of planning phase is to define the
search process and the inclusion and exclusion criteria. For such proposal, was selected
publications from journals and conferences in the AIS Electronic Library (AISel) considering
to identify studies about personalization in the IS literature. Was used the keyword
“personalization” that needed to appear in one of these parts at list: tittle, abstract and
keywords. As “personalization” is a noun that might be used in different situations, was
founded a lot of studies that aren’t related to our major objective.
In order to select the correct studies, was determined some inclusion and exclusion
criteria that might be seen in Fig.2. Is important to clarify that this study just considered
papers published in English and with no publishing year limit.
1.2 CONDUCTING THE REVIEW
This second phase of the SLR follow the planning phase and present the studies select
based on the inclusion and exclusion criteria illustrated by Figure 3. In the first search, 138
papers are found. To ensure the study quality assessment, was followed one more important
step in order to be sure that it satisfies the objective of the research. This step includes reading
and scanning parts from the articles as figures, tables, appendixes and others.
Furthermore, to support the research was extracted analysis from the “Nvivo”
software. The results collected with this tool will present in the reporting the review phase.
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Figure 3: Summary of Conducting Phase
Source: created by authors.
1.3 REPORTING THE REVIEW
The last one SLR phase presents the quantitative and qualitative results and analysis of
the 27 papers selected. Those findings are based on the others two phases presented and the
objective pre-established. Some important information about the studies selected might be
found in Appendix 1.
Figure 4 shows the number of publications with the personalization topic in journals
and conferences. The journal “MIS Quarterly” is in the top of list with six papers followed by
two important conferences: “AMCIS” and “ICIS” also with six studies.
Figure 4: Number of Publications
Source: created by authors.
Comparing the publications per year, the topic began to been seen in 2003, mainly in
journals. Between 2008 and 2013 there was a balance between journals and conferences
issues. Moreover, after 2013 total number of journals publications declined progressively.
More results might be seen in Figure 5.
1
1
2
2
3
6
6
6
01234567
Journal of the Association for Information…
Journal of Information Technology Theory…
Hawaii International Conference on System…
Communications of the Association for…
Pacific Asia Conference on Information…
International Conference on Information…
Americas Conference on Information Systems
MIS Quarterly
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Figure 5: Number of Publications per Year
Source: created by authors.
Regarding to authors, Figure 6 present an analysis extracted from the “Nvivo”
software just of the authors who have more than one publication related to the topic. It’s
important to notice that the number of publications is low and seven authors have more than
one paper published.
Figure 6: Number of Publications per Author
Source: created by authors at Nvivo software.
Finally, the cloud words (Figure 7) shows the most cited words present on the titles
and abstracts of all papers selected. Notice that “personalization” is the word mostly frequent,
as expected considering the selection step in second phase of the SLR. In addition, the words
“information”, “online”, “privacy” and “trust” are the most recurrent. This might be explained
by the fact that the papers relate personalization topic to these factor and concepts. Moreover,
highlight the importance of information for personalization as a strategy.
0 0 0 0
2
1
2
3
1
5
3
1 1
3
1
2
1
0
1
0 0 0
0
1
2
3
4
5
6
2003 2005 2006 2008 2011 2012 2013 2014 2015 2016 2017
Conference
Journals
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Figure 7: Words Cloud
Source: created by authors at Nvivo software.
Regarding to the idea of personalization as a strategy, was selected just the
personalization concepts presented in each paper selected and, with the aim of Nvivo,
generated a word tree (Figure 8) linking the most frequently words related to the word
strategy. The results highlight that for some authors personalization might be understood as an
“effective business strategy”, “important marketing strategy” and might support the “firm
strategy”. In addition, was generated a word tree (Figure 9) that extend the most frequently
words linked with the “environment” in whole papers. Notice that, digital and online
environments and their variations appear, hence other important characteristics as “changing”
and “open”.
Figure 8: Strategy Word Tree
Source: created by authors based on Nvivo software.
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Figure 9: Environment Word Tree
Source: created by authors based on Nvivo software
In complement to reporting the review phase, the next section presents three debates.
Including the importance of anticipative strategic intelligence and environment scanning for
personalization.
2 DEBATES
Based on literature review and the qualitative and quantitative results reporting before,
was able to structure some insights that will present as three debates. Is extant the meaning
and the benefits of personalization as a strategy presented in the IS literature, the relationship
between anticipative strategic intelligence and environment scanning with personalization,
responding the propositions of this study.
2.1 INFORMATION: THE PRIMARY RESOURCE
Information is an essential element of almost every activity in the company. If the
company not grasp of how it creates, transforms and uses information, the company might
lack the coherent vision to manage and integrate its information processes, resources and
information technologies (Choo, 1996). In complement, Lesca and Lesca (1995) argue that
information from outside the company enable anticipated some possible alterations within the
socio-economic environment. This information might be defined as: by nature, ongoing,
uncertain, rarely repetitive, ambiguous, fragmented and opposing.
Information can be used for companies strategically in three areas: to make sense of
transformation in its environment; to generate new knowledge for innovation; and to make
decisions about paths of action. Through sensemaking, people in a company give sense to the
events and actions of the organization. Through knowledge creation, the insights of
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individuals are transformed into knowledge that might be used to design new products,
services or increase performance. Moreover, in decision making, understanding and
knowledge are focused on the selection of and commitment to an appropriate course of action
(Choo, 1996).
For personalization, is important to know that users’ interests and information needs
vary constantly. Some users are interested in the latest stock quotes, while others are
interested in weather or sports news (County, 2003). Besides that, costumer preferences are
always changing based on relevant experiences and gathering additional information. They
(re)construct their preferences and in the end set established their profile after are investing
effort, getting experience with a product or service and making purchase choices (Hoeffler &
Ariely 1999).
A costumer profile is complexity resource composed of a record of user-specific data,
not just personal information as interest and preferences, but may include some different
information define as: user information, target device information, service profile and wireless
network (County, 2003). The author also defines as user information: “the user ID,
background information, personal interest represented by either keywords or
information/service categories, some preferences (media preference, summarization method,
and priorities among data items)”. The target device information represents the information
display limitations in mobile devices, including screen size and resolution (how much lines of
the text the costumer can see) or even a time of disconnection (network time-out/failure, low
battery and more), for example (County, 2003). In additional, the author complements the
definition of service profile: “service restrictions and user availability” and the meaning of
wireless network information: “network ID, topology, and configuration”.
Furthermore some earlier researches in the IS literature present some different types of
user data that arrange the costumer profile: user data (Brusilovsky, 1996; Daniels, 1986),
usage data (Rich, 1979; Moe & Fader, 2004; Montgomery, et al. 2004; Padmanabhan, Zheng,
& Kimbrough, 2006), context data (Kobsa et al. 2001) and social data (Abel et al. 2011; Park
et al. 2012). User data describes the costumer characteristics and include data provided by the
user (during a registration) or inferred by the company (preferences, goals, beliefs,
experience, etc.), for example (Brusilovsky, 1996; Daniels, 1986). Usage data could be not
different of costumers’ behavior and is an important resource in the personalization process
once, in some cases, customers might be not able to provide accurate information of your
personality because don’t realize some self-behaviors (Rich, 1979; Moe & Fader, 2004;
Montgomery, et al. 2004; Padmanabhan, Zheng, & Kimbrough, 2006).
In addition, context data provides a lot of opportunities for personalization since
represent the data of customers environment (type of user’s device, usage context or
geographic location) and companies might personalized their content to attempt customers
(Kobsa et al., 2001). Regarding to social relationships, social data might be define as data
collect through user’s social contact. With social media environments, customers can interact
with friends or people who have the same interest and generate own content (blogs, for
example), and this represents one of the reasons why social data become very important
source of information for personalization (Abel et al., 2011; Park et al., 2012).
Regarding to meanings and terms explained of personalization, might be inferred that
information is a key resource in whole personalization strategy supporting the first
proposition of this study (P1: Information is a key source related to the personalization). To
succeed with this strategy, the company needs to attend two objectives. The first one is
control the content, format and timing of messages to achieve a favorable customer response.
The second one and one of the most important objectives is to increase the probability of
customers accepting the companies’ offerings in the future by placing marketing messages in
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the customers mind. Those two objectives help to influence customers future purchase
intentions (Utcomes & Tam, 2006).
2.2 PERSONALIZATION AS A PROCESS: LINKING ANTICIPATIVE STRATEGIC
INTELLIGENCE AND ENVIRONMENT SCANNING PROCESS
Before start to explain the concepts, is important to define the concept of
“intelligence” and their meaning for this study. Lesca and Lesca (2011), define “intelligence”
as the ability to recognize, locate, compare and connect information with the objective to
make sense and create a representation. Obtain information is a crucial step for the
intelligence process, for that reason the environment scanning is called as anticipative
strategic intelligence as well (Lesca, Freitas, & Janissek-Muniz, 2003; Lesca & Lesca, 2011).
For “anticipative” or “anticipation” meaning might be understood as the action of looking
forward. Moreover, as individual agents, or particular facts and events that can represent a
risk for companies and their agents (Lesca & Lesca, 2011). In simple words, anticipation
represent the felling that we know that something will happen and we have to be on alert and
in a position to pick up the forewarning signals of a threat or opportunity in order to act
quickly in the right time, creating conditions for this (Lesca & Lesca, 2011).
Furthermore, environmental scanning represents an information process through
which companies monitoring their environment with the objective to create opportunities and
reduce uncertainty (Lesca, 1996; Blanco & Lesca, 1997). In addition, Choo (2011) argue that
companies scan the environment in order to comprehend external forces of change to develop
rapid and effective responses which improve and their market position and competitive
advanced in the future. Moreover, considered exploration of how the future competitive
landscape may evolve is critical to discovery threats and opportunities for organizations that
seek to improve their core businesses and advance to a superior position in the markets of the
future (Schwarz, Ram & Rohrbeck, 2018).
As presented before, information represents an important resource for personalization
that as well as intelligence process need to collect, create a sense and use the information to
anticipates the next steps. To obtain and analyze those information, companies needs the help
of algorithms (Adomavicius & Tuzhilin 2005; Ricci et al. 2011). For that reason,
personalization count on different algorithms approaches and a crucial tool for deliver the
information, products and services for customers: the Recommendation Agents (RAs), a type
of personalization agent enabled by IT (Utcomes & Tam, 2006). Recommendation agents
(RAs) are one of the most popular personalization agent that delivery to online customers,
products and services recommendations based on their preferences with the intention to make
this customers buy (Maes et al. 1999). Recommendation agents are a web-based technology
(Murthi & Sarkar, 2003) that helps companies to increase revenue (Shaffer and Zhang 2000),
reduce costs of gathering consumers’ data (Dewan et al. 2000), simplify product and service
personalization (Dewan et al. 2000) and reduce the impacts of information overload and help
the customer to make decisions (Häubl & Trifts 2000).
The environment is sometimes turbulent. Evolution and changes result from events
and interdependencies that have as consequence human behaviors, decisions, and actions
(Lesca, Caron-Fasan & Falcy, 2012). For personalization this is not completely different, Ho
et al. (2011) present two different ways of considering the costumer data collect through
algorithms: static personalization and adaptive personalization. In the first one, companies
assume that customer’s preferences are unchanged. Therefore, with this approach online
retailers, for example, captured just the basic information when the costumer join the services,
registering to the system and provide their personal information (Ho et al. 2011). In the other
hand, adaptive personalization is a real-time and dynamically system that catches the up-to-
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date preferences and information of customers and matching with their preferences based on
usage and social data over the time (Ho et al., 2011).
That’s mean that personalization look for costumer information trying to anticipate
some actions and be prepare for the future, responding to the costumers preferences and
needs, supporting two of the study propositions (P2: Anticipative strategic intelligence is
related to personalization. P3: Environment Scanning is related to personalization). Moreover,
with the rapid development of technologies and systems as cloud computing, social media,
mobile payment, IT-enabled personalization have been considered one of the most important
paths for companies to improve their revenues and customer responsiveness through their
preferences to influence purchase choices (Hawkings, 2012).
2.3 INFORMATION OVERLOAD
The most resents advances and changes with “big data” analytics promise a lot of
unprecedented opportunities for personalization (Bragge, Sunikka, & Kallio, 2012). First of
all because personalization is understood as a solution for information overload when
companies’ delivery products and purchase experiences to the tastes of individual consumers
based on their personal and preference information (Chellappa & Sin 2005). Furthermore,
personalization is a strongly tool to increase information search process because it helps
customers in making decisions and prevents the too much information effect (Pöyry et al.
2017). To this end, the companies’ needs to collect and comprehend the costumer
information, as mentioned before. Organizations need to scan the environment in order to
recognize the external forces of change so that they may develop real responses which secure
or improve their situation in the future and respond fast to costumer’s information changes
(Choo, 2001). Environmental scanning includes both looking at information (viewing) and
looking for information (searching), that might be important steps for personalization.
When customers don’t choose online personalized advertising they might be stuck in
information overload with irrelevant information and additional time cost on accessing
information of promotional items and making decisions on online shopping environments. As
an example, some of personalized online advertising service platforms such as
“Amazon.com” and “Facebook” provide users with associated information or products,
specific consuming incentives, consumers’ personal interest and appreciate interactions.
(Chen et al. 2017).
Some authors argue that with Recommendation Agents (RA) adoption, an important
web tool, companies might reduce information overload in m-commerce and e-commerce
environments (Chopra & Wallace, 2003; Liao et al. 2004) and information overload in e-
commerce, RAs help customers to make decisions easily and fast (Chopra & Wallace, 2003)
and in m-commerce reduce the complexities of users browse and access the information they
need (Zhang, 2003). In addition, Freitas, Janissek-muniz & Brodbeck (2008) complement that
use tools available on the web might support the companies to produce the discussions and
come up with ideas that will enable the generation of meaning from the data, in this case:
costumer data, in order to feed the decision-making process. Those tools enable to anticipate,
based on client’s demands and expectations. Furthermore, those web tools might aid in
detecting “weak signals” and creating anticipative strategic intelligence.
The IS literature present another type of overload: choice overload that represent the
most important reason why costumers tend to make suboptimal purchase decisions (Alba &
Hutchinson 1987; Haubl & Trifts 2000). Nevertheless, without information customers don’t
have any choice and this prove that information is the major object of personalization.
However, personalization on online environments offer to the customers just the things he or
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she like and this situation might imped them to decide what they buy, choose and even what
they think (Newell & Marabelli, 2015).
Have too much information can be profitable with personalization. Companies who
understand the challenges of information overload, as what information for users might be
blocked or removed from the content guided to them (County, 2003), in the same time see
important opportunities for the evolution of innovative ways in offering product or service
information personalized to a costumers based on it individual needs (Pierrakos et al. 2003;
Wei, Ma, & Chang, 2014).
Last but not least, the concepts presented in this section support the importance and
the presence of anticipative strategic intelligence and environment scanning in personalization
and support the proposition four and five, present before (P4: Anticipative strategic
intelligence, related to personalization, enable to reduce information overload. P5:
Environment scanning, related to personalization, enable to reduce information overload). The
next section presents the conclusions of this research, limitations and directions for further
studies.
3 CONCLUSIONS
This research contributes to both academic and business knowledge. First of all,
following a SLR process, it was seen as a suitable strategy to define the objective and
propositions was presented a broad review of the literature that can serve as a model for future
research related to personalization, anticipative strategic intelligence, environment scanning
topic and more.
Following the research objective that is to verify the relationship between anticipative
strategic intelligence and environment scanning with personalization, as part of the study
results, was developed unprecedented debates linking anticipative strategic intelligence and
environment scanning presented in earlier papers clarifying the importance of those concepts
for personalization.
In addition, regarding to business terms, we contributed reporting the benefits of the
personalization strategy to companies and the importance of scan and understand the
environment that involves watching significant events and trends in the whole environment
and this knowledge might support executives in identifying strategic threats and opportunities,
and in making strategic decisions (Walters, Jiang & Klein, 2013). For personalization, enable
comprehend the customer and direct messages, products and services to achieve them or keep
their brand loyalty. Moreover, the advance of new IT systems and tool contributes to
emphasize the importance of scanning practices and anticipative strategic intelligence (Lesca,
Caron-Fasan, & Falcy, 2012) and the companies do not grasp this, might be lagging
behind the competitors.
Considering the SLR objective that was to analyze personalization in the IS literature,
it may be considered a limitation of the study that was only analyze papers in this scope.
Future studies might elaborate a systematic literature review considering researches of other
business areas.
Moreover, some studies (Chen et al. 2017; Pierrakos et al. 2003; Wei, Ma, & Chang,
2014; County, 2003; Zhang, 2003) present personalization as a strongly tool to solve the
information overload problem because it helps customers in making decisions and prevents
the too much information effect (Pöyry et al. 2017). Those studies only shows the benefits
and that have too much information can be profitable with personalization. This allows an
inquiry if there are no dark sides in providing consumers only information that interests them,
depriving them of seeing something different and opens possibilities for future studies to
investigate the dark side of personalization, starting with information overload.
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APPENDIX
Appendix 1 – Personalization papers selected:
https://docs.google.com/document/d/1aWjKc38IUuAsSoQhkEzEpE0PgVuoI3ZUKMd3ACd
Rack/edit?usp=sharing
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